ncibtep@nih.gov

Bioinformatics Training and Education Program

Listing of past BTEP classes

BTEP routinely runs Classes (single event) and Courses (series of related offerings) covering a wide variety of Bioinformatics topics.

Upcoming Offerings are listed below

Link to calendar (past) filtered by the Organizer – BTEP

class_id details description start_date Venues learning_levels Topic Tags delivery_method presenters Organizer seminar_series class_title
214
https://hpc.nih.gov/training/handouts/200220_python_in_hpc.pdf https://xkcd.com/353/ 1970-01-01 01:00:00 In-Person HPC Biowulf 0 Python in HPC
169
Description
We present the AMARETTO-Hub as a Knowledge Graph-based software platform that leverages Neo4j and Shiny to embed and interactively interrogate results generated by the *AMARETTO software toolbox that offers modular and complementary solutions to multimodal and multiscale network-based fusion of multi-omics, clinical, imaging, and perturbation data across studies of patients, etiologies and model systems of cancer and COVID-19, towards better diagnostic, prognostic and therapeutic decision-making in complex disease. For several Use Cases of cancer ...Read More
We present the AMARETTO-Hub as a Knowledge Graph-based software platform that leverages Neo4j and Shiny to embed and interactively interrogate results generated by the *AMARETTO software toolbox that offers modular and complementary solutions to multimodal and multiscale network-based fusion of multi-omics, clinical, imaging, and perturbation data across studies of patients, etiologies and model systems of cancer and COVID-19, towards better diagnostic, prognostic and therapeutic decision-making in complex disease. For several Use Cases of cancer and COVID-19, we provide the biomedical research community with R Jupyter Notebook workflows that run the Bioconductor and GitHub repositories on Google Colaboratory, and GenePattern Notebooks that run the GenePattern modules in the Amazon Cloud, and that generate HTML reports comprising queryable tables with heatmap and graph visualizations in an automated manner, and additionally provide users with Neo4j-embedded Shiny interactive representation and querying tools that redirect users to *AMARETTO-generated HTML reports. Specifically, our software toolbox comprises of the following algorithms: (1) The AMARETTO algorithm learns networks of regulatory circuits - circuits of drivers and their target genes - from functional genomics or multi-omics data and associates these circuits to clinical, molecular and imaging-derived phenotypes within each biological system (e.g., model systems or patients). (2) The Community-AMARETTO algorithm learns subnetworks of regulatory circuits that are shared or distinct across networks derived from multiple biological systems (e.g., model systems and patients, cohorts and individuals, diseases and etiologies, in vitro and in vivo systems). (3) The Imaging-AMARETTO algorithm maps radiography and histopathology imaging data onto the patient-derived multi-omics networks for imaging diagnostics and prognostics to identify clinically relevant imaging biomarkers and decipher their underlying molecular mechanisms. (4) The Perturbation-AMARETTO algorithm maps genetic and chemical perturbations in model systems onto patient-derived networks for driver and drug discovery, respectively, and prioritizes lead drivers, targets and drugs for follow-up with experimental validation, towards better therapeutics. (5) The AMARETTO-Hub platform for Knowledge Graph-based embedding of knowledge learned via multimodal and multiscale network-based data fusion in previous steps. In these complex graphs, nodes and edges represent the diverse range of biomedical entities and the relationships between them, respectively. Graph-based embedding enables querying these complex graph-structured representations in a more sophisticated, efficient and user-friendly manner than can otherwise be accomplished by table representations alone. Resources are available from : github.com/broadinstitute/BioC2020Workshop_AMARETTO-Huband portals.broadinstitute.org/pochetlab/amaretto.html(to be updated). The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancergov/
Details
Organizer
CBIIT
When
Fri, Jun 16, 2000 - 1:30 pm - 2:30 pm
Where
Online
We present the AMARETTO-Hub as a Knowledge Graph-based software platform that leverages Neo4j and Shiny to embed and interactively interrogate results generated by the *AMARETTO software toolbox that offers modular and complementary solutions to multimodal and multiscale network-based fusion of multi-omics, clinical, imaging, and perturbation data across studies of patients, etiologies and model systems of cancer and COVID-19, towards better diagnostic, prognostic and therapeutic decision-making in complex disease. For several Use Cases of cancer and COVID-19, we provide the biomedical research community with R Jupyter Notebook workflows that run the Bioconductor and GitHub repositories on Google Colaboratory, and GenePattern Notebooks that run the GenePattern modules in the Amazon Cloud, and that generate HTML reports comprising queryable tables with heatmap and graph visualizations in an automated manner, and additionally provide users with Neo4j-embedded Shiny interactive representation and querying tools that redirect users to *AMARETTO-generated HTML reports. Specifically, our software toolbox comprises of the following algorithms: (1) The AMARETTO algorithm learns networks of regulatory circuits - circuits of drivers and their target genes - from functional genomics or multi-omics data and associates these circuits to clinical, molecular and imaging-derived phenotypes within each biological system (e.g., model systems or patients). (2) The Community-AMARETTO algorithm learns subnetworks of regulatory circuits that are shared or distinct across networks derived from multiple biological systems (e.g., model systems and patients, cohorts and individuals, diseases and etiologies, in vitro and in vivo systems). (3) The Imaging-AMARETTO algorithm maps radiography and histopathology imaging data onto the patient-derived multi-omics networks for imaging diagnostics and prognostics to identify clinically relevant imaging biomarkers and decipher their underlying molecular mechanisms. (4) The Perturbation-AMARETTO algorithm maps genetic and chemical perturbations in model systems onto patient-derived networks for driver and drug discovery, respectively, and prioritizes lead drivers, targets and drugs for follow-up with experimental validation, towards better therapeutics. (5) The AMARETTO-Hub platform for Knowledge Graph-based embedding of knowledge learned via multimodal and multiscale network-based data fusion in previous steps. In these complex graphs, nodes and edges represent the diverse range of biomedical entities and the relationships between them, respectively. Graph-based embedding enables querying these complex graph-structured representations in a more sophisticated, efficient and user-friendly manner than can otherwise be accomplished by table representations alone. Resources are available from : github.com/broadinstitute/BioC2020Workshop_AMARETTO-Huband portals.broadinstitute.org/pochetlab/amaretto.html(to be updated). The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancergov/ 2000-06-16 13:30:00 Online CBIIT 0 AMARETTO-Hub: a Knowledge Graph-based software platform that leverages the *AMARETTO software toolbox for multimodal and multisc
824
Description

As the scientific world has moved from the pre-genomic to the post-genomic era the need for tools that enable the visualization, integration and interrogation of genomic-scale data has never been greater. This talk will provide an overview of the world of Genome Browsers and demonstrate how you can use these powerful tools to visualize you own and other published data and to thus gain greater insight into the underlying biological processes. HIghlighted ...Read More

As the scientific world has moved from the pre-genomic to the post-genomic era the need for tools that enable the visualization, integration and interrogation of genomic-scale data has never been greater. This talk will provide an overview of the world of Genome Browsers and demonstrate how you can use these powerful tools to visualize you own and other published data and to thus gain greater insight into the underlying biological processes. HIghlighted topics will include:
 

  • How to navigate the UCSC Genome Browser
  • How to integrate your own data into the Browser
  • How to get more detailed views of your data with tools like IGB and IGV
  • And more.... 

A list of the Web Sites referenced in this talk can be found HERE

Register
Organizer
BTEP
When
Tue, Sep 25, 2012 - 2:00 pm - 3:30 pm
Where
Building 37, Room 4041/4107
As the scientific world has moved from the pre-genomic to the post-genomic era the need for tools that enable the visualization, integration and interrogation of genomic-scale data has never been greater. This talk will provide an overview of the world of Genome Browsers and demonstrate how you can use these powerful tools to visualize you own and other published data and to thus gain greater insight into the underlying biological processes. HIghlighted topics will include:   How to navigate the UCSC Genome Browser How to integrate your own data into the Browser How to get more detailed views of your data with tools like IGB and IGV And more....  A list of the Web Sites referenced in this talk can be found HERE 2012-09-25 14:00:00 Building 37, Room 4041/4107 In-Person Peter FitzGerald (GAU) BTEP 0 Genome Browsers
828
Description
  1. Microarray Technology and Preprocessing
    • Quality Control
    • Normalization Using MAS5 and RMA
    • Filtering
    • Batch Effect Correction
  2. Basic Statistical Tests for Differentially Expressed Genes
    • T-test
    • ANOVA
    • SAM
    • Calculating False Discovery Rate
    • Principal Components Analysis and Clustering
  3. Functional and Network Analysis
    1. Pathway Analysis
      • Ingenuity Pathway Analysis (...Read More
  1. Microarray Technology and Preprocessing
    • Quality Control
    • Normalization Using MAS5 and RMA
    • Filtering
    • Batch Effect Correction
  2. Basic Statistical Tests for Differentially Expressed Genes
    • T-test
    • ANOVA
    • SAM
    • Calculating False Discovery Rate
    • Principal Components Analysis and Clustering
  3. Functional and Network Analysis
    1. Pathway Analysis
      • Ingenuity Pathway Analysis (IPA)
      • Gene Set Enrichment Analysis (GSEA)
      • Fishers Exact Test
    2. Motif Enrichment Analysis
      • IPA motif enrichment analysis for transcription factor and miRNA motifs
      • PSCAN for transcription factor motifs
      • Fishers Exact Test for transcription factor and miRNA motifs
    3. Network Reconstruction
      • ARACNE
      • Multivariate Regression
         

Course Materials: Lecture Slides in PDF Format

Register
Organizer
BTEP
When
Tue, Oct 02, 2012 - 2:00 pm - 3:30 pm
Where
Building 37 Room 4041/4107
Microarray Technology and Preprocessing Quality Control Normalization Using MAS5 and RMA Filtering Batch Effect Correction Basic Statistical Tests for Differentially Expressed Genes T-test ANOVA SAM Calculating False Discovery Rate Principal Components Analysis and Clustering Functional and Network Analysis Pathway Analysis Ingenuity Pathway Analysis (IPA) Gene Set Enrichment Analysis (GSEA) Fishers Exact Test Motif Enrichment Analysis IPA motif enrichment analysis for transcription factor and miRNA motifs PSCAN for transcription factor motifs Fishers Exact Test for transcription factor and miRNA motifs Network Reconstruction ARACNE Multivariate Regression   Course Materials: Lecture Slides in PDF Format 2012-10-02 14:00:00 Building 37 Room 4041/4107 In-Person BTEP 0 Introduction to Gene Expression Microarray Data Analysis
827
Description

Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Partek Pathway. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways.

  • Gene Expression Analysis with Affymetrix (cancer dataset)
  • Good experimental design practices
  • Import CEL files/normalization optionsRead More

Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Partek Pathway. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways.

  • Gene Expression Analysis with Affymetrix (cancer dataset)
  • Good experimental design practices
  • Import CEL files/normalization options
  • Describing sample groups
  • Batch correction
  • Detecting differentially expressed genes/creating a gene list
  • Hierarchical Clustering and other visualizations
  • GO Analysis
  • Pathway Enrichment
  • GeneSet analysis (including Pathway ANOVA)
  • Integration with microRNA 

Course Materials:

 

Register
Organizer
BTEP
When
Tue, Oct 09, 2012 - 2:00 pm - 5:00 pm
Where
In-Person
Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Partek Pathway. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways. Gene Expression Analysis with Affymetrix (cancer dataset) Good experimental design practices Import CEL files/normalization options Describing sample groups Batch correction Detecting differentially expressed genes/creating a gene list Hierarchical Clustering and other visualizations GO Analysis Pathway Enrichment GeneSet analysis (including Pathway ANOVA) Integration with microRNA  Course Materials: Partek Shoe Example Analysis of GSE20437 GSE20437 Publication   2012-10-09 14:00:00 In-Person BTEP 0 Hands-on - Gene Expression using Microarrays with Partek Genomics Suite and Partek Pathway
826
Description

Fundamentals of DNA copy number analysis using Nexus
Learn the basics of copy number analysis and its application to genomic research. Fundamental concepts such as copy number measurement methods, quality assessment, and different approaches/algorithms used for detecting copy number changes and allelic events as well as unique complications encountered in cancer data will be presented. You will also learn how to apply this knowledge to common research objectives such as identification of ...Read More

Fundamentals of DNA copy number analysis using Nexus
Learn the basics of copy number analysis and its application to genomic research. Fundamental concepts such as copy number measurement methods, quality assessment, and different approaches/algorithms used for detecting copy number changes and allelic events as well as unique complications encountered in cancer data will be presented. You will also learn how to apply this knowledge to common research objectives such as identification of significant aberrations, comparisons between sub-populations, and identification of biomarkers.

  1. Introduction and course overview
  2. Fundamentals of DNA copy number analysis
    1. Review of copy number measurement methods
      • BAC arrays
      • Oligo two color arrays
      • SNP Arrays (with and without CNV probes)
      • MIP Array data
      • Next-Gen data
    2. Review of unique complications in cancer data
      • Aneuploidy
      • Sample heterogeneity due to surrounding tissue contamination as well as colonal diversity
      • DNA fragmentation in FFPE samples
    3. Data preprocessing and quality assessment
    4. Approaches for detecting copy number and allelic event changes
      • Differences between copy number and allelic event data
      • HMM methods, CBS, ADM, ASCAT
  3. Research objectives attained with the data
    1. Identification of recurrent events in a population
    2. Identification of statistically significant aberrations (STAC/GISTIC)
    3. Comparisons between groups
    4. Grouping samples based on copy number profiles
    5. Identification of biomarkers that are predictive of events such as survival
       

 

Register
Organizer
BTEP
When
Tue, Oct 16, 2012 - 2:15 pm - 3:30 pm
Where
In-Person
Fundamentals of DNA copy number analysis using Nexus Learn the basics of copy number analysis and its application to genomic research. Fundamental concepts such as copy number measurement methods, quality assessment, and different approaches/algorithms used for detecting copy number changes and allelic events as well as unique complications encountered in cancer data will be presented. You will also learn how to apply this knowledge to common research objectives such as identification of significant aberrations, comparisons between sub-populations, and identification of biomarkers. Introduction and course overview Fundamentals of DNA copy number analysis Review of copy number measurement methods BAC arrays Oligo two color arrays SNP Arrays (with and without CNV probes) MIP Array data Next-Gen data Review of unique complications in cancer data Aneuploidy Sample heterogeneity due to surrounding tissue contamination as well as colonal diversity DNA fragmentation in FFPE samples Data preprocessing and quality assessment Approaches for detecting copy number and allelic event changes Differences between copy number and allelic event data HMM methods, CBS, ADM, ASCAT Research objectives attained with the data Identification of recurrent events in a population Identification of statistically significant aberrations (STAC/GISTIC) Comparisons between groups Grouping samples based on copy number profiles Identification of biomarkers that are predictive of events such as survival     2012-10-16 14:15:00 In-Person Soheil Shams PhD (CEO and CSO BioDiscovery) BTEP 0 Introduction to Copy Number Analysis and its Application to Genomic Research
825
Description

Learn the fundamentals in genomic data analysis of CGH and SNP arrays using BioDiscovery Nexus Copy Number software.  In this hands-on training session, you will learn how to load, process, visualize, and analyze array data. You will learn how to effectively use the features within the software to efficiently and quickly explore the data to gain biological insights from copy number and allelic event changes in the genome.

  • Data Loading and ...Read More

Learn the fundamentals in genomic data analysis of CGH and SNP arrays using BioDiscovery Nexus Copy Number software.  In this hands-on training session, you will learn how to load, process, visualize, and analyze array data. You will learn how to effectively use the features within the software to efficiently and quickly explore the data to gain biological insights from copy number and allelic event changes in the genome.

  • Data Loading and processing
  • Visualization of results and exporting
  • Population and Sub-population analysis
  • Gene Enrichment Analysis
  • Clustering samples
  • Comparing differences between populations
  • Predictive power analysis
  • Survival predictive power and K-M plot/statistics
  • Nexus DB
Register
Organizer
BTEP
When
Tue, Oct 23, 2012 - 2:00 pm - 5:00 pm
Where
In-Person
Learn the fundamentals in genomic data analysis of CGH and SNP arrays using BioDiscovery Nexus Copy Number software.  In this hands-on training session, you will learn how to load, process, visualize, and analyze array data. You will learn how to effectively use the features within the software to efficiently and quickly explore the data to gain biological insights from copy number and allelic event changes in the genome. Data Loading and processing Visualization of results and exporting Population and Sub-population analysis Gene Enrichment Analysis Clustering samples Comparing differences between populations Predictive power analysis Survival predictive power and K-M plot/statistics Nexus DB 2012-10-23 14:00:00 In-Person Zhiwei Che PhD (Director of Application Science BioDiscovery) BTEP 0 Hands-on: Copy Number Analysis and its Application to Genomic Research Using Nexus Copy Number
829
Description

Due to the weather related shutdown of the FEDERAL GOVERNMENT (NIH) this seminar has been POSTPONED ... we will attempt to reschedule at a later date. (10-30-2012)

 

Topics to be covered

 

I. Genomic and transcript related information

  • chromosome location
  • genomic, mRNA and protein sequence
  • alternative splice products and gene structure (exon-intron locations)
  • <...Read More

Due to the weather related shutdown of the FEDERAL GOVERNMENT (NIH) this seminar has been POSTPONED ... we will attempt to reschedule at a later date. (10-30-2012)

 

Topics to be covered

 

I. Genomic and transcript related information

  • chromosome location
  • genomic, mRNA and protein sequence
  • alternative splice products and gene structure (exon-intron locations)
  • primers specific for the gene and spliced product
  • SNPs in the gene and which ones are known to be associated with a phenotype

II. Publications

  • publications documenting experiments that add to our understanding of the HRAS gene

III. Protein related information

  • protein function
  • pathways and downloading all genes in that pathway

IV. Regulation of expression

  •  known transcription factor binding sites

 

Register
Organizer
BTEP
When
Tue, Oct 30, 2012 - 2:15 pm - 4:00 pm
Where
Building 37 Room 4041/4107
Due to the weather related shutdown of the FEDERAL GOVERNMENT (NIH) this seminar has been POSTPONED ... we will attempt to reschedule at a later date. (10-30-2012)   Topics to be covered   I. Genomic and transcript related information chromosome location genomic, mRNA and protein sequence alternative splice products and gene structure (exon-intron locations) primers specific for the gene and spliced product SNPs in the gene and which ones are known to be associated with a phenotype II. Publications publications documenting experiments that add to our understanding of the HRAS gene III. Protein related information protein function pathways and downloading all genes in that pathway IV. Regulation of expression  known transcription factor binding sites   2012-10-30 14:15:00 Building 37 Room 4041/4107 In-Person BTEP 0 Gene Resources: From Transcription Factor Binding Sites to Function
823
Description

Introduction of Next-Generation Sequencing
 
This seminar will provide an overview of Next Generation DNA Sequencing. Highlighting Illumina sequencing technology, and its application in the areas of DNAse-seq, ChIP-seq, and RNA-seq, as well as Genomic sequencing.
 
The "slides" for this talk can be found at the following Prezi Web page:
 
http://prezi.com/iwsuh0in03hn/very-simple-ngs-overview/<...Read More

Introduction of Next-Generation Sequencing
 
This seminar will provide an overview of Next Generation DNA Sequencing. Highlighting Illumina sequencing technology, and its application in the areas of DNAse-seq, ChIP-seq, and RNA-seq, as well as Genomic sequencing.
 
The "slides" for this talk can be found at the following Prezi Web page:
 
http://prezi.com/iwsuh0in03hn/very-simple-ngs-overview/
 
 

Register
Organizer
BTEP
When
Tue, Nov 06, 2012 - 2:15 pm - 3:30 pm
Where
Building 37 Room 4041/4107
Introduction of Next-Generation Sequencing   This seminar will provide an overview of Next Generation DNA Sequencing. Highlighting Illumina sequencing technology, and its application in the areas of DNAse-seq, ChIP-seq, and RNA-seq, as well as Genomic sequencing.   The "slides" for this talk can be found at the following Prezi Web page:  http://prezi.com/iwsuh0in03hn/very-simple-ngs-overview/     2012-11-06 14:15:00 Building 37 Room 4041/4107 In-Person BTEP 0 Introduction to Next-Generation Sequencing
821
Description

The ChIP-Seq data analysis session will specifically focus on visualization of mapped reads, peak detection, motif discovery (find both novel motif and known motif), annotate enriched regions with overlapping genes or database.   The topics to be covered include:


  • BAM file import in Partek Genomics Suite
  • Peak detection
  • Motif discovery in ChIP-seq
  • Find overlapping genes with enriched regions

The ChIP-Seq data analysis session will specifically focus on visualization of mapped reads, peak detection, motif discovery (find both novel motif and known motif), annotate enriched regions with overlapping genes or database.   The topics to be covered include:


  • BAM file import in Partek Genomics Suite
  • Peak detection
  • Motif discovery in ChIP-seq
  • Find overlapping genes with enriched regions
Register
Organizer
BTEP
When
Tue, Nov 13, 2012 - 2:00 pm - 5:00 pm
Where
In-Person
The ChIP-Seq data analysis session will specifically focus on visualization of mapped reads, peak detection, motif discovery (find both novel motif and known motif), annotate enriched regions with overlapping genes or database.   The topics to be covered include:
 BAM file import in Partek Genomics Suite Peak detection Motif discovery in ChIP-seq Find overlapping genes with enriched regions 2012-11-13 14:00:00 In-Person BTEP 0 Hands-on - Analysis of ChIP-Seq Data with Partek Genomics Suite
819
Description

 

This is a repeat class for those who couldn't make it into the class on October 9th
 
Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Partek Pathway. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways.

    <...Read More

 

This is a repeat class for those who couldn't make it into the class on October 9th
 
Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Partek Pathway. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways.

  • Gene Expression Analysis with Affymetrix (cancer dataset)
  • Good experimental design practices
  • Import CEL files/normalization options
  • Describing sample groups
  • Batch correction
  • Detecting differentially expressed genes/creating a gene list
  • Hierarchical Clustering and other visualizations
  • GO Analysis
  • Pathway Enrichment
  • GeneSet analysis (including Pathway ANOVA)
  • Integration with microRNA 

Course Materials:

 

Register
Organizer
BTEP
When
Wed, Nov 14, 2012 - 9:00 am - 12:00 pm
Where
In-Person
  This is a repeat class for those who couldn't make it into the class on October 9th   Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Partek Pathway. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways. Gene Expression Analysis with Affymetrix (cancer dataset) Good experimental design practices Import CEL files/normalization options Describing sample groups Batch correction Detecting differentially expressed genes/creating a gene list Hierarchical Clustering and other visualizations GO Analysis Pathway Enrichment GeneSet analysis (including Pathway ANOVA) Integration with microRNA  Course Materials: Partek Shoe Example Analysis of GSE20437 GSE20437 Publication     2012-11-14 09:00:00 In-Person BTEP 0 Hands-on - Gene Expression using Microarrays with Partek Genomics Suite and Partek Pathway - Repeat Class
820
Description

There will be no talk this week.

There will be no talk this week.

Register
Organizer
BTEP
When
Tue, Nov 20, 2012 - 2:15 pm - 3:30 pm
Where
Building 37 Room 4041/4107
There will be no talk this week. 2012-11-20 14:15:00 Building 37 Room 4041/4107 In-Person BTEP 0 No Talk This Week
822
Description

 
The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public ...Read More

 
The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.
 
In this workshop participants will learn to view Next Generation Sequencing (NGS) datasets in the Integrative Genomics Viewer (IGV).  Topics to be covered include:
 

  • IGV Basics
  • SNPS and Variants
  • Structural Events
  • RNA-Seq and Exome Sequencing
  • Bisulfite Sequencing
  • Sessions and Sharing Data

 

Register
Organizer
BTEP
When
Thu, Nov 29, 2012 - 11:00 am - 12:00 pm
Where
Building 37 Room 4041/4107
  The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.   In this workshop participants will learn to view Next Generation Sequencing (NGS) datasets in the Integrative Genomics Viewer (IGV).  Topics to be covered include:   IGV Basics SNPS and Variants Structural Events RNA-Seq and Exome Sequencing Bisulfite Sequencing Sessions and Sharing Data   2012-11-29 11:00:00 Building 37 Room 4041/4107 In-Person Jim Robinson PhD (Cancer Informatics Broad Institute) BTEP 0 NGS Visualization with the
 Integrative Genomics Viewer (IGV)
817
Description

All researchers at the NCI have unlimited access to Pathway Studio software, MedScan text mining module, and the Mammalian database.

Pathway Studio Application Areas Include:

• Disease Biology
• Disease Biomarkers
• Drug Target Identification
• Protein Functionality
• Cellular Interactions
• Biomarkers of Target Regulation
• Drug Repositioning
• Drug Target Modulation & Characterization
• Safety Biomarkers
• Cellular & Molecular Systems Biology
• Experimental Data Analysis (Gene Expression, Mass. ...Read More

All researchers at the NCI have unlimited access to Pathway Studio software, MedScan text mining module, and the Mammalian database.

Pathway Studio Application Areas Include:

• Disease Biology
• Disease Biomarkers
• Drug Target Identification
• Protein Functionality
• Cellular Interactions
• Biomarkers of Target Regulation
• Drug Repositioning
• Drug Target Modulation & Characterization
• Safety Biomarkers
• Cellular & Molecular Systems Biology
• Experimental Data Analysis (Gene Expression, Mass. Spec, Assay Data, etc.)

The Pathway Studio training will be presented by the Elsevier.  You will learn how to process thousands of PubMed abstracts and build pathways related to disease, biological process or any other topic of interest.  We will explain how to interpret biological networks build as the result of Medscan text-mining.  We will also show you how to navigate the database of relations and build pathways to interpret your experimental data.  You will learn: how in the matter of few clicks find the major regulators responsible for the expression pattern observed in your experiment; how to find differentially expressed pathway and Gene Ontology groups using Gene Set Enrichment analysis; how to obtain high quality pathway pictures of the result pathways for your publication.  The training will empower you to become an advanced user of Pathway Studio and significantly increase your research productivity.

NOTE: This training is also being offered on December 14th, at the Building 549, Scientific Library
, Frederick, MD

Register
Organizer
BTEP
When
Tue, Dec 11, 2012 - 10:00 am - 3:00 pm
Where
In-Person
All researchers at the NCI have unlimited access to Pathway Studio software, MedScan text mining module, and the Mammalian database. Pathway Studio Application Areas Include: • Disease Biology • Disease Biomarkers • Drug Target Identification • Protein Functionality • Cellular Interactions • Biomarkers of Target Regulation • Drug Repositioning • Drug Target Modulation & Characterization • Safety Biomarkers • Cellular & Molecular Systems Biology • Experimental Data Analysis (Gene Expression, Mass. Spec, Assay Data, etc.) The Pathway Studio training will be presented by the Elsevier.  You will learn how to process thousands of PubMed abstracts and build pathways related to disease, biological process or any other topic of interest.  We will explain how to interpret biological networks build as the result of Medscan text-mining.  We will also show you how to navigate the database of relations and build pathways to interpret your experimental data.  You will learn: how in the matter of few clicks find the major regulators responsible for the expression pattern observed in your experiment; how to find differentially expressed pathway and Gene Ontology groups using Gene Set Enrichment analysis; how to obtain high quality pathway pictures of the result pathways for your publication.  The training will empower you to become an advanced user of Pathway Studio and significantly increase your research productivity. NOTE: This training is also being offered on December 14th, at the Building 549, Scientific Library
, Frederick, MD 2012-12-11 10:00:00 In-Person BTEP 0 Hands-On: Pathway Analysis Training using Pathway Studio at Bethesda
818
Description

All researchers at the NCI have unlimited access to Pathway Studio software, MedScan text mining module, and the Mammalian database.

Pathway Studio Application Areas Include:

• Disease Biology
• Disease Biomarkers
• Drug Target Identification
• Protein Functionality
• Cellular Interactions
• Biomarkers of Target Regulation
• Drug Repositioning
• Drug Target Modulation & Characterization
• Safety Biomarkers
• Cellular & Molecular Systems Biology
• Experimental Data Analysis (Gene Expression, Mass. ...Read More

All researchers at the NCI have unlimited access to Pathway Studio software, MedScan text mining module, and the Mammalian database.

Pathway Studio Application Areas Include:

• Disease Biology
• Disease Biomarkers
• Drug Target Identification
• Protein Functionality
• Cellular Interactions
• Biomarkers of Target Regulation
• Drug Repositioning
• Drug Target Modulation & Characterization
• Safety Biomarkers
• Cellular & Molecular Systems Biology
• Experimental Data Analysis (Gene Expression, Mass. Spec, Assay Data, etc.)

The Pathway Studio training will be presented by the Elsevier.  You will learn how to process thousands of PubMed abstracts and build pathways related to disease, biological process or any other topic of interest.  We will explain how to interpret biological networks build as the result of Medscan text-mining.  We will also show you how to navigate the database of relations and build pathways to interpret your experimental data.  You will learn: how in the matter of few clicks find the major regulators responsible for the expression pattern observed in your experiment; how to find differentially expressed pathway and Gene Ontology groups using Gene Set Enrichment analysis; how to obtain high quality pathway pictures of the result pathways for your publication.  The training will empower you to become an advanced user of Pathway Studio and significantly increase your research productivity.

NOTE: This training is also being offered on December 11th, at the NCI training facility 6116 Executive Blvd.

Register
Organizer
BTEP
When
Fri, Dec 14, 2012 - 10:00 am - 3:00 pm
Where
Building 549, Scientific Library, Frederick, MD
All researchers at the NCI have unlimited access to Pathway Studio software, MedScan text mining module, and the Mammalian database. Pathway Studio Application Areas Include: • Disease Biology • Disease Biomarkers • Drug Target Identification • Protein Functionality • Cellular Interactions • Biomarkers of Target Regulation • Drug Repositioning • Drug Target Modulation & Characterization • Safety Biomarkers • Cellular & Molecular Systems Biology • Experimental Data Analysis (Gene Expression, Mass. Spec, Assay Data, etc.) The Pathway Studio training will be presented by the Elsevier.  You will learn how to process thousands of PubMed abstracts and build pathways related to disease, biological process or any other topic of interest.  We will explain how to interpret biological networks build as the result of Medscan text-mining.  We will also show you how to navigate the database of relations and build pathways to interpret your experimental data.  You will learn: how in the matter of few clicks find the major regulators responsible for the expression pattern observed in your experiment; how to find differentially expressed pathway and Gene Ontology groups using Gene Set Enrichment analysis; how to obtain high quality pathway pictures of the result pathways for your publication.  The training will empower you to become an advanced user of Pathway Studio and significantly increase your research productivity. NOTE: This training is also being offered on December 11th, at the NCI training facility 6116 Executive Blvd. 2012-12-14 10:00:00 Building 549, Scientific Library, Frederick, MD In-Person Cindy Sood PhD (Solutions Specialist Elsevier) BTEP 0 Hands-On: Pathway Analysis Training using Pathway Studio at Frederick
816
Description

This training session will focus on Gene Expression analysis and the rich set of results possible from RNA-Seq based studies. In addition to differential gene expression, researchers also have the opportunity to discover SNPs, and important alternative splicing patterns that result in allele-specific expression. The following topics will be discussed:

  • Alignment
  • Pre/Post Alignment QA/QC 
  • Trimming, filtering reads
  • Quantification
  • Differential Expression Detection ...Read More

This training session will focus on Gene Expression analysis and the rich set of results possible from RNA-Seq based studies. In addition to differential gene expression, researchers also have the opportunity to discover SNPs, and important alternative splicing patterns that result in allele-specific expression. The following topics will be discussed:

  • Alignment
  • Pre/Post Alignment QA/QC 
  • Trimming, filtering reads
  • Quantification
  • Differential Expression Detection using Gene Specific Analysis
  • Variants/Indel Detection and Annotation
  • Visualization (PCA, Dot Plot, Chromosome View, etc.)
  • Allele Specific Expression Analysis
  • Alt-splicing Detection
  • Biological interpretation: Gene Set Analysis and Pathway Analysis

 

Partek Genomics Suite is  available to all researchers affiliated with CCR 

 

Course Materials:

 

Register
Organizer
BTEP
When
Tue, Feb 05, 2013 - 2:00 pm - 5:00 pm
Where
6116 Executive Blvd., Room 4075
This training session will focus on Gene Expression analysis and the rich set of results possible from RNA-Seq based studies. In addition to differential gene expression, researchers also have the opportunity to discover SNPs, and important alternative splicing patterns that result in allele-specific expression. The following topics will be discussed: Alignment Pre/Post Alignment QA/QC  Trimming, filtering reads Quantification Differential Expression Detection using Gene Specific Analysis Variants/Indel Detection and Annotation Visualization (PCA, Dot Plot, Chromosome View, etc.) Allele Specific Expression Analysis Alt-splicing Detection Biological interpretation: Gene Set Analysis and Pathway Analysis   Partek Genomics Suite is  available to all researchers affiliated with CCR    Course Materials: Introduction to NGS (PDF)   2013-02-05 14:00:00 6116 Executive Blvd., Room 4075 In-Person BTEP 0 Hands-on: RNA-Seq Data Analysis with Partek Genomics Suite
815
Description

 
This lecture will provide an overview of Illumina sequencing technology as implemented at the CCR Sequencing Facility (SF). It will outline the data and sample QC and analysis workflow performed by the facility and will provide guidance for understanding the files/data provided by the SF.  Subsequent bioinformatics options and the initial project submission process will be reviewed. The topics covered will include:
 

Overview of Illumina Sequencing TechnologiesRead More

 
This lecture will provide an overview of Illumina sequencing technology as implemented at the CCR Sequencing Facility (SF). It will outline the data and sample QC and analysis workflow performed by the facility and will provide guidance for understanding the files/data provided by the SF.  Subsequent bioinformatics options and the initial project submission process will be reviewed. The topics covered will include:
 

Overview of Illumina Sequencing Technologies

  • Library preparation
  • Multiplex and barcodes
  • Cluster amplification and sequencing

Data and Sample QC and Analysis Steps

  •   QC and analysis workflows
  •   QC metrics

Illumina Data File Structure and Format

  •   Basecall and alignment files
  •   SAM/BAM manipulation
  •   Variants call data files

Overview of Illumina BaseSpace

  •   BaseSpace highlights
  •   BaseSpace applications
Register
Organizer
BTEP
When
Tue, Feb 12, 2013 - 2:15 pm - 3:30 pm
Where
Building 37 Room 4041/4107
  This lecture will provide an overview of Illumina sequencing technology as implemented at the CCR Sequencing Facility (SF). It will outline the data and sample QC and analysis workflow performed by the facility and will provide guidance for understanding the files/data provided by the SF.  Subsequent bioinformatics options and the initial project submission process will be reviewed. The topics covered will include:   Overview of Illumina Sequencing Technologies Library preparation Multiplex and barcodes Cluster amplification and sequencing Data and Sample QC and Analysis Steps   QC and analysis workflows   QC metrics Illumina Data File Structure and Format   Basecall and alignment files   SAM/BAM manipulation   Variants call data files Overview of Illumina BaseSpace   BaseSpace highlights   BaseSpace applications 2013-02-12 14:15:00 Building 37 Room 4041/4107 In-Person Yongmei Zhao (CCR-SF IFX Group) BTEP 0 Introduction to Illumina Sequencing Data QC and Analysis
814
Description

Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research.
This training session will cover: 
 
Large Scale (gene expression, proteomics, Metabolomics) Data Analysis
Using an example gene expression dataset the basic to intermediate IPA functionalities will be covered.

  • Upload single and multiple observation datasets
           Microarray, RNAseq, ...Read More

Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research.
This training session will cover: 
 
Large Scale (gene expression, proteomics, Metabolomics) Data Analysis
Using an example gene expression dataset the basic to intermediate IPA functionalities will be covered.

  • Upload single and multiple observation datasets
           Microarray, RNAseq, proteomic, miRNA, metabolite data
  • Find and interpret the most relevant processes and disease associated with your data
  • Find and interpret the most relevant canonical pathway
  • Identify predicted upstream regulators (transcription factors, miRNA, receptors, drugs, etc.)
  • Understand the basics of the Network generation algorithm and how to interpret/modify the network result

Comparing Large Data sets and results
Using an example microarray datasets, methods for comparing core analysis results and gene lists will be discussed. In addition, we will discuss integrating multiple experimental platforms such as microarray, SNPs, proteomics, etc.

  • Comparing IPA core analysis results
  • Comparing datasets, gene lists, and members of a core analysis
  • Using the expression bar-chart overlay option
  • Integrate multiple experimental platforms together
  •  

Ingenuity Pathways Analysis is  available to all researchers affiliated with the NCI
 

Register
Organizer
BTEP
When
Tue, Feb 19, 2013 - 9:00 am - 5:00 pm
Where
6116 Executive Blvd. Room 4075
Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research. This training session will cover:   Large Scale (gene expression, proteomics, Metabolomics) Data Analysis Using an example gene expression dataset the basic to intermediate IPA functionalities will be covered. Upload single and multiple observation datasets        Microarray, RNAseq, proteomic, miRNA, metabolite data Find and interpret the most relevant processes and disease associated with your data Find and interpret the most relevant canonical pathway Identify predicted upstream regulators (transcription factors, miRNA, receptors, drugs, etc.) Understand the basics of the Network generation algorithm and how to interpret/modify the network result Comparing Large Data sets and results Using an example microarray datasets, methods for comparing core analysis results and gene lists will be discussed. In addition, we will discuss integrating multiple experimental platforms such as microarray, SNPs, proteomics, etc. Comparing IPA core analysis results Comparing datasets, gene lists, and members of a core analysis Using the expression bar-chart overlay option Integrate multiple experimental platforms together   Ingenuity Pathways Analysis is  available to all researchers affiliated with the NCI   2013-02-19 09:00:00 6116 Executive Blvd. Room 4075 In-Person Darryl Gietzen PhD (Field Application Scientist Ingenuity) BTEP 0 Hands-on - Ingenuity Pathways Analysis (IPA) Beginners Class
813
Description

Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research.
This training session will cover Advanced uses of this software: 
 

Gene Information, Pathway building, target characterization

This session will cover how to use IPA’s Knowledge Base for deep investigation of any gene, protein, or metabolite and how to further refine gene ...Read More

Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research.
This training session will cover Advanced uses of this software: 
 

Gene Information, Pathway building, target characterization

This session will cover how to use IPA’s Knowledge Base for deep investigation of any gene, protein, or metabolite and how to further refine gene sets isolated from large scale data analyses.

  • Search for a Gene/Chemical/function and drug
  • Performing an Advanced Search: Limiting results to a molecule type, family or subcellular location.
  • Add molecules from search results a pathway
  • Understanding the legend
  • General pathway navigating
  • Using the pathway Build Tools
  • Using the Overlay interpretation tools

Understanding IPA Statistics

  • How is the Fisher’s Exact Test calculated
  • How are z-scores calculated and what does it mean

Micro RNA and biomarkers in IPA

  • This training session will focus on two advanced workflows: the biomarkers interpretation and the microRNA-mRNA interpretation. After this training session a user should be able to:
  • Run a microRNA filter Analysis
  • Filter the microRNA- targets relationship using a mRNA dataset.
  • Explore the functional involvement of the microRNA’s targets within a Core analysis.
  • Identify potential microRNA targets by using the pathway functionalities.
  • Run and View a Biomarkers Filter Analysis
  • Explore further the biomarkers result in pathway and list.
  • Generate a Biomarker Filter comparison analysis.

 
Ingenuity Pathways Analysis is  available to all researchers affiliated with the NCI

Register
Organizer
BTEP
When
Wed, Feb 20, 2013 - 9:00 am - 5:00 pm
Where
6116 Executive Blvd. Room 4075
Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research. This training session will cover Advanced uses of this software:    Gene Information, Pathway building, target characterization This session will cover how to use IPA’s Knowledge Base for deep investigation of any gene, protein, or metabolite and how to further refine gene sets isolated from large scale data analyses. Search for a Gene/Chemical/function and drug Performing an Advanced Search: Limiting results to a molecule type, family or subcellular location. Add molecules from search results a pathway Understanding the legend General pathway navigating Using the pathway Build Tools Using the Overlay interpretation tools Understanding IPA Statistics How is the Fisher’s Exact Test calculated How are z-scores calculated and what does it mean Micro RNA and biomarkers in IPA This training session will focus on two advanced workflows: the biomarkers interpretation and the microRNA-mRNA interpretation. After this training session a user should be able to: Run a microRNA filter Analysis Filter the microRNA- targets relationship using a mRNA dataset. Explore the functional involvement of the microRNA’s targets within a Core analysis. Identify potential microRNA targets by using the pathway functionalities. Run and View a Biomarkers Filter Analysis Explore further the biomarkers result in pathway and list. Generate a Biomarker Filter comparison analysis.  Ingenuity Pathways Analysis is  available to all researchers affiliated with the NCI 2013-02-20 09:00:00 6116 Executive Blvd. Room 4075 In-Person Darryl Gietzen PhD (Field Application Scientist Ingenuity) BTEP 0 Hands-on - Ingenuity Pathways Analysis (IPA) Advanced Class
812
Description

 
Comparison Study of NGS SNP Detection Tools

  • Brief background and introduction for the current status of SNP detection field and each of the selected tools to be compared
  • Description of our benchmark exome-seq data with pedigree info and SNP array data from matched-samples and why they are useful for comparison of these tools for SNP call quality
  • Comparison and validation results of these tools using the ...Read More

 
Comparison Study of NGS SNP Detection Tools

  • Brief background and introduction for the current status of SNP detection field and each of the selected tools to be compared
  • Description of our benchmark exome-seq data with pedigree info and SNP array data from matched-samples and why they are useful for comparison of these tools for SNP call quality
  • Comparison and validation results of these tools using the benchmark data
  • Conclusion and take-home message
  • Q & A session

 

Detailed Illustration of the Practical Usage of Each SNP Detection Tool

  • Brief introduction of practical aspects of the tools (e.g., download, installation, interface, running environment, basic system requirement etc)
  • Practical command lines for command-driven tool(s), parameter options, wrapper script examples for the command-driven tools, interface for commercial tools
  • Brief discussion of result files and some diagnosis plots, etc.
  • Q & A session

 

 

Register
Organizer
BTEP
When
Tue, Feb 26, 2013 - 2:15 pm - 5:00 pm
Where
Building 37 Room 4041/4107
  Comparison Study of NGS SNP Detection Tools Brief background and introduction for the current status of SNP detection field and each of the selected tools to be compared Description of our benchmark exome-seq data with pedigree info and SNP array data from matched-samples and why they are useful for comparison of these tools for SNP call quality Comparison and validation results of these tools using the benchmark data Conclusion and take-home message Q & A session   Detailed Illustration of the Practical Usage of Each SNP Detection Tool Brief introduction of practical aspects of the tools (e.g., download, installation, interface, running environment, basic system requirement etc) Practical command lines for command-driven tool(s), parameter options, wrapper script examples for the command-driven tools, interface for commercial tools Brief discussion of result files and some diagnosis plots, etc. Q & A session     2013-02-26 14:15:00 Building 37 Room 4041/4107 In-Person BTEP 0 Introduction to SNP discovery tools used for Next Generation Sequencing data
811
Description

The cBio Cancer Genomics Portal Helps Researchers Explore Multidimensional Cancer Genomics Data
This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets.
As of early 2012 the Portal contains data for 7848 tumor samples from 26 cancer studies.
 

The cBio Cancer Genomics Portal Helps Researchers Explore Multidimensional Cancer Genomics Data
This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets.
As of early 2012 the Portal contains data for 7848 tumor samples from 26 cancer studies.
 

Register
Organizer
BTEP
When
Mon, Mar 04, 2013 - 2:15 pm - 4:00 pm
Where
Building 37 Room 4041/4107
The cBio Cancer Genomics Portal Helps Researchers Explore Multidimensional Cancer Genomics Data This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets. As of early 2012 the Portal contains data for 7848 tumor samples from 26 cancer studies.   2013-03-04 14:15:00 Building 37 Room 4041/4107 In-Person Nikolaus Schultz PhD (Computational Biology Center Memorial Sloan-Kettering Cancer Center) BTEP 0 Introduction to the cBio Genomics Portal
810
Description

 
Geneious is an integrated and extensible software platform for the organization, visualization and analysis of DNA and protein sequence information. Researchers can analyze any NGS data alongside traditional Sanger data and combine technologies in hybrid approaches for their analyses. 

THIS SEMINAR IS POSPONED AND WILL BE RESCHEDULED

  • Re-sequencing workflows to identify novel mutational variants
  • De novo sequencing and assembly to identify novel haplotypes
  • <...Read More

 
Geneious is an integrated and extensible software platform for the organization, visualization and analysis of DNA and protein sequence information. Researchers can analyze any NGS data alongside traditional Sanger data and combine technologies in hybrid approaches for their analyses. 

THIS SEMINAR IS POSPONED AND WILL BE RESCHEDULED

  • Re-sequencing workflows to identify novel mutational variants
  • De novo sequencing and assembly to identify novel haplotypes
  • Variant calling to identify SNPs, INDELs and STRs
  • Variant validation of novel NGS variants with PCR-based Sanger re-sequencing
  • Multi-genome comparisons to identify large genomic events 
  • RNA-Seq mapping to identify low/high expressed genes

 
CCR currently has a number of floating licenses for Geneious

Register
Organizer
BTEP
When
Wed, Mar 06, 2013 - 3:00 pm - 5:00 pm
Where
Building 37 Room 4041/4107
  Geneious is an integrated and extensible software platform for the organization, visualization and analysis of DNA and protein sequence information. Researchers can analyze any NGS data alongside traditional Sanger data and combine technologies in hybrid approaches for their analyses.  THIS SEMINAR IS POSPONED AND WILL BE RESCHEDULED Re-sequencing workflows to identify novel mutational variants De novo sequencing and assembly to identify novel haplotypes Variant calling to identify SNPs, INDELs and STRs Variant validation of novel NGS variants with PCR-based Sanger re-sequencing Multi-genome comparisons to identify large genomic events  RNA-Seq mapping to identify low/high expressed genes  CCR currently has a number of floating licenses for Geneious 2013-03-06 15:00:00 Building 37 Room 4041/4107 In-Person Peter Meintjes PhD (Biomatters Ltd Auckland New Zealand ) BTEP 0 NGS Applications with Geneious (POSTPONED)
809
Description

Using pathway analysis in MetaCore/GeneGO to evaluate glioma subtypes from public gene expression data 
MetaCore from GeneGo is an integrated software suite for functional analysis of microarray, metabolic, SAGE, proteomics, siRNA, microRNA, and screening data. MetaCore is based on a high-quality, manually-curated database of:

  • Transcription factors, receptors, ligands, kinases, drugs, and endogenous metabolites
  • Species-specific directional interactions between protein-protein, protein-DNA and protein-RNA, drug targeting, and bioactive ...Read More

Using pathway analysis in MetaCore/GeneGO to evaluate glioma subtypes from public gene expression data 
MetaCore from GeneGo is an integrated software suite for functional analysis of microarray, metabolic, SAGE, proteomics, siRNA, microRNA, and screening data. MetaCore is based on a high-quality, manually-curated database of:

  • Transcription factors, receptors, ligands, kinases, drugs, and endogenous metabolites
  • Species-specific directional interactions between protein-protein, protein-DNA and protein-RNA, drug targeting, and bioactive molecules and their effects
  • Signaling and metabolic pathways represented on maps and networks
  • Rich ontologies for diseases and processes with hierarchical or graphic output

Using a dataset for gene expression profiling in human glial brain tumors, this hands-on class will show how one can analyze Pathway Maps and build custom networks.

 

MetaCore/GeneGo is  available to all researchers affiliated with the NCI

Register
Organizer
BTEP
When
Tue, Mar 12, 2013 - 2:00 pm - 5:00 pm
Where
6116 Executive Blvd. Room 4075
Using pathway analysis in MetaCore/GeneGO to evaluate glioma subtypes from public gene expression data  MetaCore from GeneGo is an integrated software suite for functional analysis of microarray, metabolic, SAGE, proteomics, siRNA, microRNA, and screening data. MetaCore is based on a high-quality, manually-curated database of: Transcription factors, receptors, ligands, kinases, drugs, and endogenous metabolites Species-specific directional interactions between protein-protein, protein-DNA and protein-RNA, drug targeting, and bioactive molecules and their effects Signaling and metabolic pathways represented on maps and networks Rich ontologies for diseases and processes with hierarchical or graphic output Using a dataset for gene expression profiling in human glial brain tumors, this hands-on class will show how one can analyze Pathway Maps and build custom networks.   MetaCore/GeneGo is  available to all researchers affiliated with the NCI 2013-03-12 14:00:00 6116 Executive Blvd. Room 4075 In-Person BTEP 0 MetaCore/GeneGO hands-on training
808
Description

 
Geneious is an integrated and extensible software platform for the organization, visualization and analysis of DNA and protein sequence information. Researchers can analyze any NGS data alongside traditional Sanger data and combine technologies in hybrid approaches for their analyses. 

  • Re-sequencing workflows to identify novel mutational variants
  • De novo sequencing and assembly to identify novel haplotypes
  • Variant calling to identify SNPs, INDELs and STRs
  • Variant ...Read More

 
Geneious is an integrated and extensible software platform for the organization, visualization and analysis of DNA and protein sequence information. Researchers can analyze any NGS data alongside traditional Sanger data and combine technologies in hybrid approaches for their analyses. 

  • Re-sequencing workflows to identify novel mutational variants
  • De novo sequencing and assembly to identify novel haplotypes
  • Variant calling to identify SNPs, INDELs and STRs
  • Variant validation of novel NGS variants with PCR-based Sanger re-sequencing
  • Multi-genome comparisons to identify large genomic events 
  • RNA-Seq mapping to identify low/high expressed genes

 
CCR currently has a number of floating licenses for Geneious

Register
Organizer
BTEP
When
Tue, Apr 02, 2013 - 2:15 pm - 5:00 pm
Where
Building 37 Room 4041/4107
  Geneious is an integrated and extensible software platform for the organization, visualization and analysis of DNA and protein sequence information. Researchers can analyze any NGS data alongside traditional Sanger data and combine technologies in hybrid approaches for their analyses.  Re-sequencing workflows to identify novel mutational variants De novo sequencing and assembly to identify novel haplotypes Variant calling to identify SNPs, INDELs and STRs Variant validation of novel NGS variants with PCR-based Sanger re-sequencing Multi-genome comparisons to identify large genomic events  RNA-Seq mapping to identify low/high expressed genes  CCR currently has a number of floating licenses for Geneious 2013-04-02 14:15:00 Building 37 Room 4041/4107 In-Person Peter Meintjes PhD (Biomatters Ltd Auckland New Zealand ) BTEP 0 NGS Applications with Geneious
807
Description

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). 

  • Using gene sets, e.g., pathways, GO categories, to interpret microarray (and other) biology data
  • Using a measure of differential expression for all the genes, rather than a list of distinguished  genes
  • The general ...Read More

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). 

  • Using gene sets, e.g., pathways, GO categories, to interpret microarray (and other) biology data
  • Using a measure of differential expression for all the genes, rather than a list of distinguished  genes
  • The general approach of the Broad Institute’s GSEA software  // comparison with DAVID (NIAID)
  • The statistics behind GSEA  //  The data files required to use GSEA
  • Understanding the output files produced by GSEA  (April 23: hands on running the GSEA software)

Tuesday April 23rd, 2013 there will be a companion hands-on training session for GSEA

 
Register
Organizer
BTEP
When
Tue, Apr 16, 2013 - 2:15 pm - 3:30 pm
Where
Building 37 Room 4041/4107
Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).  Using gene sets, e.g., pathways, GO categories, to interpret microarray (and other) biology data Using a measure of differential expression for all the genes, rather than a list of distinguished  genes The general approach of the Broad Institute’s GSEA software  // comparison with DAVID (NIAID) The statistics behind GSEA  //  The data files required to use GSEA Understanding the output files produced by GSEA  (April 23: hands on running the GSEA software) Tuesday April 23rd, 2013 there will be a companion hands-on training session for GSEA   2013-04-16 14:15:00 Building 37 Room 4041/4107 In-Person BTEP 0 Introduction to the Broad Institute’s Gene Set Enrichment Analysis (GSEA) software
806
Description

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). 
The GSEA software is a Java based tool freely available from the Broad Institute of MIT and Harvard. 
This training class will walk ...Read More

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). 
The GSEA software is a Java based tool freely available from the Broad Institute of MIT and Harvard. 
This training class will walk you through getting the most out of this software.
Topics to include:

  • Installing GSEA
  • Required input data files and formats
  • Parameter selection
  • Broad Institute Utilities
  • Understanding the output

 
Tuesday April 16th, 2013 there will be a companion lecture session on GSEA
 
Class Notes
 
Class Data
 

Register
Organizer
BTEP
When
Tue, Apr 23, 2013 - 2:00 pm - 5:00 pm
Where
6116 Executive Blvd. Room 4075
Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).  The GSEA software is a Java based tool freely available from the Broad Institute of MIT and Harvard.  This training class will walk you through getting the most out of this software. Topics to include: Installing GSEA Required input data files and formats Parameter selection Broad Institute Utilities Understanding the output  Tuesday April 16th, 2013 there will be a companion lecture session on GSEA  Class Notes  Class Data   2013-04-23 14:00:00 6116 Executive Blvd. Room 4075 In-Person BTEP 0 Hands-on with the Broad Institute’s Gene Set Enrichment Analysis (GSEA) software
805
Description

Intended Audience: This day-long training course is intended for users who wish to get an introduction to the central concepts, strategies, and analysis software offered by Genomatix for transcription factor binding site and promoter analysis, and to learn how to apply them most efficiently. 

  1. Introduction to Genomatix
  2. Why genome annotation is important for promoter analysis
    ElDorado, Gene2Promoter, Comparative Genomics
  3. Transcription ...Read More

Intended Audience: This day-long training course is intended for users who wish to get an introduction to the central concepts, strategies, and analysis software offered by Genomatix for transcription factor binding site and promoter analysis, and to learn how to apply them most efficiently. 

  1. Introduction to Genomatix
  2. Why genome annotation is important for promoter analysis
    ElDorado, Gene2Promoter, Comparative Genomics
  3. Transcription factor binding sites (TFBS) basics
    MatBase, MatInspector
  4. How to define your own TF binding sites de novo
    MatDefine, CoreSearch
  5. Functional promoter analysis
    FastM, FrameWorker, Modelinspector
  6. Putting TFBS and their regulatory targets into biological context
    GeneRanker, Genomatix Pathway System
  7. Analyzing SNP effects on TF binding sites
    SNPInspector, Variant Analysis
  8. Assorted TFBS tools DiAlign/DiAlignTF, SequenceShaper

Genomatix is  available to all researchers affiliated with the NCI

Register
Organizer
BTEP
When
Tue, Apr 30, 2013 - 9:00 am - 5:00 pm
Where
6116 Executive Blvd. Room 4075
Intended Audience: This day-long training course is intended for users who wish to get an introduction to the central concepts, strategies, and analysis software offered by Genomatix for transcription factor binding site and promoter analysis, and to learn how to apply them most efficiently.  Introduction to Genomatix Why genome annotation is important for promoter analysis ElDorado, Gene2Promoter, Comparative Genomics Transcription factor binding sites (TFBS) basics MatBase, MatInspector How to define your own TF binding sites de novo MatDefine, CoreSearch Functional promoter analysis FastM, FrameWorker, Modelinspector Putting TFBS and their regulatory targets into biological context GeneRanker, Genomatix Pathway System Analyzing SNP effects on TF binding sites SNPInspector, Variant Analysis Assorted TFBS tools DiAlign/DiAlignTF, SequenceShaper Genomatix is  available to all researchers affiliated with the NCI 2013-04-30 09:00:00 6116 Executive Blvd. Room 4075 In-Person BTEP 0 Hands-on: Transcription Factor Binding Site and Promoter Analysis with Genomatix
804
Description

Intended Audience: This day-long training course is intended for Users who want to apply Next Generation Sequencing methodologies for DNA-Seq, Methyl-Seq, small RNA-Seq, RNA-Seq and ChIP-Seq studies.  All analyses are done on the Genomatix Mining Station (GMS) and Genomatix Genome Analyzer (GGA)

  1. Methodological background and Genomatix Mining Station (GMS):
    • Sequence statistics, mapping and mapping statistics
    • Read classification
    • Small variant detection
  2. <...Read More

Intended Audience: This day-long training course is intended for Users who want to apply Next Generation Sequencing methodologies for DNA-Seq, Methyl-Seq, small RNA-Seq, RNA-Seq and ChIP-Seq studies.  All analyses are done on the Genomatix Mining Station (GMS) and Genomatix Genome Analyzer (GGA)

  1. Methodological background and Genomatix Mining Station (GMS):
    • Sequence statistics, mapping and mapping statistics
    • Read classification
    • Small variant detection
  2. Methodological background and hands-on examples using Genomatix Genome Analyzer (GGA):
    • SNP analysis
    • Copy Number Variation (CNV) analysis
    • Methyl-Seq: Data Visualization
    • Small RNA-Seq and RNA-Seq: Expression Analysis
    • ChIP-Seq: Peak detection and analysis:
      • TF binding site analysis in ChIP peaks
      • De novo motif detection
      • Next-neighbor analysis and regulatory target prediction for ChIP regions
    • Meta analysis of different data sets
Register
Organizer
BTEP
When
Wed, May 01, 2013 - 9:00 am - 5:00 pm
Where
6116 Executive Blvd. Room 4075
Intended Audience: This day-long training course is intended for Users who want to apply Next Generation Sequencing methodologies for DNA-Seq, Methyl-Seq, small RNA-Seq, RNA-Seq and ChIP-Seq studies.  All analyses are done on the Genomatix Mining Station (GMS) and Genomatix Genome Analyzer (GGA) Methodological background and Genomatix Mining Station (GMS): Sequence statistics, mapping and mapping statistics Read classification Small variant detection Methodological background and hands-on examples using Genomatix Genome Analyzer (GGA): SNP analysis Copy Number Variation (CNV) analysis Methyl-Seq: Data Visualization Small RNA-Seq and RNA-Seq: Expression Analysis ChIP-Seq: Peak detection and analysis: TF binding site analysis in ChIP peaks De novo motif detection Next-neighbor analysis and regulatory target prediction for ChIP regions Meta analysis of different data sets 2013-05-01 09:00:00 6116 Executive Blvd. Room 4075 In-Person BTEP 0 Hands-on: Analysis of Next Generation Sequencing Data with Genomatix
803
Description

 
This 2 hour seminar will be an interactive discussion and demonstration of the types of applications and work-flows that can be performed on deep sequencing data generated by the latest instruments from Illumina, Life Technologies (SOLiD and Ion Torrent), Roche/454 and others. Applications include the following:
 

  • Data import - Un-aligned reads (FASTQ, .sff etc.) and aligned reads (SAM/BAM)
  • Read mapping to reference sequence(s)
  • De ...Read More

 
This 2 hour seminar will be an interactive discussion and demonstration of the types of applications and work-flows that can be performed on deep sequencing data generated by the latest instruments from Illumina, Life Technologies (SOLiD and Ion Torrent), Roche/454 and others. Applications include the following:
 

  • Data import - Un-aligned reads (FASTQ, .sff etc.) and aligned reads (SAM/BAM)
  • Read mapping to reference sequence(s)
  • De novo assembly
  • Transcriptome assembly
  • Digital gene expression analysis by RNA Sequencing
  • Exome sequencing by target enrichment
  • Variant detection
  • ChIP Seq Analysis
  • Small RNA analysis
  • Curating reference sequences with annotations of interest
  • Working with Annotation Tracks
  • BLAST  - Find and compare genes, protein products and place contigs
  • Workflows- Visually Creating and Editing Analysis Pipelines

 
CLC bio software (Genomics Server and Genomics Workbench) is available to all researchers affiliated with CCR
 

 

Register
Organizer
BTEP
When
Tue, May 07, 2013 - 2:15 pm - 4:15 pm
Where
Building 37 Room 4041/4107
  This 2 hour seminar will be an interactive discussion and demonstration of the types of applications and work-flows that can be performed on deep sequencing data generated by the latest instruments from Illumina, Life Technologies (SOLiD and Ion Torrent), Roche/454 and others. Applications include the following:   Data import - Un-aligned reads (FASTQ, .sff etc.) and aligned reads (SAM/BAM) Read mapping to reference sequence(s) De novo assembly Transcriptome assembly Digital gene expression analysis by RNA Sequencing Exome sequencing by target enrichment Variant detection ChIP Seq Analysis Small RNA analysis Curating reference sequences with annotations of interest Working with Annotation Tracks BLAST  - Find and compare genes, protein products and place contigs Workflows- Visually Creating and Editing Analysis Pipelines  CLC bio software (Genomics Server and Genomics Workbench) is available to all researchers affiliated with CCR     2013-05-07 14:15:00 Building 37 Room 4041/4107 In-Person Robert Mervis PhD (CLC bio.) BTEP 0 An Introduction to Analysis of Next Generation Sequencing Data using the CLC Genomics Workbench and Genomics Server
802
Description

Nexus Copy Number is a user-friendly desktop application for analysis and visualization of structural variation (copy number, allelic events, and small sequence variations) from CGH and SNP array- and NGS-generated data. The simple and interactive user interface allows for quick review of CNV/LOH/seq. variant results, annotation of samples, and customized report generation. All major statistical methods and algorithms that have become accepted as standards of practice in the field are incorporated into ...Read More

Nexus Copy Number is a user-friendly desktop application for analysis and visualization of structural variation (copy number, allelic events, and small sequence variations) from CGH and SNP array- and NGS-generated data. The simple and interactive user interface allows for quick review of CNV/LOH/seq. variant results, annotation of samples, and customized report generation. All major statistical methods and algorithms that have become accepted as standards of practice in the field are incorporated into an intuitive, easy-to-use workflow for robust and straightforward analysis.
This seminar will highlight the following:

  1. Platform Independent Copy Number Analysis and Visualization
    • Affymetrix
    • Agilent
    • Illumina
    • Exome/Genome Sequencing
    • Other
  2. Co-visualization of Sequence Variation
    • Exome
    • Genome
    • Targeted
  3. Powerful Statistical Analysis Methods
    • Group Comparison
    • Concordance
    • Survival
    • Gene Enrichment
    • Clustering
    • Predictive Power
  4. Query for Aberrations in Nexus DB
    • TCGA
    • GEO
    • ISCA/ICCG

CCR currently has a number of floating licenses for Nexus software

Register
Organizer
BTEP
When
Tue, May 28, 2013 - 2:30 pm - 4:00 pm
Where
Building 37 Room 4041/4107
Nexus Copy Number is a user-friendly desktop application for analysis and visualization of structural variation (copy number, allelic events, and small sequence variations) from CGH and SNP array- and NGS-generated data. The simple and interactive user interface allows for quick review of CNV/LOH/seq. variant results, annotation of samples, and customized report generation. All major statistical methods and algorithms that have become accepted as standards of practice in the field are incorporated into an intuitive, easy-to-use workflow for robust and straightforward analysis. This seminar will highlight the following: Platform Independent Copy Number Analysis and Visualization Affymetrix Agilent Illumina Exome/Genome Sequencing Other Co-visualization of Sequence Variation Exome Genome Targeted Powerful Statistical Analysis Methods Group Comparison Concordance Survival Gene Enrichment Clustering Predictive Power Query for Aberrations in Nexus DB TCGA GEO ISCA/ICCG CCR currently has a number of floating licenses for Nexus software 2013-05-28 14:30:00 Building 37 Room 4041/4107 In-Person BTEP 0 Somatic and Germline Copy Number and Sequence Variant Analysis Using Nexus Software
801
Description

 

  • Common sample prep and library preparation pitfalls
  • Understand what determines the quality of your NGS data
  • Current publishing and data reporting standards for NGS studies
  • Types of experimental designs used NGS studies
  • Ensure efficient use of experimental budget
  • Sample budgets for standard RNA-Seq, CHiP-Seq, Exome-Seq projects
  • Increase precision and accuracy of results
  • Microarrays vs RNA-Seq: pluses and ...Read More

 

  • Common sample prep and library preparation pitfalls
  • Understand what determines the quality of your NGS data
  • Current publishing and data reporting standards for NGS studies
  • Types of experimental designs used NGS studies
  • Ensure efficient use of experimental budget
  • Sample budgets for standard RNA-Seq, CHiP-Seq, Exome-Seq projects
  • Increase precision and accuracy of results
  • Microarrays vs RNA-Seq: pluses and minuses
  • Increase the likelihood for publication in a top-tier journal
Register
Organizer
BTEP
When
Tue, Jun 11, 2013 - 2:15 pm - 3:30 pm
Where
Building 37 Room 4041/4107
  Common sample prep and library preparation pitfalls Understand what determines the quality of your NGS data Current publishing and data reporting standards for NGS studies Types of experimental designs used NGS studies Ensure efficient use of experimental budget Sample budgets for standard RNA-Seq, CHiP-Seq, Exome-Seq projects Increase precision and accuracy of results Microarrays vs RNA-Seq: pluses and minuses Increase the likelihood for publication in a top-tier journal 2013-06-11 14:15:00 Building 37 Room 4041/4107 In-Person BTEP 0 Five ways to get the most from your NGS project and stay on budget
800
Description

This is repeat of the lecture given June 11th on the Bethesda Campus.

  • Common sample prep and library preparation pitfalls
  • Understand what determines the quality of your NGS data
  • Current publishing and data reporting standards for NGS studies
  • Types of experimental designs used NGS studies
  • Ensure efficient use of experimental budget
  • Sample budgets for standard RNA-Seq, CHiP-Seq, Exome-Seq projectsRead More

This is repeat of the lecture given June 11th on the Bethesda Campus.

  • Common sample prep and library preparation pitfalls
  • Understand what determines the quality of your NGS data
  • Current publishing and data reporting standards for NGS studies
  • Types of experimental designs used NGS studies
  • Ensure efficient use of experimental budget
  • Sample budgets for standard RNA-Seq, CHiP-Seq, Exome-Seq projects
  • Increase precision and accuracy of results
  • Microarrays vs RNA-Seq: pluses and minuses
  • Increase the likelihood for publication in a top-tier journal
Register
Organizer
BTEP
When
Thu, Jun 20, 2013 - 2:00 pm - 3:00 pm
Where
NCI-F Building 430, Conference Room 230
This is repeat of the lecture given June 11th on the Bethesda Campus. Common sample prep and library preparation pitfalls Understand what determines the quality of your NGS data Current publishing and data reporting standards for NGS studies Types of experimental designs used NGS studies Ensure efficient use of experimental budget Sample budgets for standard RNA-Seq, CHiP-Seq, Exome-Seq projects Increase precision and accuracy of results Microarrays vs RNA-Seq: pluses and minuses Increase the likelihood for publication in a top-tier journal 2013-06-20 14:00:00 NCI-F Building 430, Conference Room 230 In-Person BTEP 0 Five ways to get the most from your NGS project and stay on budget
799
Description

Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research.

 

Agenda for Day 1 (Tuesday September 17th)

Large Scale (gene expression, proteomics, Metabolomics) Data Analysis

Using an example gene expression dataset the basic to intermediate IPA functionalities will be covered.

  • Upload single and multiple ...Read More

Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research.

 

Agenda for Day 1 (Tuesday September 17th)

Large Scale (gene expression, proteomics, Metabolomics) Data Analysis

Using an example gene expression dataset the basic to intermediate IPA functionalities will be covered.

  • Upload single and multiple observation datasets
           Microarray, RNAseq, proteomic, miRNA, metabolite data
  • Find and interpret the most relevant processes and disease associated with your data
  • Find and interpret the most relevant canonical pathway
  • Identify predicted upstream regulators (transcription factors, miRNA, receptors, drugs, etc.)
  • Understand the basics of the Network generation algorithm and how to interpret/modify the network result

Comparing Large Data sets and results
Using an example microarray datasets, methods for comparing core analysis results and gene lists will be discussed. In addition, we will discuss integrating multiple experimental platforms such as microarray, SNPs, proteomics, etc.

  • Comparing IPA core analysis results
  • Comparing datasets, gene lists, and members of a core analysis
  • Using the expression bar-chart overlay option
  • Integrate multiple experimental platforms together
     

Agenda for Day 2 (Wednesday September 18th)

Gene Information, Pathway building, target characterization

This session will cover how to use IPA’s Knowledge Base for deep investigation of any gene, protein, or metabolite and how to further refine gene sets isolated from large scale data analyses.

  • Search for a Gene/Chemical/function and drug
  • Performing an Advanced Search: Limiting results to a molecule type, family or subcellular location.
  • Add molecules from search results a pathway
  • Understanding the legend
  • General pathway navigating
  • Using the pathway Build Tools
  • Using the Overlay interpretation tools

Understanding IPA Statistics

  • How is the Fisher’s Exact Test calculated
  • How are z-scores calculated and what does it mean

Micro RNA and biomarkers in IPA

  • This training session will focus on two advanced workflows: the biomarkers interpretation and the microRNA-mRNA interpretation. After this training session a user should be able to:
  • Run a microRNA filter Analysis
  • Filter the microRNA- targets relationship using a mRNA dataset.
  • Explore the functional involvement of the microRNA’s targets within a Core analysis.
  • Identify potential microRNA targets by using the pathway functionalities.
  • Run and View a Biomarkers Filter Analysis
  • Explore further the biomarkers result in pathway and list.
  • Generate a Biomarker Filter comparison analysis.

 

Ingenuity Pathways Analysis is  available to all researchers affiliated with the NCI

 

If you plan to drive to the Fernwood building, you will need to park in the 6720C Parking Garage - Parking fees will be collected by cash or credit/debit card.

We apologize for any inconvenience this may cause. CIT Training recommends that NIH staff utilize the NIH Rockledge Shuttle from the Medical Center Metro to the Fernwood building, if at all possible, to avoid having to pay for parking. Exit the shuttle at the 6700B/Fernwood stop.

Directions to the Fernwood facility can be found here

Register
Organizer
BTEP
When
Tue, Sep 17 - Wed, Sep 18, 2013 -9:00 am - 4:30 pm
Where
Fernwood Building, 10401 Fernwood Road, Rm 1NW02, Bethesda, Maryland
Ingenuity Pathways Analysis (IPA)  is software that helps researchers model, analyze, and understand the complex biological and chemical systems at the core of life science research.   Agenda for Day 1 (Tuesday September 17th) Large Scale (gene expression, proteomics, Metabolomics) Data Analysis Using an example gene expression dataset the basic to intermediate IPA functionalities will be covered. Upload single and multiple observation datasets        Microarray, RNAseq, proteomic, miRNA, metabolite data Find and interpret the most relevant processes and disease associated with your data Find and interpret the most relevant canonical pathway Identify predicted upstream regulators (transcription factors, miRNA, receptors, drugs, etc.) Understand the basics of the Network generation algorithm and how to interpret/modify the network result Comparing Large Data sets and results Using an example microarray datasets, methods for comparing core analysis results and gene lists will be discussed. In addition, we will discuss integrating multiple experimental platforms such as microarray, SNPs, proteomics, etc. Comparing IPA core analysis results Comparing datasets, gene lists, and members of a core analysis Using the expression bar-chart overlay option Integrate multiple experimental platforms together   Agenda for Day 2 (Wednesday September 18th) Gene Information, Pathway building, target characterization This session will cover how to use IPA’s Knowledge Base for deep investigation of any gene, protein, or metabolite and how to further refine gene sets isolated from large scale data analyses. Search for a Gene/Chemical/function and drug Performing an Advanced Search: Limiting results to a molecule type, family or subcellular location. Add molecules from search results a pathway Understanding the legend General pathway navigating Using the pathway Build Tools Using the Overlay interpretation tools Understanding IPA Statistics How is the Fisher’s Exact Test calculated How are z-scores calculated and what does it mean Micro RNA and biomarkers in IPA This training session will focus on two advanced workflows: the biomarkers interpretation and the microRNA-mRNA interpretation. After this training session a user should be able to: Run a microRNA filter Analysis Filter the microRNA- targets relationship using a mRNA dataset. Explore the functional involvement of the microRNA’s targets within a Core analysis. Identify potential microRNA targets by using the pathway functionalities. Run and View a Biomarkers Filter Analysis Explore further the biomarkers result in pathway and list. Generate a Biomarker Filter comparison analysis.   Ingenuity Pathways Analysis is  available to all researchers affiliated with the NCI   If you plan to drive to the Fernwood building, you will need to park in the 6720C Parking Garage - Parking fees will be collected by cash or credit/debit card. We apologize for any inconvenience this may cause. CIT Training recommends that NIH staff utilize the NIH Rockledge Shuttle from the Medical Center Metro to the Fernwood building, if at all possible, to avoid having to pay for parking. Exit the shuttle at the 6700B/Fernwood stop. Directions to the Fernwood facility can be found here 2013-09-17 09:00:00 Fernwood Building, 10401 Fernwood Road, Rm 1NW02, Bethesda, Maryland In-Person Sohela Shah (Ingenuity) BTEP 0 Hands-on: Ingenuity Pathways Analysis (IPA)
798
Description

Due to the recent Government Furlough this talk had been POSTPONED and wil be rescheduled at a later date.

This 2-day course, which includes both lecture and hands-on components,  will teach the basic concepts and practical aspects of microarray gene expression analysis. Learn everything from experimental design to statistical analysis and several downstream pathway and pattern discovery methods using both commercial (Partek) and open source software. Those who  successfully complete this course ...Read More

Due to the recent Government Furlough this talk had been POSTPONED and wil be rescheduled at a later date.

This 2-day course, which includes both lecture and hands-on components,  will teach the basic concepts and practical aspects of microarray gene expression analysis. Learn everything from experimental design to statistical analysis and several downstream pathway and pattern discovery methods using both commercial (Partek) and open source software. Those who  successfully complete this course will receive a certificate, that will not only look good on their wall, but will also entitle their lab to an additional subsidy from OSTR towards the cost of microarrays processed by  the LMT Core.

 

Day 1 - AM (9:30-12)  Introductory Lecture
(Maggie Cam, PhD - CCR, NCI)

 

                Introduction

                                Historical Perspective

                                Microarray Technologies, Sample Processing Methods

                                Microarray comparisons to RNA-Seq

                Data Analysis

                                Experimental Design

                                QC methods

                                Preprocessing: Normalization and low level analysis algorithms

                Statistical Analysis

                                Common statistical models used for analysis of microarray data

                                Examples of blocking

                                Batch effects and removal methods

                Validation and Downstream Analysis

                                Validation methods

                                Gene Ontology Enrichment and Pathway analysis tools

                                Major Software applications

                                Public Repositories of Microarray Data

                Bioinformatics Core Presentation  (Manjula Kasoji - CCRIFX)

                                Lessons learned and how to work with the core                               

 

Day 1 - PM (1-5 pm):  Hands-on  Microarray analysis using Partek Genomics Suite
(Xiaowen Wang, PhD - Partek)

                Partek Genomics Suite Analysis Workflow

                                Process Cel files (RMA)

                                Looking at data distributions, histograms, bar plots, MA plots, etc.

                                Statistical Analysis (Anova)

                                Create contrasts

                                False Discovery Analysis

                                Making lists of significant genes

                                Venn Diagrams

                                Work independently on dataset

 

Day 2  AM (9:30-12):  Hands-on  Partek Genomics Suite Analysis and Partek Pathway 
(Xiaowen Wang, PhD - Partek)

                                Unsupervised Clustering

                                Custom Filtering

                                Pathway ANOVA

                                Work independently on another dataset

 

Day 2 PM (1-5): GeneSet Enrichment Analysis (GSEA)
(Alan Berger, PhD - School of Medicine Johns Hopkins University) 

 

GSEA is a computational method that determines which (if any) a priori defined sets of genes are   significantly differentially expressed, as an ensemble, between two biological states.  It is an open-source program developed by the Broad Institute:    http://www.broadinstitute.org/gsea/index.jsp

 

                Lecture

                                 The general approach of gene set enrichment methods and comparison with DAVID

                                 How GSEA measures differential expression for each set of genes

                                 Controlling effects of multiple comparisons in GSEA (false discovery rate)

                                 The Broad Institute library of groups of gene sets (MSigDB)

                                 What files and formats are needed for GSEA

                                 User options and running GSEA

                Hands-on

                                 Loading the GSEA required input files for an example dataset

                                 Using and choosing values in the GSEA GUI interface

                                 Rank-based analysis

                                 Full dataset analysis

                                 Understanding the GSEA outputs and judging significance in the results 

                                Work independently on another dataset

 

Register
Organizer
BTEP
When
Thu, Oct 03 - Fri, Oct 04, 2013 -9:30 am - 5:00 pm
Where
In-Person
Due to the recent Government Furlough this talk had been POSTPONED and wil be rescheduled at a later date. This 2-day course, which includes both lecture and hands-on components,  will teach the basic concepts and practical aspects of microarray gene expression analysis. Learn everything from experimental design to statistical analysis and several downstream pathway and pattern discovery methods using both commercial (Partek) and open source software. Those who  successfully complete this course will receive a certificate, that will not only look good on their wall, but will also entitle their lab to an additional subsidy from OSTR towards the cost of microarrays processed by  the LMT Core.   Day 1 - AM (9:30-12)  Introductory Lecture (Maggie Cam, PhD - CCR, NCI)                   Introduction                                 Historical Perspective                                 Microarray Technologies, Sample Processing Methods                                 Microarray comparisons to RNA-Seq                 Data Analysis                                 Experimental Design                                 QC methods                                 Preprocessing: Normalization and low level analysis algorithms                 Statistical Analysis                                 Common statistical models used for analysis of microarray data                                 Examples of blocking                                 Batch effects and removal methods                 Validation and Downstream Analysis                                 Validation methods                                 Gene Ontology Enrichment and Pathway analysis tools                                 Major Software applications                                 Public Repositories of Microarray Data                 Bioinformatics Core Presentation  (Manjula Kasoji - CCRIFX)                                 Lessons learned and how to work with the core                                  Day 1 - PM (1-5 pm):  Hands-on  Microarray analysis using Partek Genomics Suite (Xiaowen Wang, PhD - Partek)                 Partek Genomics Suite Analysis Workflow                                 Process Cel files (RMA)                                 Looking at data distributions, histograms, bar plots, MA plots, etc.                                 Statistical Analysis (Anova)                                 Create contrasts                                 False Discovery Analysis                                 Making lists of significant genes                                 Venn Diagrams                                 Work independently on dataset   Day 2  AM (9:30-12):  Hands-on  Partek Genomics Suite Analysis and Partek Pathway  (Xiaowen Wang, PhD - Partek)                                 Unsupervised Clustering                                 Custom Filtering                                 Pathway ANOVA                                 Work independently on another dataset   Day 2 PM (1-5): GeneSet Enrichment Analysis (GSEA) (Alan Berger, PhD - School of Medicine Johns Hopkins University)    GSEA is a computational method that determines which (if any) a priori defined sets of genes are   significantly differentially expressed, as an ensemble, between two biological states.  It is an open-source program developed by the Broad Institute:    http://www.broadinstitute.org/gsea/index.jsp                   Lecture                                  The general approach of gene set enrichment methods and comparison with DAVID                                  How GSEA measures differential expression for each set of genes                                  Controlling effects of multiple comparisons in GSEA (false discovery rate)                                  The Broad Institute library of groups of gene sets (MSigDB)                                  What files and formats are needed for GSEA                                  User options and running GSEA                 Hands-on                                  Loading the GSEA required input files for an example dataset                                  Using and choosing values in the GSEA GUI interface                                  Rank-based analysis                                  Full dataset analysis                                  Understanding the GSEA outputs and judging significance in the results                                  Work independently on another dataset   2013-10-03 09:30:00 In-Person Maggie Cam (NCI CCBR),Xiaowen Wang (Partek) BTEP 0 TO BE RESCHEDULED - Microarray Workshop (2 day)
797
Description

Due to the recent Government Furlough this talk had been POSTPONED

This talk will now take place on November 5th, at the same time and location.

This talk will provide an overview of the extensive computing resources (both hardware and software) available throught the NIH Helix Systems.

Background:

The Helix Systems group is responsible for the planning and management of high-performance ...Read More

Due to the recent Government Furlough this talk had been POSTPONED

This talk will now take place on November 5th, at the same time and location.

This talk will provide an overview of the extensive computing resources (both hardware and software) available throught the NIH Helix Systems.

Background:

The Helix Systems group is responsible for the planning and management of high-performance computing systems specifically for the intramural NIH community. These systems include Helix, a multiprocessor shared-memory system for interactive use; Biowulf, an 18,000+ processor Linux cluster; and Helixweb, which provides a number of scientific tools via the web. These systems provide access to an extensive library of computational applications for molecular and structural biology, genomics, mathematical and graphical analysis, and other scientific fields.

Register
Organizer
BTEP
When
Tue, Oct 22, 2013 - 3:00 pm - 4:30 pm
Where
Building 37 Room 4041/4107
Due to the recent Government Furlough this talk had been POSTPONED This talk will now take place on November 5th, at the same time and location. This talk will provide an overview of the extensive computing resources (both hardware and software) available throught the NIH Helix Systems. Background: The Helix Systems group is responsible for the planning and management of high-performance computing systems specifically for the intramural NIH community. These systems include Helix, a multiprocessor shared-memory system for interactive use; Biowulf, an 18,000+ processor Linux cluster; and Helixweb, which provides a number of scientific tools via the web. These systems provide access to an extensive library of computational applications for molecular and structural biology, genomics, mathematical and graphical analysis, and other scientific fields. 2013-10-22 15:00:00 Building 37 Room 4041/4107 In-Person Susan Chacko (HPC Biowulf),Steven Fellini PhD (NIH) BTEP 0 POSTPONED - Overview of Helix/Biowulf
796
Description

This talk will provide an overview of the extensive computing resources (both hardware and software) available through the NIH Helix Systems.

Background:

The Helix Systems group is responsible for the planning and management of high-performance computing systems specifically for the intramural NIH community. These systems include Helix, a multiprocessor shared-memory system ...Read More

This talk will provide an overview of the extensive computing resources (both hardware and software) available through the NIH Helix Systems.

Background:

The Helix Systems group is responsible for the planning and management of high-performance computing systems specifically for the intramural NIH community. These systems include Helix, a multiprocessor shared-memory system for interactive use; Biowulf, an 18,000+ processor Linux cluster; and Helixweb, which provides a number of scientific tools via the web. These systems provide access to an extensive library of computational applications for molecular and structural biology, genomics, mathematical and graphical analysis, and other scientific fields.

  This talk is a rescheduled event for the talk postponed from October 22nd, 2013,

 

Register
Organizer
BTEP
When
Tue, Nov 05, 2013 - 3:00 pm - 4:30 pm
Where
Building 37 Room 4041/4107
This talk will provide an overview of the extensive computing resources (both hardware and software) available through the NIH Helix Systems. Background: The Helix Systems group is responsible for the planning and management of high-performance computing systems specifically for the intramural NIH community. These systems include Helix, a multiprocessor shared-memory system for interactive use; Biowulf, an 18,000+ processor Linux cluster; and Helixweb, which provides a number of scientific tools via the web. These systems provide access to an extensive library of computational applications for molecular and structural biology, genomics, mathematical and graphical analysis, and other scientific fields.   This talk is a rescheduled event for the talk postponed from October 22nd, 2013,   2013-11-05 15:00:00 Building 37 Room 4041/4107 In-Person Susan Chacko (HPC Biowulf),Steven Fellini PhD (NIH) BTEP 0 Overview of Helix/Biowulf
795
Description

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Learn everything from experimental design to statistical analysis and several downstream motif and pattern discovery methods using both commercial (Genomatix) and open source software. Those who successfully complete this course will receive a certificate, that will not only look good on their wall, but will also entitle their lab to an additional ...Read More

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Learn everything from experimental design to statistical analysis and several downstream motif and pattern discovery methods using both commercial (Genomatix) and open source software. Those who successfully complete this course will receive a certificate, that will not only look good on their wall, but will also entitle their lab to an additional subsidy from OSTR towards the cost of a ChIP-Seq sequencing run. 

Day 1 - AM (9:30-12)  Introductory Lecture
(Peter FitzGerald, PhD - CCR, NCI)

  • Introduction
    • Historical Perspective and Technical Variations
    • Experimental methodology
    • Comparison to ChIP-Chip
  • Data Analysis
    • Experimental Design
    • Quality Control 
    • Peak Calling (Different methodologies)
    • Major Sources of Error
    • Causes of Fail Experiments
    • Validation Methods
  • Sequence Specific Binding
    • Identification of Motifs
    • Overexpressed sequences
    • Pathways
  • Resources
    • Public Repositories
    • Literature References
    • Software directories

Bioinformatics Core Presentation 
 (Anand Merchant, PhD - CCRIFX)

  • Lessons learned
  • How to work with the Core
  • Encode "Best Practices"
  • Guides to success

Day 1 - AM (2:00-5:00)  Hands-On with Genomatix
(Susan Dombrowski, PhD - Genomatix)

  • Interacting with the system
  • Importing Data
  • Peak Calling

Day 2 - AM (9:30-12:00)  Hands-On with Genomatix
(Susan Dombrowski, PhD - Genomatix)

  • Biological insights
  • Motif Finding
  • Pathways

Day 2 - AM (2:00-5:00)  Data Visualization 

(Peter FitzGerald, PhD - CCR, NCI)

  • Review
  • Visualization Tools
  • Examples of good and bad data
Register
Organizer
BTEP
When
Thu, Nov 21 - Fri, Nov 22, 2013 -9:30 am - 5:00 pm
Where
In-Person
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Learn everything from experimental design to statistical analysis and several downstream motif and pattern discovery methods using both commercial (Genomatix) and open source software. Those who successfully complete this course will receive a certificate, that will not only look good on their wall, but will also entitle their lab to an additional subsidy from OSTR towards the cost of a ChIP-Seq sequencing run.  Day 1 - AM (9:30-12)  Introductory Lecture (Peter FitzGerald, PhD - CCR, NCI) Introduction Historical Perspective and Technical Variations Experimental methodology Comparison to ChIP-Chip Data Analysis Experimental Design Quality Control  Peak Calling (Different methodologies) Major Sources of Error Causes of Fail Experiments Validation Methods Sequence Specific Binding Identification of Motifs Overexpressed sequences Pathways Resources Public Repositories Literature References Software directories Bioinformatics Core Presentation  (Anand Merchant, PhD - CCRIFX) Lessons learned How to work with the Core Encode "Best Practices" Guides to success Day 1 - AM (2:00-5:00)  Hands-On with Genomatix(Susan Dombrowski, PhD - Genomatix) Interacting with the system Importing Data Peak Calling Day 2 - AM (9:30-12:00)  Hands-On with Genomatix(Susan Dombrowski, PhD - Genomatix) Biological insights Motif Finding Pathways Day 2 - AM (2:00-5:00)  Data Visualization  (Peter FitzGerald, PhD - CCR, NCI) Review Visualization Tools Examples of good and bad data 2013-11-21 09:30:00 In-Person Peter FitzGerald (GAU) BTEP 0 ChIP-Seq Data Analysis Workshop (2 day)
794
Description
Register
Organizer
BTEP
When
Thu, Dec 05, 2013 - 9:00 am - 1:00 pm
Where
In-Person
2013-12-05 09:00:00 In-Person Susan Chacko (HPC Biowulf) BTEP 0 Hands-on Introduction of Helix/Biowulf
793
Description

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek, Genomatix) and open source software.

Tuesday 18th,  9:30-12:00
Introductory Lecture 
Sean Davis, MD, PhD - CCR, NCI
...Read More

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek, Genomatix) and open source software.

Tuesday 18th,  9:30-12:00
Introductory Lecture 
Sean Davis, MD, PhD - CCR, NCI
Link to Talk Slides on SlideShare
Tuedsay 18th, 12:00-12:30
A Perspective from the CCR BioInformatics Core (CCRIFX)
Parthav Jailwala - CCRIFX, NCI

  • Lessons learned
  • How to work with the Core
  • "Best Practices"
  • Guides to success

Tuesday 18th, 2:00-5:00
Hands-on: Open Source Tools 
Sean Davis, MD, PhD - CCR, NCI
 
Link to Hands on Tutorial
Wednesday 19th, 9:30-12:30
Hands-on:  RNA-Seq Analysis using Partek Flow
Xiaowen Wang, PhD - Partek

  • Data import
  • Add sample attribute
  • Pre-alignment QA/QC
  • Alignment
  • Post-alignment QA/QC
  • Quantification
  • Differential expression detection
  • Build analysis pipeline

Wednesday 19th, 2:00-5:00
Hands-on:  RNA-Seq Analysis using Geomatix
Susan Dombrowski, PhD -  Genomatix Software, Inc.

  • Introduction to the Genomatix Genome Analyzer (GGA) 
  • Import of data to the GGA 
  • Expression Analysis of RNA-Seq 
  • Visualization of RNA-seq isoforms
  • Pathway Analysis 
  • Defining gene regulatory models of differentially-expressed genes

 

Register
Organizer
BTEP
When
Tue, Feb 18 - Wed, Feb 19, 2014 -9:30 am - 5:00 pm
Where
Bldg 12A, Room B51, Bethesda, MD
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek, Genomatix) and open source software. Tuesday 18th,  9:30-12:00Introductory Lecture Sean Davis, MD, PhD - CCR, NCILink to Talk Slides on SlideShareTuedsay 18th, 12:00-12:30A Perspective from the CCR BioInformatics Core (CCRIFX)Parthav Jailwala - CCRIFX, NCI Lessons learned How to work with the Core "Best Practices" Guides to success Tuesday 18th, 2:00-5:00Hands-on: Open Source Tools Sean Davis, MD, PhD - CCR, NCI  Link to Hands on TutorialWednesday 19th, 9:30-12:30Hands-on:  RNA-Seq Analysis using Partek FlowXiaowen Wang, PhD - Partek Data import Add sample attribute Pre-alignment QA/QC Alignment Post-alignment QA/QC Quantification Differential expression detection Build analysis pipeline Wednesday 19th, 2:00-5:00Hands-on:  RNA-Seq Analysis using GeomatixSusan Dombrowski, PhD -  Genomatix Software, Inc. Introduction to the Genomatix Genome Analyzer (GGA)  Import of data to the GGA  Expression Analysis of RNA-Seq  Visualization of RNA-seq isoforms Pathway Analysis  Defining gene regulatory models of differentially-expressed genes   2014-02-18 09:30:00 Bldg 12A, Room B51, Bethesda, MD In-Person Parthav Jailwala (CCBR),Sean Davis (CU Anschutz),Xiaowen Wang (Partek) BTEP 0 RNA-Seq Data Analysis
792
Description

The Cancer Genome Atlas (TCGA) is a large-scale study that has catalogued genomic data accumulated from more than 20 different types of cancer including mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation.  Being publicly distributed, it has become a major resource for cancer researchers in target discovery and in the biological interpretation and assessment of the clinical impact of genes of interest.  This 2 day workshop will familiarize ...Read More

The Cancer Genome Atlas (TCGA) is a large-scale study that has catalogued genomic data accumulated from more than 20 different types of cancer including mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation.  Being publicly distributed, it has become a major resource for cancer researchers in target discovery and in the biological interpretation and assessment of the clinical impact of genes of interest.  This 2 day workshop will familiarize the audience with the types of data available and analytical tools, including a number of software packages, that enable end-users to easily and effectively mine TCGA data.

Day 1 - Tuesday March 18th 9:30-11:30 am
Introductory Lecture to TCGA Data Analysis
(Maxwell Lee, PhD - CCR NCI)

  1. Introduction
    • A brief history
    • Overview of TCGA data
  2.  Discussion of three TCGA papers
    • Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma.
    • Cancer Cell. 2010 May 18;17(5):510-22. 
       
    • Comprehensive molecular portraits of human breast tumours.
    • Nature. 2012 Oct 4;490(7418):61-70. 
       
    • Discovery and saturation analysis of cancer genes across 21 tumour types.
      Nature. 2014 Jan 23;505(7484):495-501. 
  3. Using TCGA data
  4. Where to download the data?
  5. Some case studies of data analyses

 
Day 1 - Tuesday March 18th 11:30-12:30 pm
cBioPortal Demo
(Anand Merchant, PhD, CCRIFX)
This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets.
As of early 2014 the Portal contains data for 15506 tumor samples from 56 cancer studies. This presentation will include:

  • Introduction to the web application – mission and evolving goals – What is the purpose?
  • Website walk-through – Where is the information and how to query it?
  • Review of the Cancer and Data Types available in the underlying cBio database
  • Advantages and Limitations
  • OncoQueryLanguage (OQL) - Key words and Codes
  • Features and Analytics
  • Viewing and Interpretation of results
  • Example Case  with TCGA dataset (Breast Cancer – 2012 Nature publication)
  • References/Tutorials/FAQ/Pre-set queries
  • Q&A

 
Day 1 - Tuesday March 18th 2:00-5:00 pm
TCGA Data mining using Qlucore (emphasis on expression/methylation)
(Carl-Johan Ivarsson, MSc - Qlucore)
Qlucore Omics Explorer is a user-friendly and interactive software program for data visualization and analysis of any large numerical data set, especially developed for biologists. Through a straightforward user interface built on sliders and check-boxes the users get the possibility to explore and analyze very large data sets.With Qlucore Omics Explorer it is easy to investigate data and evaluate key biological information directly on screen, results are achieved immediately with only a few mouse-clicks. It is possible to work with multiple data sets and the users can introduce as many annotations and clinical parameters as they want – no limits.
In this workshop you will learn how to use Qlucore Omics Explorer to mine TCGA data. Focus will be on working with two data sets and how to find relationships between gene expression and DNA methylation data.

Learning Objectives:

  • Import data and clinical annotations from TCGA
  • Create new hypotheses and new findings using interactive visualization including PCA and heatmaps
  • Learn how to focus the data mining by using interactive selections and statistical filters
  • Work with both gene expression and DNA methylation data in an integrated manner
  • Generate plots and lists for easy publication

Day 2 -Wednesday March 19th 9:30-12:30 pm
BioDiscovery Nexus: TCGA data analysis using Nexus DB (emphasis on Copy Number/mutation)
(Andrea O Hara, PhD, Field Appliction Sceintist, BioDiscovery)
Nexus Copy Number is a platform independent copy number analysis and visualization tool that includes co-visualization of sequence variants. NCI’s site license now includes unlimited access to TCGA Premier, a database of re-processed, curated and reviewed TCGA samples.  The Nexus Copy Number training session will include:

  1. Approaches to optimizing CNV calling from array data.
  2. Downstream analysis of data sets, including:
    • Visualization and statistical approaches for CNV discovery.
    • Stratification by clinical annotation factors or biomarkers.
    • Finding CNVs predictive of survival or other outcome data.
  3. TCGA Premier Data Access:
    • How to access of CNV TCGA data directly from Nexus
    • Query and Integration of TCGA CNV tumor profiles

 
Day 2 - Wednesday March 19th 2:00-5:00 pm
Oncomine: TCGA data analysis (expression, CN, mutation analysis)
(Matthew Anstett, Sr. Market Development Manager)

Oncomine™ Research Edition is a free powerful web application that integrates and unifies high-throughput cancer profiling data so that target expression across a large number of cancer types and experiments can be accessed online, in seconds. Oncomine™ Research Edition includes annual data updates and basic analysis types such as cancer vs. normal, multi-cancer, and co-expression. It features gene and concept summaries, outlier analysis, meta-analysis, and meta-cancer outlier profile analysis (COPA). Oncomine™ Research Premium Edition is a subscription-based software tool for academic researchers that provides additional advanced features and analyses over Oncomine™ Research Edition (www.oncomine.org).

 

This presentation will include the following topics:

 

      Oncomine Research Premium Edition

  • Advanced differential expression analysis
  • Cancer Outlier Profile Analysis (COPA)
  • Signature mapping
  • Import/export of findings

 

      Oncomine Gene Browser

  • Mutation frequencies and gain/loss of function prediction
  • DNA Copy frequencies in cancer
  • Gene expression cancer panel
  • Identifying cell line models

 

Register
Organizer
BTEP
When
Tue, Mar 18 - Wed, Mar 19, 2014 -9:30 am - 5:00 pm
Where
Bldg 12A Room B51 Bethesda MD
The Cancer Genome Atlas (TCGA) is a large-scale study that has catalogued genomic data accumulated from more than 20 different types of cancer including mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation.  Being publicly distributed, it has become a major resource for cancer researchers in target discovery and in the biological interpretation and assessment of the clinical impact of genes of interest.  This 2 day workshop will familiarize the audience with the types of data available and analytical tools, including a number of software packages, that enable end-users to easily and effectively mine TCGA data. Day 1 - Tuesday March 18th 9:30-11:30 amIntroductory Lecture to TCGA Data Analysis(Maxwell Lee, PhD - CCR NCI) Introduction A brief history Overview of TCGA data  Discussion of three TCGA papers Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010 May 18;17(5):510-22.    Comprehensive molecular portraits of human breast tumours. Nature. 2012 Oct 4;490(7418):61-70.    Discovery and saturation analysis of cancer genes across 21 tumour types.Nature. 2014 Jan 23;505(7484):495-501.  Using TCGA data Where to download the data? Some case studies of data analyses  Day 1 - Tuesday March 18th 11:30-12:30 pmcBioPortal Demo(Anand Merchant, PhD, CCRIFX) This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets. As of early 2014 the Portal contains data for 15506 tumor samples from 56 cancer studies. This presentation will include: Introduction to the web application – mission and evolving goals – What is the purpose? Website walk-through – Where is the information and how to query it? Review of the Cancer and Data Types available in the underlying cBio database Advantages and Limitations OncoQueryLanguage (OQL) - Key words and Codes Features and Analytics Viewing and Interpretation of results Example Case  with TCGA dataset (Breast Cancer – 2012 Nature publication) References/Tutorials/FAQ/Pre-set queries Q&A  Day 1 - Tuesday March 18th 2:00-5:00 pmTCGA Data mining using Qlucore (emphasis on expression/methylation)(Carl-Johan Ivarsson, MSc - Qlucore) Qlucore Omics Explorer is a user-friendly and interactive software program for data visualization and analysis of any large numerical data set, especially developed for biologists. Through a straightforward user interface built on sliders and check-boxes the users get the possibility to explore and analyze very large data sets.With Qlucore Omics Explorer it is easy to investigate data and evaluate key biological information directly on screen, results are achieved immediately with only a few mouse-clicks. It is possible to work with multiple data sets and the users can introduce as many annotations and clinical parameters as they want – no limits. In this workshop you will learn how to use Qlucore Omics Explorer to mine TCGA data. Focus will be on working with two data sets and how to find relationships between gene expression and DNA methylation data. Learning Objectives: Import data and clinical annotations from TCGA Create new hypotheses and new findings using interactive visualization including PCA and heatmaps Learn how to focus the data mining by using interactive selections and statistical filters Work with both gene expression and DNA methylation data in an integrated manner Generate plots and lists for easy publication Day 2 -Wednesday March 19th 9:30-12:30 pmBioDiscovery Nexus: TCGA data analysis using Nexus DB (emphasis on Copy Number/mutation) (Andrea O Hara, PhD, Field Appliction Sceintist, BioDiscovery) Nexus Copy Number is a platform independent copy number analysis and visualization tool that includes co-visualization of sequence variants. NCI’s site license now includes unlimited access to TCGA Premier, a database of re-processed, curated and reviewed TCGA samples.  The Nexus Copy Number training session will include: Approaches to optimizing CNV calling from array data. Downstream analysis of data sets, including: Visualization and statistical approaches for CNV discovery. Stratification by clinical annotation factors or biomarkers. Finding CNVs predictive of survival or other outcome data. TCGA Premier Data Access: How to access of CNV TCGA data directly from Nexus Query and Integration of TCGA CNV tumor profiles  Day 2 - Wednesday March 19th 2:00-5:00 pmOncomine: TCGA data analysis (expression, CN, mutation analysis)(Matthew Anstett, Sr. Market Development Manager) Oncomine™ Research Edition is a free powerful web application that integrates and unifies high-throughput cancer profiling data so that target expression across a large number of cancer types and experiments can be accessed online, in seconds. Oncomine™ Research Edition includes annual data updates and basic analysis types such as cancer vs. normal, multi-cancer, and co-expression. It features gene and concept summaries, outlier analysis, meta-analysis, and meta-cancer outlier profile analysis (COPA). Oncomine™ Research Premium Edition is a subscription-based software tool for academic researchers that provides additional advanced features and analyses over Oncomine™ Research Edition (www.oncomine.org).   This presentation will include the following topics:         Oncomine Research Premium Edition Advanced differential expression analysis Cancer Outlier Profile Analysis (COPA) Signature mapping Import/export of findings         Oncomine Gene Browser Mutation frequencies and gain/loss of function prediction DNA Copy frequencies in cancer Gene expression cancer panel Identifying cell line models   2014-03-18 09:30:00 Bldg 12A Room B51 Bethesda MD In-Person Maxwell Lee (CCR NCI) BTEP 0 Workshop on TCGA Data Mining
791
Description

NCI CCR http://bioinformatics.nci.nih.gov/training/ and the NIH Library Bioinformatics
Support Program http://nihlibrary.nih.gov/Bioinformatics are partnering
to sponsor training on the use of NCBI GEO Datasets to analyze gene
expression data.

This 3-hour, mainly hands-on, workshop will show you how to find and analyze
relevant ...Read More

NCI CCR http://bioinformatics.nci.nih.gov/training/ and the NIH Library Bioinformatics
Support Program http://nihlibrary.nih.gov/Bioinformatics are partnering
to sponsor training on the use of NCBI GEO Datasets to analyze gene
expression data.

This 3-hour, mainly hands-on, workshop will show you how to find and analyze
relevant microarray and RNA-Seq datasets in NCBI's Gene Expression Omnibus
resources. After learning about data concepts in GEO, you will use both
precomputed analyses in GEO Profiles and the on-demand GEO2R tool with
non-curated experiments to investigate expression of genes of interest.
 

Register
Organizer
BTEP
When
Fri, Jun 20, 2014 - 1:00 pm - 4:00 pm
Where
NIH Library Training Room, Building 10, Clinical Center, South Entrance
NCI CCR http://bioinformatics.nci.nih.gov/training/ and the NIH Library Bioinformatics Support Program http://nihlibrary.nih.gov/Bioinformatics are partnering to sponsor training on the use of NCBI GEO Datasets to analyze gene expression data. This 3-hour, mainly hands-on, workshop will show you how to find and analyze relevant microarray and RNA-Seq datasets in NCBI's Gene Expression Omnibus resources. After learning about data concepts in GEO, you will use both precomputed analyses in GEO Profiles and the on-demand GEO2R tool with non-curated experiments to investigate expression of genes of interest.   2014-06-20 13:00:00 NIH Library Training Room, Building 10, Clinical Center, South Entrance In-Person Peter Cooper (NCBI) BTEP 0 Using NCBI GEO Datasets to Analyze Gene Expression
790
Description

PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own LapTop Computer) class. In order to provide more flexibility with room scheduling we are experimenting with a new format that involves students brining their own laptop computers to the class. This has the advantage that you can continue exactly where you left-off following the class.  Government issued or personal computers are permitted. We will be able to ...Read More

PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own LapTop Computer) class. In order to provide more flexibility with room scheduling we are experimenting with a new format that involves students brining their own laptop computers to the class. This has the advantage that you can continue exactly where you left-off following the class.  Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

 Direction of FAES Classrooms (B1C204, B1C205) can be found here http://www.faes.org/announcements/directions_faes_classrooms_nih_campus

Day 1 - AM (9:30-12)  Introductory Lecture
(Maggie Cam, PhD - CCR, NCI)

 Introduction

  • Historical Perspective
  • Microarray Technologies, Sample Processing Methods
  • Microarray comparisons to RNA-Seq

Data Analysis

  • Experimental Design
  • QC methods
  • Preprocessing: Normalization and low level analysis algorithms

Statistical Analysis

  • Common statistical models used for analysis of microarray data
  • Examples of blocking
  • Batch effects and removal methods

Visualization and Clustering

  • Volcano Plot
  • Principal Components Analysis
  • Hierarchical Clustering
  • K-means Clustering

Validation and Downstream Analysis

  • Validation methods
  • Gene Ontology Enrichment and Pathway analysis tools
  • Major Software applications
  • Public Repositories of Microarray Data

Bioinformatics Core Presentation  (Manjula Kasoji - CCRIFX)

  • Lessons learned and how to work with the core                               
Day 1 - PM (1-4:30 pm):  Hands-on  Microarray analysis using Partek Genomics Suite
(Xiaowen Wang, PhD - Partek)

Partek Genomics Suite Analysis Workflow

  • Process Cel files (RMA)
  • Looking at data distributions, histograms, bar plots, MA plots, etc.
  • Statistical Analysis (Anova)
  • Create contrasts
  • False Discovery Analysis
  • Making lists of significant genes
  • Venn Diagrams

Work independently on dataset

Day 2  AM (9:30-12):  Hands-on  Partek Genomics Suite Analysis and Partek Pathway 
(Xiaowen Wang, PhD - Partek)
  • Unsupervised Clustering
  • Custom Filtering
  • Pathway ANOVA
  • Work independently on another dataset
Day 2 PM (1-4:30): GeneSet Enrichment Analysis (GSEA)
(Alan Berger, PhD - School of Medicine Johns Hopkins University) 

GSEA is a computational method that determines which (if any) a priori defined sets of genes are   significantly differentially expressed, as an ensemble, between two biological states.  It is an open-source program developed by the Broad Institute:    http://www.broadinstitute.org/gsea/index.jsp

Lecture

  • The general approach of gene set enrichment methods and comparison with DAVID
  • How GSEA measures differential expression for each set of genes
  • Controlling effects of multiple comparisons in GSEA (false discovery rate)
  • The Broad Institute library of groups of gene sets (MSigDB)
  • What files and formats are needed for GSEA
  • User options and running GSEA

Hands-on

  • Loading the GSEA required input files for an example dataset
  • Using and choosing values in the GSEA GUI interface
  • Rank-based analysis
  • Full dataset analysis
  • Understanding the GSEA outputs and judging significance in the results 

Work independently on another dataset

 

 

Register
Organizer
BTEP
When
Mon, Sep 29 - Tue, Sep 30, 2014 -9:30 am - 4:30 pm
Where
In-Person
PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own LapTop Computer) class. In order to provide more flexibility with room scheduling we are experimenting with a new format that involves students brining their own laptop computers to the class. This has the advantage that you can continue exactly where you left-off following the class.  Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.  Direction of FAES Classrooms (B1C204, B1C205) can be found here http://www.faes.org/announcements/directions_faes_classrooms_nih_campus Day 1 - AM (9:30-12)  Introductory Lecture (Maggie Cam, PhD - CCR, NCI)  Introduction Historical Perspective Microarray Technologies, Sample Processing Methods Microarray comparisons to RNA-Seq Data Analysis Experimental Design QC methods Preprocessing: Normalization and low level analysis algorithms Statistical Analysis Common statistical models used for analysis of microarray data Examples of blocking Batch effects and removal methods Visualization and Clustering Volcano Plot Principal Components Analysis Hierarchical Clustering K-means Clustering Validation and Downstream Analysis Validation methods Gene Ontology Enrichment and Pathway analysis tools Major Software applications Public Repositories of Microarray Data Bioinformatics Core Presentation  (Manjula Kasoji - CCRIFX) Lessons learned and how to work with the core                                Day 1 - PM (1-4:30 pm):  Hands-on  Microarray analysis using Partek Genomics Suite (Xiaowen Wang, PhD - Partek) Partek Genomics Suite Analysis Workflow Process Cel files (RMA) Looking at data distributions, histograms, bar plots, MA plots, etc. Statistical Analysis (Anova) Create contrasts False Discovery Analysis Making lists of significant genes Venn Diagrams Work independently on dataset Day 2  AM (9:30-12):  Hands-on  Partek Genomics Suite Analysis and Partek Pathway  (Xiaowen Wang, PhD - Partek) Unsupervised Clustering Custom Filtering Pathway ANOVA Work independently on another dataset Day 2 PM (1-4:30): GeneSet Enrichment Analysis (GSEA) (Alan Berger, PhD - School of Medicine Johns Hopkins University)  GSEA is a computational method that determines which (if any) a priori defined sets of genes are   significantly differentially expressed, as an ensemble, between two biological states.  It is an open-source program developed by the Broad Institute:    http://www.broadinstitute.org/gsea/index.jsp Lecture The general approach of gene set enrichment methods and comparison with DAVID How GSEA measures differential expression for each set of genes Controlling effects of multiple comparisons in GSEA (false discovery rate) The Broad Institute library of groups of gene sets (MSigDB) What files and formats are needed for GSEA User options and running GSEA Hands-on Loading the GSEA required input files for an example dataset Using and choosing values in the GSEA GUI interface Rank-based analysis Full dataset analysis Understanding the GSEA outputs and judging significance in the results  Work independently on another dataset     2014-09-29 09:30:00 In-Person Maggie Cam (NCI CCBR),Xiaowen Wang (Partek) BTEP 0 Microarray Workshop (2 day)
789
Description

PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own Laptop Computer) class. In order to provide more flexibility with room scheduling we are experimenting with a new format that involves students brining their own laptop computers to the class. This has the advantage that you can continue exactly where you left-off following the class.  Government issued or personal computers are permitted. We will be able to supply a very ...Read More

PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own Laptop Computer) class. In order to provide more flexibility with room scheduling we are experimenting with a new format that involves students brining their own laptop computers to the class. This has the advantage that you can continue exactly where you left-off following the class.  Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

Direction of FAES Classrooms (B1C204, B1C205) can be found here http://www.faes.org/announcements/directions_faes_classrooms_nih_campus

This class is now full - you may still register, but will be place on a waiting list for potential openings  Click Here to Register  Day 1 AM (Oct 27) - 9:30-12:30  Introductory Lecture
(Maxwell Lee, PhD - CCR, NCI)
  1. Introduction
    • A brief history
    • Overview of TCGA data
    • TCGA data access policy and download
  2. Discussion of TCGA papers
    • Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma.
      Cancer Cell. 2010 May 18;17(5):510-22.
       
    • Comprehensive molecular portraits of human breast tumours.
      Nature. 2012 Oct 4;490(7418):61-70.
       
    • Discovery and saturation analysis of cancer genes across 21 tumour types.
      Nature. 2014 Jan 23;505(7484):495-501.
       
    • Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.
      Cell. 2014 Aug 14;158(4):929-44. 
Day 1 PM (Oct 27) - 1:30-4:30  UCSC Cancer Browser
(Mary Goldman, PhD - U.C. Santa Cruz)

This workshop will teach users how to use the UCSC Cancer Browser, https://genome-cancer.ucsc.edu/, a web-based tool that integrates relevant data, analysis and visualization, allowing users to easily discover and share their research observations. Users will learn how to explore the relationship between genomic alterations and phenotypes by visualizing various -omic data alongside clinical and phenotypic features, such as age, subtype classifications and genomic biomarkers.

Users will download and upload clinical data, generate Kaplan-Meier plots dynamically as well as generate URL bookmarks of specific views of the data to share with others. The Cancer Genomics Browser currently hosts 575 datasets from genome-wide analyses of over 227,000 samples, including datasets from TCGA, CCLE, Connectivity Map and TARGET.

Day 2 AM (Oct 28) - 9:30-12:30  BioDiscovery Nexus
(Andrea O'Hara, PhD - Field Appliction Sceintist, BioDiscovery)

Nexus Copy Number is a platform independent copy number analysis and visualization tool that includes co-visualization of sequence variants. With an easy to use visual interface, Nexus Copy Number allows for quick review and detailed analysis of population-wide copy number alterations across the entire genome.  NCI¹s site license includes unlimited access to TCGA Premier, a database of re-processed, curated and reviewed TCGA samples.

In this workshop, you will learn how to use Nexus Copy Number software to mine TCGA copy number data.  The training session will focus on access of the TCGA data within the software and a detailed evaluation of one TCGA data set to identify statistically significant changes within the sample population.

Learning Objectives:

  • How to access of CNV TCGA data directly from Nexus.
  • Visualization and statistical approaches for CNV discovery.
  • Sample stratification by clinical annotation factors or biomarkers.
  • Finding CNVs predictive of survival or other outcome data.
  • Generate publication-ready figures and charts during analysis.
  • Query and integration of TCGA CNV tumor profiles with existing copy number data.

Day 2 PM (Oct 28) - 1:30-4:30  CBioPortal
(Nikolaus Schultz, PhD - Memorial Sloan-Kettering Cancer Center and Anand Merchant, MD, PhD. - CCR, NCI)

This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets.
As of early 2014 the Portal contains data for 15506 tumor samples from 56 cancer studies. This presentation will include:

  • Introduction to the web application – mission and evolving goals – What is the purpose?
  • Website walk-through – Where is the information and how to query it?
  • Review of the Cancer and Data Types available in the underlying cBio database
  • Advantages and Limitations
  • OncoQueryLanguage (OQL) - Key words and Codes
  • Features and Analytics
  • Viewing and Interpretation of results
  • Example Case  with TCGA dataset (Breast Cancer – 2012 Nature publication)
  • References/Tutorials/FAQ/Pre-set queries
  • Q&A
Register
Organizer
BTEP
When
Mon, Oct 27 - Tue, Oct 28, 2014 -9:30 am - 4:30 pm
Where
Bldg 10 - FAES Classroom 1 (B1C204)
PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own Laptop Computer) class. In order to provide more flexibility with room scheduling we are experimenting with a new format that involves students brining their own laptop computers to the class. This has the advantage that you can continue exactly where you left-off following the class.  Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Direction of FAES Classrooms (B1C204, B1C205) can be found here http://www.faes.org/announcements/directions_faes_classrooms_nih_campus This class is now full - you may still register, but will be place on a waiting list for potential openings  Click Here to Register  Day 1 AM (Oct 27) - 9:30-12:30  Introductory Lecture (Maxwell Lee, PhD - CCR, NCI) Introduction A brief history Overview of TCGA data TCGA data access policy and download Discussion of TCGA papers Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010 May 18;17(5):510-22.   Comprehensive molecular portraits of human breast tumours. Nature. 2012 Oct 4;490(7418):61-70.   Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014 Jan 23;505(7484):495-501.   Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014 Aug 14;158(4):929-44.  Day 1 PM (Oct 27) - 1:30-4:30  UCSC Cancer Browser (Mary Goldman, PhD - U.C. Santa Cruz) This workshop will teach users how to use the UCSC Cancer Browser, https://genome-cancer.ucsc.edu/, a web-based tool that integrates relevant data, analysis and visualization, allowing users to easily discover and share their research observations. Users will learn how to explore the relationship between genomic alterations and phenotypes by visualizing various -omic data alongside clinical and phenotypic features, such as age, subtype classifications and genomic biomarkers. Users will download and upload clinical data, generate Kaplan-Meier plots dynamically as well as generate URL bookmarks of specific views of the data to share with others. The Cancer Genomics Browser currently hosts 575 datasets from genome-wide analyses of over 227,000 samples, including datasets from TCGA, CCLE, Connectivity Map and TARGET. Day 2 AM (Oct 28) - 9:30-12:30  BioDiscovery Nexus (Andrea O'Hara, PhD - Field Appliction Sceintist, BioDiscovery) Nexus Copy Number is a platform independent copy number analysis and visualization tool that includes co-visualization of sequence variants. With an easy to use visual interface, Nexus Copy Number allows for quick review and detailed analysis of population-wide copy number alterations across the entire genome.  NCI¹s site license includes unlimited access to TCGA Premier, a database of re-processed, curated and reviewed TCGA samples. In this workshop, you will learn how to use Nexus Copy Number software to mine TCGA copy number data.  The training session will focus on access of the TCGA data within the software and a detailed evaluation of one TCGA data set to identify statistically significant changes within the sample population. Learning Objectives: How to access of CNV TCGA data directly from Nexus. Visualization and statistical approaches for CNV discovery. Sample stratification by clinical annotation factors or biomarkers. Finding CNVs predictive of survival or other outcome data. Generate publication-ready figures and charts during analysis. Query and integration of TCGA CNV tumor profiles with existing copy number data. Day 2 PM (Oct 28) - 1:30-4:30  CBioPortal (Nikolaus Schultz, PhD - Memorial Sloan-Kettering Cancer Center and Anand Merchant, MD, PhD. - CCR, NCI) This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets. As of early 2014 the Portal contains data for 15506 tumor samples from 56 cancer studies. This presentation will include: Introduction to the web application – mission and evolving goals – What is the purpose? Website walk-through – Where is the information and how to query it? Review of the Cancer and Data Types available in the underlying cBio database Advantages and Limitations OncoQueryLanguage (OQL) - Key words and Codes Features and Analytics Viewing and Interpretation of results Example Case  with TCGA dataset (Breast Cancer – 2012 Nature publication) References/Tutorials/FAQ/Pre-set queries Q&A 2014-10-27 09:30:00 Bldg 10 - FAES Classroom 1 (B1C204) In-Person Maxwell Lee (CCR NCI) BTEP 0 TCGA Data Analysis Workshop (2 day)
787
Description

Day 1 - AM (9:30-12:30)  Introductory Lecture
(Peter FitzGerald, PhD - CCR, NCI)

  • Read More

Day 1 - AM (9:30-12:30)  Introductory Lecture
(Peter FitzGerald, PhD - CCR, NCI)

  • Introduction
    • Historical Perspective and Technical Variations
    • Experimental methodology
    • Comparison to ChIP-Chip
  • Data Analysis
    • Experimental Design
    • Quality Control 
    • Peak Calling (Different methodologies)
    • Major Sources of Error
    • Causes of Fail Experiments
    • Validation Methods
  • Sequence Specific Binding
    • Identification of Motifs
    • Overexpressed sequences
    • Pathways
  • Resources
    • Public Repositories
    • Literature References
    • Software listings

Day 1 - PM  (1:30-4:30)  Introduction to Genomatix
The Genomatix Mining Stations (GMS) and the Genomatix Genome Analyzer (GGA) at the NCI
(Susan Dombrowski, PhD - Genomatix)

  • The basics of these tools
  • Importing data and mapping of NGS data on the GMS

Day 2 - AM (9:30-12:30)  Genomatix Continued
(Susan Dombrowski, PhD - Genomatix)

  • Import of data to the GGA
  • Automated, Complete Workflow for ChIP-Seq Analysis
    • Peak Finding
    • Read and Peak Classification
    • Sequence Extraction
    • TFBS overrepresenation
    • Definition of new TFBS
  • Downstream Application Areas
    • Position Correlation with ENCODE ChIP-Seq data
    • Annotation of binding regions: target prediction
    • Pathway analysis of potential TF targets
Day 2 - PM (1:30-4:30)  ChIP-Seq data analysis and integration using Cistrome
(Chongzhi/George Zang, PhD - Dana-Farber Cancer Institute, Harvard School of Public Health)  

Cistrome (cistrome.org) is a web-based platform for ChIP-chip and ChIP-seq data analysis and integration. “Cistrome” refers to the in vivo genome-wide location of a transcription factor or a histone modification, which can be characterized using ChIP-chip or ChIP-seq. In this training session, I will introduce the basic functions of Cistrome analysis pipeline and the recently launched Cistrome dataset browser, which has collected over 12,000 public ChIP-seq datasets. Then I will give a practical example to analyze a ChIP-seq dataset using a series of tools on Cistrome. The practice will include: 

 

  • ChIP-seq peak calling using MACS
  • ChIP-seq integrative analyses
  • ChIP-seq and gene expression data integration using BETA
  • Investigate public ChIP-seq data using Cistrome Dataset Browser 

UCSC Demo lnks

USCS-with data hub

Helix-with data hub

Register
Organizer
BTEP
When
Tue, Nov 18 - Wed, Nov 19, 2014 -9:30 am - 4:30 pm
Where
FAES Classroom 4
Day 1 - AM (9:30-12:30)  Introductory Lecture(Peter FitzGerald, PhD - CCR, NCI) Introduction Historical Perspective and Technical Variations Experimental methodology Comparison to ChIP-Chip Data Analysis Experimental Design Quality Control  Peak Calling (Different methodologies) Major Sources of Error Causes of Fail Experiments Validation Methods Sequence Specific Binding Identification of Motifs Overexpressed sequences Pathways Resources Public Repositories Literature References Software listings Day 1 - PM  (1:30-4:30)  Introduction to Genomatix The Genomatix Mining Stations (GMS) and the Genomatix Genome Analyzer (GGA) at the NCI(Susan Dombrowski, PhD - Genomatix) The basics of these tools Importing data and mapping of NGS data on the GMS Day 2 - AM (9:30-12:30)  Genomatix Continued(Susan Dombrowski, PhD - Genomatix) Import of data to the GGA Automated, Complete Workflow for ChIP-Seq Analysis Peak Finding Read and Peak Classification Sequence Extraction TFBS overrepresenation Definition of new TFBS Downstream Application Areas Position Correlation with ENCODE ChIP-Seq data Annotation of binding regions: target prediction Pathway analysis of potential TF targets Day 2 - PM (1:30-4:30)  ChIP-Seq data analysis and integration using Cistrome (Chongzhi/George Zang, PhD - Dana-Farber Cancer Institute, Harvard School of Public Health)   Cistrome (cistrome.org) is a web-based platform for ChIP-chip and ChIP-seq data analysis and integration. “Cistrome” refers to the in vivo genome-wide location of a transcription factor or a histone modification, which can be characterized using ChIP-chip or ChIP-seq. In this training session, I will introduce the basic functions of Cistrome analysis pipeline and the recently launched Cistrome dataset browser, which has collected over 12,000 public ChIP-seq datasets. Then I will give a practical example to analyze a ChIP-seq dataset using a series of tools on Cistrome. The practice will include:    ChIP-seq peak calling using MACS ChIP-seq integrative analyses ChIP-seq and gene expression data integration using BETA Investigate public ChIP-seq data using Cistrome Dataset Browser  UCSC Demo lnks USCS-with data hub Helix-with data hub 2014-11-18 09:30:00 FAES Classroom 4 In-Person Peter FitzGerald (GAU) BTEP 0 ChIP-Seq Data Analysis Workshop (2-day)
788
Description

Day 1 - AM (9:30 AM – 12:30 PM)  Ingenuity IPA -  Basic Training 
(Kate Wendelsdorf, Ph.D. - Ingenuity Pathway Analysis)
 
Ingenuity IPA® is the industry leading software solution to model, analyze, and understand complex biological and chemical systems foundational to human health and disease. Quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >10,000 peer-reviewed articles.
Getting Started

    Read More

Day 1 - AM (9:30 AM – 12:30 PM)  Ingenuity IPA -  Basic Training 
(Kate Wendelsdorf, Ph.D. - Ingenuity Pathway Analysis)
 
Ingenuity IPA® is the industry leading software solution to model, analyze, and understand complex biological and chemical systems foundational to human health and disease. Quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >10,000 peer-reviewed articles.
Getting Started

  • Fundamentals of IPA
  • Overview of key features
  • Search & Pathway Building
  • Advanced Search
  • Building & editing pathways
  • Using Build & Overlay tools

Day 1 - PM - (1:30 PM – 4:30 PM)  Ingenuity IPA -  Data Analysis
(Kate Wendelsdorf, Ph.D. - Ingenuity Pathway Analysis)
Dataset Analysis 

  • Data Upload & Analysis
  • Interpretation of Gene, Transcript, Protein & Metabolite Data
  • Pathway Analysis & Canonical Pathways
  • Downstream Effects &vInterpreting the Heat Map
  • Upstream Regulators & Regulator Effects Analysis
  • Interpreting networks
  • Comparison & multiple observations analysis

Day 2 - AM (9:30 AM – 12:30 PM) MetaCore - Introductory Topics
(Matthew Wampole, Ph.D. - Thomson Reuters Metacore)
 
MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. MetaCore™ is based on a proprietary manually-curated database of human protein-protein, protein-DNA and protein-compound interactions, metabolic and signaling pathways for human, mouse and rat, supported by proprietary ontologies and controlled vocabulary. The analytical package includes easy-to-use, intuitive tools for searching and data visualization, enabling the identification of the most relevant biological pathways, networks, and processes in our “virtual lab.”

  • General Overview: Thomson Reuters Systems Biology Solutions    
  • Knowledge Mining: Explore the database and exporting
  • Uploading, filtering and setting a background·  
  • Running Functional Enrichments and exploring Pathway Maps
  • Running Workflows

Day 2 - AM (1:30 AM – 4:30 PM) MetaCore - Advanced Topics
(Matthew Wampole, Ph.D. - Thomson Reuters Metacore)

  • Interactome Analysis: Finding key hubs in your data
  • Microarray Repository: Using and comparing public datasets
  • Network Building: When to use each algorithm
Register
Organizer
BTEP
When
Wed, Dec 17, 2014 - 9:30 pm - 4:30 pm
Where
In-Person
Day 1 - AM (9:30 AM – 12:30 PM)  Ingenuity IPA -  Basic Training  (Kate Wendelsdorf, Ph.D. - Ingenuity Pathway Analysis)   Ingenuity IPA® is the industry leading software solution to model, analyze, and understand complex biological and chemical systems foundational to human health and disease. Quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >10,000 peer-reviewed articles. Getting Started Fundamentals of IPA Overview of key features Search & Pathway Building Advanced Search Building & editing pathways Using Build & Overlay tools Day 1 - PM - (1:30 PM – 4:30 PM)  Ingenuity IPA -  Data Analysis (Kate Wendelsdorf, Ph.D. - Ingenuity Pathway Analysis) Dataset Analysis  Data Upload & Analysis Interpretation of Gene, Transcript, Protein & Metabolite Data Pathway Analysis & Canonical Pathways Downstream Effects &vInterpreting the Heat Map Upstream Regulators & Regulator Effects Analysis Interpreting networks Comparison & multiple observations analysis Day 2 - AM (9:30 AM – 12:30 PM) MetaCore - Introductory Topics (Matthew Wampole, Ph.D. - Thomson Reuters Metacore)  MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. MetaCore™ is based on a proprietary manually-curated database of human protein-protein, protein-DNA and protein-compound interactions, metabolic and signaling pathways for human, mouse and rat, supported by proprietary ontologies and controlled vocabulary. The analytical package includes easy-to-use, intuitive tools for searching and data visualization, enabling the identification of the most relevant biological pathways, networks, and processes in our “virtual lab.” General Overview: Thomson Reuters Systems Biology Solutions     Knowledge Mining: Explore the database and exporting Uploading, filtering and setting a background·   Running Functional Enrichments and exploring Pathway Maps Running Workflows Day 2 - AM (1:30 AM – 4:30 PM) MetaCore - Advanced Topics (Matthew Wampole, Ph.D. - Thomson Reuters Metacore) Interactome Analysis: Finding key hubs in your data Microarray Repository: Using and comparing public datasets Network Building: When to use each algorithm 2014-12-17 21:30:00 In-Person BTEP 0 Pathway Analysis Workshop (2-day)
786
Description
/* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1, h2, h3, h4, h5, h6 { font-family: Helvetica, Arial, sans-serif; margin: 1.2em 0em 0.6em 0em; font-weight: bold; } h1 { font-size: 250%; font-weight: normal; color: #87b13f; line-height: 1.1em; } h2 { font-size: 160%; font-weight: normal; line-height: 1.4em; border-bottom: 1px #1a81c2 solid; } h3 { font-size: 130%; } ...Read More
/* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1, h2, h3, h4, h5, h6 { font-family: Helvetica, Arial, sans-serif; margin: 1.2em 0em 0.6em 0em; font-weight: bold; } h1 { font-size: 250%; font-weight: normal; color: #87b13f; line-height: 1.1em; } h2 { font-size: 160%; font-weight: normal; line-height: 1.4em; border-bottom: 1px #1a81c2 solid; } h3 { font-size: 130%; } h2, h3 { color: #1a81c2; } h4, h5, h6 { font-size:115%; } /* not expecting to dive deeper than four levels on a single page */ /* links are simply blue, hovering slightly less blue */ a { color: #1a81c2; } a:active { outline: none; } a:visited { color: #1a81c2; } a:hover { color: #4c94c2; } pre, img { max-width: 100%; display: block; } pre { border: 0px none; background-color: #F8F8F8; white-space: pre; overflow-x: auto; } pre code { border: 1px #aaa dashed; background-color: white; display: block; padding: 1em; color: #111; overflow-x: inherit; } /* markdown v1 */ pre code[class] { background-color: inherit; } /* markdown v2 */ pre[class] code { background-color: inherit; } /* formatting of inline code */ code { color: #87b13f; font-size: 92%; } /* formatting of tables */ table, td, th { border: none; padding: 0 0.5em; } /* alternating row colors */ tbody tr:nth-child(odd) td { background-color: #F8F8F8; } blockquote { # color:#666666; color:#ff0000; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; font-size:13pt; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { font-size:12pt; max-width:100%; } a, a:visited { text-decoration: underline; } hr { visibility: hidden; page-break-before: always; } pre, blockquote { padding-right: 1em; page-break-inside: avoid; } tr, img { page-break-inside: avoid; } img { max-width: 100% !important; } @page :left { margin: 15mm 20mm 15mm 10mm; } @page :right { margin: 15mm 10mm 15mm 20mm; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } } code{white-space: pre;} pre:not([class]) { background-color: white; } .main-container { max-width: 940px; margin-left: auto; margin-right: auto; } code { color: inherit; background-color: rgba(0, 0, 0, 0.04); } img { max-width:100%; height: auto; } A Short Course in R for Biologists

"A Short Course in R for Biologists" is a two-day course given in four three-hour sessions entitled: Introduction to R, Introduction to Bioconductor, Introduction to Microarray Analysis, and Introduction to NGS Data Analysis.

Day Morning Session, 9:30 AM-12:30 PM Afternoon Session, 1:30 PM-4:30 PM Jan 29 Introduction to R Introduction to Bioconductor Jan 30 Introduction to Microarray Analysis Introduction to NGS Data Analysis

Registration Required

Web-based resources for this class: (See Below for PDF versions)

The course will include frequent, short hands-on periods so students should bring their own laptops with a working installation of R, version 3.1 or later. In addition, several R packages will be used which must be installed prior to the course.

R is a console application. Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier. If you are comfortable running programs, viewing output, and editing files at the terminal, you will not need RStudio in order to take the course. However, RStudio offers quite an array of functions that you may still find useful and it is well worth a look.

R Installation

The R program and instructions for its installation under Linux, Mac OSX, and Windows can be found here:

http://cran.r-project.org/

Bioconductor and Bioconductor Package Installation

Complete instructions for the installation of the basic and additional Bioconductor packages are found here:

http://www.bioconductor.org/install/

In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class:

Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene affy simpleaffy arrayQualityMetrics limma survival ggplot2 hthgu133acdf hthgu133a.db gplots

Briefly, the following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above:

# First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene","affy","simpleaffy","arrayQualityMetrics","limma","survival","ggplot2","hthgu133acdf","hthgu133a.db","gplots")) RStudio Installation

Install the "€œDesktop, Open Source Edition"€:

http://www.rstudio.com/products/RStudio/#Desk

Class Outline Day 1 (Jan 29), Morning Session: Introduction to R
  • The R environment
    • Starting an R Session, Setting Options
    • Listing Variables, Editing Commands, Using the R History
    • Getting Help on an R Function
    • Logging a Session to a File
    • Running External R Code
    • Installing and Loading Packages
    • Ending a Session, Saving Your Work
  • The Elements of R
    • Numeric
    • Character
    • Logical
    • Missing Values
  • R Data Structures
    • Vectors
    • Matrices
    • Lists
    • Data.Frames
    • Factors
    • Functions
    • Other Complex Structures
  • Procedures
    • Reading and Writing Data
    • Exploring and Summarizing Data
    • Dealing with Missing Data
    • Restructuring Data
    • Relabeling Data
    • Subsetting Data
    • Operating on Rows or Columns of Data
    • Saving R Objects for Later Use
    • Graphing Data
    • Simple Statistical Tests
    • Example: A Simple Analysis of Probe Intensity Data
  • Project: Creating a Graphical Function in 4 Easy Steps
    • Step 1: Create an X-Y Plot to Compare Two Arrays
    • Step 2: Package the X-Y Plot as a Function
    • Step 3: Create a Median Array as a Better Standard for Comparison
    • Step 4: Rotate and Scale the Plot-€“Voila, You Have Created a MAPlot!
Day 1 (Jan 29), Afternoon Session: Introduction to Bioconductor
  • Installing Bioconductor
  • An Overview of Bioconductor Packages
  • Fundamental Packages
    • Biobase: the Foundation
    • Biostrings: A Representation of Biological Sequences
    • BSgenome: A Representation of Complete Genomic Sequences
    • GenomicRanges: Manipulation of Genomic Intervals
    • GenomicFeatures: Manipulation of Genomic Features
    • GenomicAlgnments: Manipulation of Short Genomic Alignments
  • Two Fundamental Structures to Contain Experiment Data
    • The ExpressionSet for Array Data
      • Constructing an ExpressionSet
      • Analyzing an ExpressionSet
    • The SummarizedExperiment for NGS Sequence Data
      • Constructing a SummarizedExperiment
      • Analyzing a SummarizedExperiment
Day 2 (Jan 30), Morning Session: Introduction to Microarray Analysis

The objective of this session is to initiate students in the analysis of microarrays using R and Bioconductor. To better help students take advantage of the microarray services offered by the Laboratory of Molecular Technology at NCI-Frederick, the focus of the course will be on the analysis of data from Affymetrix chips. It is assumed that the student has some knowledge of microarray workflows.

  • Downloading Data from The Cancer Genome Atlas Databases
  • Preliminary Steps: Array Pre-Processing
    • Checking the Quality of Arrays
    • Performing Array Normalization
  • Identifying Differentially Expressed Genes
  • Data Visualization
    • Performing Principal Component Analysis (PCA)
    • Computing and Interpreting Heatmaps
    • Computing and Interpreting Kaplan Meir Curves
Day 2 (Jan 30), Afternoon Session: Introduction to NGS Data Analysis Details to be announced
Register
Organizer
BTEP
When
Thu, Jan 29 - Fri, Jan 30, 2015 -9:30 am - 4:30 pm
Where
FAES Classroom 4
/* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1, h2, h3, h4, h5, h6 { font-family: Helvetica, Arial, sans-serif; margin: 1.2em 0em 0.6em 0em; font-weight: bold; } h1 { font-size: 250%; font-weight: normal; color: #87b13f; line-height: 1.1em; } h2 { font-size: 160%; font-weight: normal; line-height: 1.4em; border-bottom: 1px #1a81c2 solid; } h3 { font-size: 130%; } h2, h3 { color: #1a81c2; } h4, h5, h6 { font-size:115%; } /* not expecting to dive deeper than four levels on a single page */ /* links are simply blue, hovering slightly less blue */ a { color: #1a81c2; } a:active { outline: none; } a:visited { color: #1a81c2; } a:hover { color: #4c94c2; } pre, img { max-width: 100%; display: block; } pre { border: 0px none; background-color: #F8F8F8; white-space: pre; overflow-x: auto; } pre code { border: 1px #aaa dashed; background-color: white; display: block; padding: 1em; color: #111; overflow-x: inherit; } /* markdown v1 */ pre code[class] { background-color: inherit; } /* markdown v2 */ pre[class] code { background-color: inherit; } /* formatting of inline code */ code { color: #87b13f; font-size: 92%; } /* formatting of tables */ table, td, th { border: none; padding: 0 0.5em; } /* alternating row colors */ tbody tr:nth-child(odd) td { background-color: #F8F8F8; } blockquote { # color:#666666; color:#ff0000; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; font-size:13pt; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { font-size:12pt; max-width:100%; } a, a:visited { text-decoration: underline; } hr { visibility: hidden; page-break-before: always; } pre, blockquote { padding-right: 1em; page-break-inside: avoid; } tr, img { page-break-inside: avoid; } img { max-width: 100% !important; } @page :left { margin: 15mm 20mm 15mm 10mm; } @page :right { margin: 15mm 10mm 15mm 20mm; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } } code{white-space: pre;} pre:not([class]) { background-color: white; } .main-container { max-width: 940px; margin-left: auto; margin-right: auto; } code { color: inherit; background-color: rgba(0, 0, 0, 0.04); } img { max-width:100%; height: auto; } A Short Course in R for Biologists "A Short Course in R for Biologists" is a two-day course given in four three-hour sessions entitled: Introduction to R, Introduction to Bioconductor, Introduction to Microarray Analysis, and Introduction to NGS Data Analysis. Day Morning Session, 9:30 AM-12:30 PM Afternoon Session, 1:30 PM-4:30 PM Jan 29 Introduction to R Introduction to Bioconductor Jan 30 Introduction to Microarray Analysis Introduction to NGS Data Analysis Registration Required Web-based resources for this class: (See Below for PDF versions) Introduction to R for Biologists (David Wheeler) Introduction to Bioconductor (David Wheeler) Introduction to R (Sean Davis) Vignettes (Sean Davis) Data Files (Fathi Elloumi) R script (Fathi Elloumi) The course will include frequent, short hands-on periods so students should bring their own laptops with a working installation of R, version 3.1 or later. In addition, several R packages will be used which must be installed prior to the course. R is a console application. Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier. If you are comfortable running programs, viewing output, and editing files at the terminal, you will not need RStudio in order to take the course. However, RStudio offers quite an array of functions that you may still find useful and it is well worth a look. R Installation The R program and instructions for its installation under Linux, Mac OSX, and Windows can be found here: http://cran.r-project.org/ Bioconductor and Bioconductor Package Installation Complete instructions for the installation of the basic and additional Bioconductor packages are found here: http://www.bioconductor.org/install/ In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class: Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene affy simpleaffy arrayQualityMetrics limma survival ggplot2 hthgu133acdf hthgu133a.db gplots Briefly, the following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above: # First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene","affy","simpleaffy","arrayQualityMetrics","limma","survival","ggplot2","hthgu133acdf","hthgu133a.db","gplots")) RStudio Installation Install the "€œDesktop, Open Source Edition"€: http://www.rstudio.com/products/RStudio/#Desk Class Outline Day 1 (Jan 29), Morning Session: Introduction to R The R environment Starting an R Session, Setting Options Listing Variables, Editing Commands, Using the R History Getting Help on an R Function Logging a Session to a File Running External R Code Installing and Loading Packages Ending a Session, Saving Your Work The Elements of R Numeric Character Logical Missing Values R Data Structures Vectors Matrices Lists Data.Frames Factors Functions Other Complex Structures Procedures Reading and Writing Data Exploring and Summarizing Data Dealing with Missing Data Restructuring Data Relabeling Data Subsetting Data Operating on Rows or Columns of Data Saving R Objects for Later Use Graphing Data Simple Statistical Tests Example: A Simple Analysis of Probe Intensity Data Project: Creating a Graphical Function in 4 Easy Steps Step 1: Create an X-Y Plot to Compare Two Arrays Step 2: Package the X-Y Plot as a Function Step 3: Create a Median Array as a Better Standard for Comparison Step 4: Rotate and Scale the Plot-€“Voila, You Have Created a MAPlot! Day 1 (Jan 29), Afternoon Session: Introduction to Bioconductor Installing Bioconductor An Overview of Bioconductor Packages Fundamental Packages Biobase: the Foundation Biostrings: A Representation of Biological Sequences BSgenome: A Representation of Complete Genomic Sequences GenomicRanges: Manipulation of Genomic Intervals GenomicFeatures: Manipulation of Genomic Features GenomicAlgnments: Manipulation of Short Genomic Alignments Two Fundamental Structures to Contain Experiment Data The ExpressionSet for Array Data Constructing an ExpressionSet Analyzing an ExpressionSet The SummarizedExperiment for NGS Sequence Data Constructing a SummarizedExperiment Analyzing a SummarizedExperiment Day 2 (Jan 30), Morning Session: Introduction to Microarray Analysis The objective of this session is to initiate students in the analysis of microarrays using R and Bioconductor. To better help students take advantage of the microarray services offered by the Laboratory of Molecular Technology at NCI-Frederick, the focus of the course will be on the analysis of data from Affymetrix chips. It is assumed that the student has some knowledge of microarray workflows. Downloading Data from The Cancer Genome Atlas Databases Preliminary Steps: Array Pre-Processing Checking the Quality of Arrays Performing Array Normalization Identifying Differentially Expressed Genes Data Visualization Performing Principal Component Analysis (PCA) Computing and Interpreting Heatmaps Computing and Interpreting Kaplan Meir Curves Day 2 (Jan 30), Afternoon Session: Introduction to NGS Data Analysis Details to be announced 2015-01-29 09:30:00 FAES Classroom 4 In-Person David Wheeler PhD. (Laboratory of Biochemistry and Molecular Biology CCR NCI),Sean Davis (CU Anschutz) BTEP 0 R/Bioconductor Basics Workshop (2-day)
785
Description

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek, Genomatix) and open source software.

Day 1 -  9:30-12:30
Read More

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek, Genomatix) and open source software.

Day 1 -  9:30-12:30
Introductory Lecture 
Sean Davis, MD, PhD - CCR, NCI

Day 1 -  1:30-4:30
RNA-Seq Analysis using Partek Flow

Xiaowen Wang, PhD - Partek

Hands-on RNA-seq training on Partek Flow. It starts from importing raw sequence data in fastq format, perform QA/QC, alignment, quantification, differential expression detection and biological interpretation in Partek Flow. 

Day 2 - 9:30-12:30
Read count data analysis using Partek Genomic Suite
Xiaowen Wang, PhD - Partek

This class is showing downstream RNA-seq data analysis using Partek Genomic Suite.

It will start with normalized read count data generated from Partek Flow to do expression data analysis in PGS. Different format of data importer will be illustrated, followed by standard gene expression analysis workflow including QA/QC, differential expression detection and biological interpretation using Partek Pathway will be demonstrated.

Objectives:

Students will learn how to use basic features of Partek Flow and Partek Genomics Suite to:

·       Flow

o   Import data
o   Perform QA/AC
o   Alignment
o   Gene/transcript abundance estimate
o   Differential expression detection
o   Go Enrichment
o   Visualization (PCA, dotplot, vocano plot, chromosome view, hierarchical clustering etc.)

·       PGS

o   Import Partek Flow project and text file format
o   Perform QA/QC of imported data
o   Detect differential expression
o   Pathway analysis
o   Visualization (PCA, dot plot, heatmap etc.)

Day 2 - 1:30-4:30
RNA-Seq Analysis using Geomatix
Susan Dombrowski, PhD -  Genomatix Software, Inc.

  • Introduction to the Genomatix Genome Analyzer (GGA)
  • Import of data to the GGA
  • Automated workflow: Expression Analysis of RNA-Seq Data
  • Pathway and Literature-based Analyses of differentially-expressed genes
  • Visualization of RNA-seq data in the Genomatix Genome Browser and
  • Transcriptome Viewer

 

 

Register
Organizer
BTEP
When
Thu, Feb 19, 2015 - 9:30 am - 4:30 am
Where
FAES Classroom 4
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek, Genomatix) and open source software. Day 1 -  9:30-12:30Introductory Lecture Sean Davis, MD, PhD - CCR, NCI Day 1 -  1:30-4:30RNA-Seq Analysis using Partek Flow Xiaowen Wang, PhD - Partek Hands-on RNA-seq training on Partek Flow. It starts from importing raw sequence data in fastq format, perform QA/QC, alignment, quantification, differential expression detection and biological interpretation in Partek Flow.  Day 2 - 9:30-12:30Read count data analysis using Partek Genomic SuiteXiaowen Wang, PhD - Partek This class is showing downstream RNA-seq data analysis using Partek Genomic Suite. It will start with normalized read count data generated from Partek Flow to do expression data analysis in PGS. Different format of data importer will be illustrated, followed by standard gene expression analysis workflow including QA/QC, differential expression detection and biological interpretation using Partek Pathway will be demonstrated. Objectives: Students will learn how to use basic features of Partek Flow and Partek Genomics Suite to: ·       Flow o   Import data o   Perform QA/AC o   Alignment o   Gene/transcript abundance estimate o   Differential expression detection o   Go Enrichment o   Visualization (PCA, dotplot, vocano plot, chromosome view, hierarchical clustering etc.) ·       PGS o   Import Partek Flow project and text file format o   Perform QA/QC of imported data o   Detect differential expression o   Pathway analysis o   Visualization (PCA, dot plot, heatmap etc.) Day 2 - 1:30-4:30RNA-Seq Analysis using GeomatixSusan Dombrowski, PhD -  Genomatix Software, Inc. Introduction to the Genomatix Genome Analyzer (GGA) Import of data to the GGA Automated workflow: Expression Analysis of RNA-Seq Data Pathway and Literature-based Analyses of differentially-expressed genes Visualization of RNA-seq data in the Genomatix Genome Browser and Transcriptome Viewer     2015-02-19 09:30:00 FAES Classroom 4 In-Person Sean Davis (CU Anschutz),Xiaowen Wang (Partek) BTEP 0 RNA-Seq Data Analysis Workshop (2-day)
784
Description

This workshop will cover basics of exome-seq analysis including downstream interpretation of variants using a variety of open-source and commercial webtools (Golden Helix, IGV, Ingenuity Variant Analysis, GeneGrid (Genomatix), MuPit/Cravat).

Day 1 - AM (9:30-12:30) Introductory Lectures
(Chunhua Yan, PhD - CBIIT)

  • Next generation sequencing ...Read More

This workshop will cover basics of exome-seq analysis including downstream interpretation of variants using a variety of open-source and commercial webtools (Golden Helix, IGV, Ingenuity Variant Analysis, GeneGrid (Genomatix), MuPit/Cravat).

Day 1 - AM (9:30-12:30) Introductory Lectures
(Chunhua Yan, PhD - CBIIT)

  • Next generation sequencing technology
  • Exome sequencing (Cost, Speed, Gene coverage, Biological implication)
  • Experimental design (Sample size, Coverage, Sample submission)
  • Mutation Calling (Dream challenge, Genome in Bottle)

(Chih-Hao Hsu, PhD - CBIIT)

  • VCF
  • Visualization
  • Mutation call software overview and algorithms
  • Databases (1000 genomes, ClinVar, cBio, …)

(Li Jia, MSc - CCBR)

  • Lessons learned from experimental design
  • Best practices in CCBR workflow (includes the discussion on the benchmark, GATK and others used in the tech dev)
  • Annovar annotation and filtering
  • How to collaborate with CCBR – guide to success

Day 1 - PM  (1:30-4:30) 

Golden Helix
(Bryce Christensen PhD - Golden Helix)

Cancer gene panel analysis Whole exome Tumor/normal analysis Whole exome trio analysis Whole exome extended family analysis with PhoRank Population based NGS workflows including collapsing methods   Integrative Genomics Viewer (IGV) (Online Tutorial: self-guided)   Click here to view the Tutorial    (needs VPN, for NIH only) The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.   Visualizing variant (VCF) and alignment (BAM) files using IGV    

Day 2 - AM  (9:30-12:30)
GeneGrid
(Susan Dombrowski, PhD - Genomatix)

Genomic variants like SNPs or small InDels are of major interest to biologists and clinicians alike. Identification of the relevant variants within a genome is crucial for the understanding of molecular mechanisms and diagnostics of rare or common diseases.   GeneGrid enables you to reduce the millions of variants generated by today's NGS experiments to the few or even the single relevant one(s) with a few clicks and generate a detailed report of the findings. Variants of interest and their associated alignment files can be visualized in the context of Genomatix' curated genomic data content, and literature and pathway analysis of variants of interest can also be performed within the same application.  In this session, a publicly-available cancer exome-seq dataset (normal/tumor) will be used as a case-study to showcase the features and functionality of GeneGrid for use in clinical studies.   Ingenuity Variant Analysis (Sohela Shah, PhD - Ingenuity)  Ingenuity Variant Analysis combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based both upon published biological evidence and your own knowledge of disease biology. With Variant Analysis, you can interrogate your variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up.   QIAGEN Ingenuity Variant Analysis training will include uploading, annotating, and searching samples, and setting up, reviewing, and exportinganalyses. We will review the different filter settings, particularly focusing on the genetic analysis and statistical association.  

Day 2 - PM  (1:30-4:30) CRAVAT/MuPIT - Analysis of Genomic Variants
(Michael Ryan  - Johns Hopkins University)

CRAVAT (www.cravat.us) is a free tool for high-throughput analysis of sequencing variants.  CRAVAT is funded by NCI’s Informatics Technology for Cancer Research program.  CRAVAT accepts very large variant data files and returns a wide variety of annotations and scores that help with identification of important variants.  CRAVAT is a cancer focused analysis package tailored to the needs of cancer studies.  The workshop will provide some background on CRAVAT and lots of hands-on exercises to learn how to use the tool and interpret the results.

MuPIT (mupit.icm.jhu) is a sister tool to CRAVAT that shows mutations on 3D protein structures.  Clusters of mutations in 3D space are not always apparent from the position of mutations on a protein sequence.  For proteins with solved structures, MuPIT can show the position of mutations from your study along with a variety of structural annotations (e.g. the position of a DNA binding site).  MuPIT also includes a pre-built database of TCGA mutations so an investigator’s mutations can be viewed in the context of mutations and mutation clusters from other cancer studies.  The focus of the workshop will be a series of exercises to learn how to visualize your mutations in MuPIT, how CRAVAT and MuPIT are integrated, and how to manipulate, investigate, and understand the results.

Register
Organizer
BTEP
When
Wed, Mar 18, 2015 - 9:30 pm - 4:30 pm
Where
In-Person
This workshop will cover basics of exome-seq analysis including downstream interpretation of variants using a variety of open-source and commercial webtools (Golden Helix, IGV, Ingenuity Variant Analysis, GeneGrid (Genomatix), MuPit/Cravat). Day 1 - AM (9:30-12:30) Introductory Lectures(Chunhua Yan, PhD - CBIIT) Next generation sequencing technology Exome sequencing (Cost, Speed, Gene coverage, Biological implication) Experimental design (Sample size, Coverage, Sample submission) Mutation Calling (Dream challenge, Genome in Bottle) (Chih-Hao Hsu, PhD - CBIIT) VCF Visualization Mutation call software overview and algorithms Databases (1000 genomes, ClinVar, cBio, …) (Li Jia, MSc - CCBR) Lessons learned from experimental design Best practices in CCBR workflow (includes the discussion on the benchmark, GATK and others used in the tech dev) Annovar annotation and filtering How to collaborate with CCBR – guide to success Day 1 - PM  (1:30-4:30)  Golden Helix(Bryce Christensen PhD - Golden Helix) Cancer gene panel analysis Whole exome Tumor/normal analysis Whole exome trio analysis Whole exome extended family analysis with PhoRank Population based NGS workflows including collapsing methods   Integrative Genomics Viewer (IGV) (Online Tutorial: self-guided)   Click here to view the Tutorial    (needs VPN, for NIH only) The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.   Visualizing variant (VCF) and alignment (BAM) files using IGV     Day 2 - AM  (9:30-12:30)GeneGrid (Susan Dombrowski, PhD - Genomatix) Genomic variants like SNPs or small InDels are of major interest to biologists and clinicians alike. Identification of the relevant variants within a genome is crucial for the understanding of molecular mechanisms and diagnostics of rare or common diseases.   GeneGrid enables you to reduce the millions of variants generated by today's NGS experiments to the few or even the single relevant one(s) with a few clicks and generate a detailed report of the findings. Variants of interest and their associated alignment files can be visualized in the context of Genomatix' curated genomic data content, and literature and pathway analysis of variants of interest can also be performed within the same application.  In this session, a publicly-available cancer exome-seq dataset (normal/tumor) will be used as a case-study to showcase the features and functionality of GeneGrid for use in clinical studies.   Ingenuity Variant Analysis (Sohela Shah, PhD - Ingenuity)  Ingenuity Variant Analysis combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based both upon published biological evidence and your own knowledge of disease biology. With Variant Analysis, you can interrogate your variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up.   QIAGEN Ingenuity Variant Analysis training will include uploading, annotating, and searching samples, and setting up, reviewing, and exportinganalyses. We will review the different filter settings, particularly focusing on the genetic analysis and statistical association.   Day 2 - PM  (1:30-4:30) CRAVAT/MuPIT - Analysis of Genomic Variants(Michael Ryan  - Johns Hopkins University) CRAVAT (www.cravat.us) is a free tool for high-throughput analysis of sequencing variants.  CRAVAT is funded by NCI’s Informatics Technology for Cancer Research program.  CRAVAT accepts very large variant data files and returns a wide variety of annotations and scores that help with identification of important variants.  CRAVAT is a cancer focused analysis package tailored to the needs of cancer studies.  The workshop will provide some background on CRAVAT and lots of hands-on exercises to learn how to use the tool and interpret the results. MuPIT (mupit.icm.jhu) is a sister tool to CRAVAT that shows mutations on 3D protein structures.  Clusters of mutations in 3D space are not always apparent from the position of mutations on a protein sequence.  For proteins with solved structures, MuPIT can show the position of mutations from your study along with a variety of structural annotations (e.g. the position of a DNA binding site).  MuPIT also includes a pre-built database of TCGA mutations so an investigator’s mutations can be viewed in the context of mutations and mutation clusters from other cancer studies.  The focus of the workshop will be a series of exercises to learn how to visualize your mutations in MuPIT, how CRAVAT and MuPIT are integrated, and how to manipulate, investigate, and understand the results. 2015-03-18 21:30:00 In-Person Sohela Shah (Ingenuity) BTEP 0 Exome-Seq Data Analysis Workshop (2-day)
783
Description

This workshop will cover some basic concepts involved in the integration of different types of NGS data in order to obtain a better overall picture of the underlying biology. Specifically, the course will examine the integration of micro RNA and mRNA expression data as well as methylation, mutation and copy number alteration as they relate to mRNA expression.  Topics covered in the lecture components will be complemented by hands-on sessions with software from Partek, ...Read More

This workshop will cover some basic concepts involved in the integration of different types of NGS data in order to obtain a better overall picture of the underlying biology. Specifically, the course will examine the integration of micro RNA and mRNA expression data as well as methylation, mutation and copy number alteration as they relate to mRNA expression.  Topics covered in the lecture components will be complemented by hands-on sessions with software from Partek, cBioPortal and Qlucore.

PLEASE NOTE: This 1 day workshop is  a BYOC (Bring your own laptop Computer) class.  Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

AM 9:30-11:00 - Introductory Lecture

(Anand Merchant MD, PhD - CCBR)

  • Concepts 
  • Data types
  • Modalities
  • Challenges
  • Example Workflows
  • Hands-on exercise (Partek/IPA)
    • Genomics Data Integration (mRNA/miRNA)

 

AM 11:00-12:00 - NGS Data Integration (cBioPortal)

(Parthav Jailwala, MSc- CCBR)

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing and analyzing multidimensional cancer genomics data that is curated from large scale cancer genomic data sets. Users can visualize patterns of gene alterations across samples in a cancer study, compare gene alteration frequencies across multiple cancer studies, or summarize all relevant genomic alterations in an individual tumor sample. Genomic data types integrated by cBioPortal include somatic mutations, DNA copy-number alterations (CNAs), mRNA and microRNA (miRNA) expression and DNA methylation.    In this session, followed by an introductory overview of data integration in cBioPortal, we will carry out a brief hands-on exercise of querying and visualizing different data-types in TCGA related to a few key genes in human GBM.  
  • Introductory Lecture
  • Hands on with FireBrowse and cBioPortal
  PM 1:30-4:30 Qlucore Omics Explorer - Basic Training & Data Integration

(Carl Johan Ivarsson)

  • Introduction and Live demonstration ~ 30 min
    • Introduction and terminology
    • Visualize data using PCA
    • Identify discriminating variables using basic statistical tests
    • Export variable lists and images
    • Presentation of data in different plot types
    • Integrate data sets
  • Basic Hands-on Training including Data Integration methylation/mRNA ~ 2 hours
    • Visualize (PCA, color according to annotation)
    • Identify discriminating variables (t-test)
    • Create Variable Lists
    • Present the results in different plots (heat map and box plot)
    • Data Integration – mRNA/methylation

 Direction of FAES Classrooms (B1C207) can be found here http://www.faes.org/announcements/directions_faes_classrooms_nih_campus

Register
Organizer
BTEP
When
Tue, Jun 02, 2015 - 9:30 am - 4:30 pm
Where
Bldg 10: FAES Classroom 3 (B1C207)
This workshop will cover some basic concepts involved in the integration of different types of NGS data in order to obtain a better overall picture of the underlying biology. Specifically, the course will examine the integration of micro RNA and mRNA expression data as well as methylation, mutation and copy number alteration as they relate to mRNA expression.  Topics covered in the lecture components will be complemented by hands-on sessions with software from Partek, cBioPortal and Qlucore. PLEASE NOTE: This 1 day workshop is  a BYOC (Bring your own laptop Computer) class.  Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. AM 9:30-11:00 - Introductory Lecture(Anand Merchant MD, PhD - CCBR) Concepts  Data types Modalities Challenges Example Workflows Hands-on exercise (Partek/IPA) Genomics Data Integration (mRNA/miRNA)   AM 11:00-12:00 - NGS Data Integration (cBioPortal) (Parthav Jailwala, MSc- CCBR) The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing and analyzing multidimensional cancer genomics data that is curated from large scale cancer genomic data sets. Users can visualize patterns of gene alterations across samples in a cancer study, compare gene alteration frequencies across multiple cancer studies, or summarize all relevant genomic alterations in an individual tumor sample. Genomic data types integrated by cBioPortal include somatic mutations, DNA copy-number alterations (CNAs), mRNA and microRNA (miRNA) expression and DNA methylation.    In this session, followed by an introductory overview of data integration in cBioPortal, we will carry out a brief hands-on exercise of querying and visualizing different data-types in TCGA related to a few key genes in human GBM.   Introductory Lecture Hands on with FireBrowse and cBioPortal   PM 1:30-4:30 Qlucore Omics Explorer - Basic Training & Data Integration (Carl Johan Ivarsson) Introduction and Live demonstration ~ 30 min Introduction and terminology Visualize data using PCA Identify discriminating variables using basic statistical tests Export variable lists and images Presentation of data in different plot types Integrate data sets Basic Hands-on Training including Data Integration methylation/mRNA ~ 2 hours Visualize (PCA, color according to annotation) Identify discriminating variables (t-test) Create Variable Lists Present the results in different plots (heat map and box plot) Data Integration – mRNA/methylation  Direction of FAES Classrooms (B1C207) can be found here http://www.faes.org/announcements/directions_faes_classrooms_nih_campus 2015-06-02 09:30:00 Bldg 10: FAES Classroom 3 (B1C207) In-Person Parthav Jailwala (CCBR) BTEP 0 Data Integration Workshop
782
Description

Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Open Source Tools. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways.

PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own LapTop Computer) class. ...Read More

Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Open Source Tools. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways.

PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own LapTop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

 Direction of FAES Classroom 7 (B1C206)    can be found here: http://www.faes.org/announcements/directions_faes_classrooms_nih_campus

Day 1 - AM (9:30-11:30)  Introductory Lecture
(Maggie Cam, PhD - CCR, NCI)

 Introduction

  • Historical Perspective
  • Microarray Technologies, Sample Processing Methods
  • Microarray comparisons to RNA-Seq

Data Analysis

  • Experimental Design
  • QC methods
  • Preprocessing: Normalization and low level analysis algorithms

Statistical Analysis

  • Common statistical models used for analysis of microarray data
  • Examples of blocking
  • Batch effects and removal methods

Visualization and Clustering

  • Volcano Plot
  • Principal Components Analysis
  • Hierarchical Clustering
  • K-means Clustering

Validation and Downstream Analysis

  • Validation methods
  • Gene Ontology Enrichment and Pathway analysis tools
  • Major Software applications
  • Public Repositories of Microarray Data

 

Day 1 - PM (2:00-4:30 pm):  Hands-on  Gene Expression Data Analysis in Partek Genomics Suite
(Xiaowen Wang, PhD - Partek)

Attendees will learn how to use basic features of Partek Genomics Suite for the analysis on Gene Expression Data. An Affymetrix Gene Expression Data will be used to conduct Gene Expression workflow:

  • Import data
  • Perform QA/QC of imported data
  • Exploratory data analysis
  • Detect differential expression (ANOVA)
  • Gene list creation
Day 2 - AM (9:30-11:30):  Hands-on  Gene Expression Data Analysis in Partek Genomics Suite - Continued 
(Xiaowen Wang, PhD - Partek)
  • Biological interpretation
  • Visualization (PCA, histogram, box plot, dot plot, volcano plot, interaction plot heatmap etc.)
Day 2 - PM (1:30-2:30): GEO2R
(Parthav Jailwala, MSc- CCBR, NCI)

GEO2R is an interactive web tool that allows users to compare two or more groups of samples in a GEO Series in order to identify genes that are differentially expressed across experimental conditions. GEO2R performs comparisons on original submitter-supplied processed data tables using the GEOquery and limma R packages from the Bioconductor project. Bioconductor is an open source software project based on the R programming language that provides tools for the analysis of high-throughput genomic data. The GEOquery R package parses GEO data into R data structures that can be used by other R packages. The limma (Linear Models for Microarray Analysis) R package has emerged as one of the most widely used statistical tests for identifying differentially expressed genes. It handles a wide range of experimental designs and data types and applies multiple-testing corrections on P-values to help correct for the occurrence of false positives. Thus, GEO2R provides a simple interface that allows users to perform R statistical analysis without command line expertise.

Lecture

  • Background on GEO datasets
  • What is GEO2R and how can it help you
  • How to use GEO2R
  • Options and features
  • Limitations and caveats
  • Hands-on exercise
Day 2 - PM (2:30-3:30): DAVID
(David/Dawei Huang, M.D. - LMB, CCR, NCI)

The Database for Annotation, Visualization and Integrated Discovery (DAVID ) provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes.

Lecture

Day 2 - PM (3:30-4:30): GeneSet Enrichment Analysis (GSEA) (Maggie Cam, PhD - CCR, NCI)

GSEA is a computational method that determines which (if any) a priori defined sets of genes are   significantly differentially expressed, as an ensemble, between two biological states.  It is an open-source program developed by the Broad Institute:    http://www.broadinstitute.org/gsea/index.jsp

Lecture

  • The general approach of gene set enrichment methods and comparison with DAVID
  • How GSEA measures differential expression for each set of genes
  • Controlling effects of multiple comparisons in GSEA (false discovery rate)
  • The Broad Institute library of groups of gene sets (MSigDB)
  • What files and formats are needed for GSEA
  • User options and running GSEA

Hands-on

  • Loading the GSEA required input files for an example dataset
  • Using and choosing values in the GSEA GUI interface
  • Rank-based analysis
  • Full dataset analysis
  • Understanding the GSEA outputs and judging significance in the results 
Register
Organizer
BTEP
When
Tue, Sep 22 - Wed, Sep 23, 2015 -9:30 am - 4:30 pm
Where
Bldg10: FAES Classroom 7 ( B1C206)
Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Open Source Tools. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using tools geneset analyses and pathways. PLEASE NOTE: This 2 day workshop is  a BYOC (Bring your own LapTop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.  Direction of FAES Classroom 7 (B1C206)    can be found here: http://www.faes.org/announcements/directions_faes_classrooms_nih_campus Day 1 - AM (9:30-11:30)  Introductory Lecture (Maggie Cam, PhD - CCR, NCI)  Introduction Historical Perspective Microarray Technologies, Sample Processing Methods Microarray comparisons to RNA-Seq Data Analysis Experimental Design QC methods Preprocessing: Normalization and low level analysis algorithms Statistical Analysis Common statistical models used for analysis of microarray data Examples of blocking Batch effects and removal methods Visualization and Clustering Volcano Plot Principal Components Analysis Hierarchical Clustering K-means Clustering Validation and Downstream Analysis Validation methods Gene Ontology Enrichment and Pathway analysis tools Major Software applications Public Repositories of Microarray Data   Day 1 - PM (2:00-4:30 pm):  Hands-on  Gene Expression Data Analysis in Partek Genomics Suite (Xiaowen Wang, PhD - Partek) Attendees will learn how to use basic features of Partek Genomics Suite for the analysis on Gene Expression Data. An Affymetrix Gene Expression Data will be used to conduct Gene Expression workflow: Import data Perform QA/QC of imported data Exploratory data analysis Detect differential expression (ANOVA) Gene list creation Day 2 - AM (9:30-11:30):  Hands-on  Gene Expression Data Analysis in Partek Genomics Suite - Continued  (Xiaowen Wang, PhD - Partek) Biological interpretation Visualization (PCA, histogram, box plot, dot plot, volcano plot, interaction plot heatmap etc.) Day 2 - PM (1:30-2:30): GEO2R (Parthav Jailwala, MSc- CCBR, NCI) GEO2R is an interactive web tool that allows users to compare two or more groups of samples in a GEO Series in order to identify genes that are differentially expressed across experimental conditions. GEO2R performs comparisons on original submitter-supplied processed data tables using the GEOquery and limma R packages from the Bioconductor project. Bioconductor is an open source software project based on the R programming language that provides tools for the analysis of high-throughput genomic data. The GEOquery R package parses GEO data into R data structures that can be used by other R packages. The limma (Linear Models for Microarray Analysis) R package has emerged as one of the most widely used statistical tests for identifying differentially expressed genes. It handles a wide range of experimental designs and data types and applies multiple-testing corrections on P-values to help correct for the occurrence of false positives. Thus, GEO2R provides a simple interface that allows users to perform R statistical analysis without command line expertise. Lecture Background on GEO datasets What is GEO2R and how can it help you How to use GEO2R Options and features Limitations and caveats Hands-on exercise Day 2 - PM (2:30-3:30): DAVID (David/Dawei Huang, M.D. - LMB, CCR, NCI) The Database for Annotation, Visualization and Integrated Discovery (DAVID ) provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes. Lecture Brief principle of DAVID gene enrichment analysis Term-centric analysis of a large gene list Gene-centric analysis of a large gene list Pathway map view of a large gene list Nature Protocols 4:44 (http://www.nature.com/nprot/journal/v4/n1/abs/nprot.2008.211.html)   Day 2 - PM (3:30-4:30): GeneSet Enrichment Analysis (GSEA) (Maggie Cam, PhD - CCR, NCI) GSEA is a computational method that determines which (if any) a priori defined sets of genes are   significantly differentially expressed, as an ensemble, between two biological states.  It is an open-source program developed by the Broad Institute:    http://www.broadinstitute.org/gsea/index.jsp Lecture The general approach of gene set enrichment methods and comparison with DAVID How GSEA measures differential expression for each set of genes Controlling effects of multiple comparisons in GSEA (false discovery rate) The Broad Institute library of groups of gene sets (MSigDB) What files and formats are needed for GSEA User options and running GSEA Hands-on Loading the GSEA required input files for an example dataset Using and choosing values in the GSEA GUI interface Rank-based analysis Full dataset analysis Understanding the GSEA outputs and judging significance in the results  2015-09-22 09:30:00 Bldg10: FAES Classroom 7 ( B1C206) In-Person Maggie Cam (NCI CCBR),Parthav Jailwala (CCBR),Xiaowen Wang (Partek) BTEP 0 Microarray Workshop (2 day)
781
Description

Ingenuity IPA® is the industry leading software solution to model, analyze, and understand complex biological and chemical systems foundational to human health and disease. Quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >10,000 peer-reviewed articles.

PLEASE NOTE: This 1 day workshop is  a BYOC (Bring your own laptop Computer) class.  Government issued or personal computers are permitted. ...Read More

Ingenuity IPA® is the industry leading software solution to model, analyze, and understand complex biological and chemical systems foundational to human health and disease. Quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >10,000 peer-reviewed articles.

PLEASE NOTE: This 1 day workshop is  a BYOC (Bring your own laptop Computer) class.  Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

Morning Session (9:30 AM – 12:30 PM)  Ingenuity IPA -  Basic Training 

(Dev Mistry, Ph.D. - Ingenuity Pathway Analysis)

Getting Started

  • Fundamentals of IPA
  • Overview of key features
  • Search & Pathway Building
  • Advanced Search
  • Building & editing pathways
  • Using Build & Overlay tools

Afternoon Session - (1:30 PM – 4:30 PM)  Ingenuity IPA -  Data Analysis

(Dev Mistry, Ph.D. - Ingenuity Pathway Analysis)

Dataset Analysis 

  • Data Upload & Analysis
  • Interpretation of Gene, Transcript, Protein & Metabolite Data
  • Pathway Analysis & Canonical Pathways
  • Downstream Effects &vInterpreting the Heat Map
  • Upstream Regulators & Regulator Effects Analysis
  • Interpreting networks
  • Comparison & multiple observations analysis

     

Register
Organizer
BTEP
When
Tue, Oct 13, 2015 - 9:30 am - 4:30 pm
Where
FAES Room 2 – B1C209
Ingenuity IPA® is the industry leading software solution to model, analyze, and understand complex biological and chemical systems foundational to human health and disease. Quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >10,000 peer-reviewed articles. PLEASE NOTE: This 1 day workshop is  a BYOC (Bring your own laptop Computer) class.  Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Morning Session (9:30 AM – 12:30 PM)  Ingenuity IPA -  Basic Training  (Dev Mistry, Ph.D. - Ingenuity Pathway Analysis) Getting Started Fundamentals of IPA Overview of key features Search & Pathway Building Advanced Search Building & editing pathways Using Build & Overlay tools Afternoon Session - (1:30 PM – 4:30 PM)  Ingenuity IPA -  Data Analysis (Dev Mistry, Ph.D. - Ingenuity Pathway Analysis) Dataset Analysis  Data Upload & Analysis Interpretation of Gene, Transcript, Protein & Metabolite Data Pathway Analysis & Canonical Pathways Downstream Effects &vInterpreting the Heat Map Upstream Regulators & Regulator Effects Analysis Interpreting networks Comparison & multiple observations analysis   2015-10-13 09:30:00 FAES Room 2 – B1C209 In-Person Devendra Mistry (QIAGEN) BTEP 0 Pathway Analysis using Ingenuity IPA
780
Description
/* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1, h2, h3, h4, h5, h6 { font-family: Helvetica, Arial, sans-serif; margin: 1.2em 0em 0.6em 0em; font-weight: bold; } h1 { font-size: 250%; font-weight: normal; color: #87b13f; line-height: 1.1em; } h2 { font-size: 160%; font-weight: normal; line-height: 1.4em; border-bottom: 1px #1a81c2 solid; } h3 { font-size: 130%; } ...Read More
/* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1, h2, h3, h4, h5, h6 { font-family: Helvetica, Arial, sans-serif; margin: 1.2em 0em 0.6em 0em; font-weight: bold; } h1 { font-size: 250%; font-weight: normal; color: #87b13f; line-height: 1.1em; } h2 { font-size: 160%; font-weight: normal; line-height: 1.4em; border-bottom: 1px #1a81c2 solid; } h3 { font-size: 130%; } h2, h3 { color: #1a81c2; } h4, h5, h6 { font-size:115%; } /* not expecting to dive deeper than four levels on a single page */ /* links are simply blue, hovering slightly less blue */ a { color: #1a81c2; } a:active { outline: none; } a:visited { color: #1a81c2; } a:hover { color: #4c94c2; } pre, img { max-width: 100%; display: block; } pre { border: 0px none; background-color: #F8F8F8; white-space: pre; overflow-x: auto; } pre code { border: 1px #aaa dashed; background-color: white; display: block; padding: 1em; color: #111; overflow-x: inherit; } /* markdown v1 */ pre code[class] { background-color: inherit; } /* markdown v2 */ pre[class] code { background-color: inherit; } /* formatting of inline code */ code { color: #87b13f; font-size: 92%; } /* formatting of tables */ table, td, th { border: none; padding: 0 0.5em; } /* alternating row colors */ tbody tr:nth-child(odd) td { background-color: #F8F8F8; } blockquote { # color:#666666; color:#ff0000; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; font-size:13pt; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { font-size:12pt; max-width:100%; } a, a:visited { text-decoration: underline; } hr { visibility: hidden; page-break-before: always; } pre, blockquote { padding-right: 1em; page-break-inside: avoid; } tr, img { page-break-inside: avoid; } img { max-width: 100% !important; } @page :left { margin: 15mm 20mm 15mm 10mm; } @page :right { margin: 15mm 10mm 15mm 20mm; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } } code{white-space: pre;} pre:not([class]) { background-color: white; } .main-container { max-width: 940px; margin-left: auto; margin-right: auto; } code { color: inherit; background-color: rgba(0, 0, 0, 0.04); } img { max-width:100%; height: auto; } A Short Course in R for Biologists

"A Short Course in R for Biologists" is a two-day course given in four three-hour sessions entitled: Introduction to R, Introduction to Bioconductor, Introduction to Microarray Analysis, and Introduction to NGS Data Analysis.

Day Morning Session, 9:30 AM-12:30 PM Afternoon Session, 1:30 PM-4:30 PM Oct 22 Introduction to R Introduction to Bioconductor Oct 23 Introduction to Microarray Analysis Introduction to NGS Data Analysis

Registration Required

PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

Web-based resources for this class: (See Below for PDF versions)

The course will include frequent, short hands-on periods so students should bring their own laptops with a working installation of R, version 3.1 or later. In addition, several R packages will be used which must be installed prior to the course.

R is a console application. Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier. If you are comfortable running programs, viewing output, and editing files at the terminal, you will not need RStudio in order to take the course. However, RStudio offers quite an array of functions that you may still find useful and it is well worth a look.

R Installation

The R program and instructions for its installation under Linux, Mac OSX, and Windows can be found here:

http://cran.r-project.org/

Bioconductor and Bioconductor Package Installation

Complete instructions for the installation of the basic and additional Bioconductor packages are found here:

http://www.bioconductor.org/install/

In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class:

Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene affy simpleaffy arrayQualityMetrics limma survival ggplot2 hthgu133acdf hthgu133a.db gplots

Briefly, the following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above:

# First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene","affy","simpleaffy","arrayQualityMetrics","limma","survival","ggplot2","hthgu133acdf","hthgu133a.db","gplots")) RStudio Installation

Install the "€œDesktop, Open Source Edition"€:

http://www.rstudio.com/products/RStudio/#Desk

Class Outline Day 1 (Oct 22), Morning Session: Introduction to R
  • The R environment
    • Starting an R Session, Setting Options
    • Listing Variables, Editing Commands, Using the R History
    • Getting Help on an R Function
    • Logging a Session to a File
    • Running External R Code
    • Installing and Loading Packages
    • Ending a Session, Saving Your Work
  • The Elements of R
    • Numeric
    • Character
    • Logical
    • Missing Values
  • R Data Structures
    • Vectors
    • Matrices
    • Lists
    • Data.Frames
    • Factors
    • Functions
    • Other Complex Structures
  • Procedures
    • Reading and Writing Data
    • Exploring and Summarizing Data
    • Dealing with Missing Data
    • Restructuring Data
    • Relabeling Data
    • Subsetting Data
    • Operating on Rows or Columns of Data
    • Saving R Objects for Later Use
    • Graphing Data
    • Simple Statistical Tests
    • Example: A Simple Analysis of Probe Intensity Data
  • Project: Creating a Graphical Function in 4 Easy Steps
    • Step 1: Create an X-Y Plot to Compare Two Arrays
    • Step 2: Package the X-Y Plot as a Function
    • Step 3: Create a Median Array as a Better Standard for Comparison
    • Step 4: Rotate and Scale the Plot-€“Voila, You Have Created a MAPlot!
Day 1 (Oct 22), Afternoon Session: Introduction to Bioconductor
  • Installing Bioconductor
  • An Overview of Bioconductor Packages
  • Fundamental Packages
    • Biobase: the Foundation
    • Biostrings: A Representation of Biological Sequences
    • BSgenome: A Representation of Complete Genomic Sequences
    • GenomicRanges: Manipulation of Genomic Intervals
    • GenomicFeatures: Manipulation of Genomic Features
    • GenomicAlgnments: Manipulation of Short Genomic Alignments
  • Two Fundamental Structures to Contain Experiment Data
    • The ExpressionSet for Array Data
      • Constructing an ExpressionSet
      • Analyzing an ExpressionSet
    • The SummarizedExperiment for NGS Sequence Data
      • Constructing a SummarizedExperiment
      • Analyzing a SummarizedExperiment
Day 2 (Oct 23), Morning Session: Introduction to Microarray Analysis

The objective of this session is to initiate students in the analysis of microarrays using R and Bioconductor. To better help students take advantage of the microarray services offered by the Laboratory of Molecular Technology at NCI-Frederick, the focus of the course will be on the analysis of data from Affymetrix chips. It is assumed that the student has some knowledge of microarray workflows.

  • Downloading Data from The Cancer Genome Atlas Databases
  • Preliminary Steps: Array Pre-Processing
    • Checking the Quality of Arrays
    • Performing Array Normalization
  • Identifying Differentially Expressed Genes
  • Data Visualization
    • Performing Principal Component Analysis (PCA)
    • Computing and Interpreting Heatmaps
    • Computing and Interpreting Kaplan Meir Curves
Day 2 (Oct 23), Afternoon Session: Introduction to NGS Data Analysis Details to be announced
Register
Organizer
BTEP
When
Thu, Oct 22 - Fri, Oct 23, 2015 -9:30 am - 4:30 pm
Where
FAES Room 3 – B1C207
/* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1, h2, h3, h4, h5, h6 { font-family: Helvetica, Arial, sans-serif; margin: 1.2em 0em 0.6em 0em; font-weight: bold; } h1 { font-size: 250%; font-weight: normal; color: #87b13f; line-height: 1.1em; } h2 { font-size: 160%; font-weight: normal; line-height: 1.4em; border-bottom: 1px #1a81c2 solid; } h3 { font-size: 130%; } h2, h3 { color: #1a81c2; } h4, h5, h6 { font-size:115%; } /* not expecting to dive deeper than four levels on a single page */ /* links are simply blue, hovering slightly less blue */ a { color: #1a81c2; } a:active { outline: none; } a:visited { color: #1a81c2; } a:hover { color: #4c94c2; } pre, img { max-width: 100%; display: block; } pre { border: 0px none; background-color: #F8F8F8; white-space: pre; overflow-x: auto; } pre code { border: 1px #aaa dashed; background-color: white; display: block; padding: 1em; color: #111; overflow-x: inherit; } /* markdown v1 */ pre code[class] { background-color: inherit; } /* markdown v2 */ pre[class] code { background-color: inherit; } /* formatting of inline code */ code { color: #87b13f; font-size: 92%; } /* formatting of tables */ table, td, th { border: none; padding: 0 0.5em; } /* alternating row colors */ tbody tr:nth-child(odd) td { background-color: #F8F8F8; } blockquote { # color:#666666; color:#ff0000; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; font-size:13pt; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { font-size:12pt; max-width:100%; } a, a:visited { text-decoration: underline; } hr { visibility: hidden; page-break-before: always; } pre, blockquote { padding-right: 1em; page-break-inside: avoid; } tr, img { page-break-inside: avoid; } img { max-width: 100% !important; } @page :left { margin: 15mm 20mm 15mm 10mm; } @page :right { margin: 15mm 10mm 15mm 20mm; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } } code{white-space: pre;} pre:not([class]) { background-color: white; } .main-container { max-width: 940px; margin-left: auto; margin-right: auto; } code { color: inherit; background-color: rgba(0, 0, 0, 0.04); } img { max-width:100%; height: auto; } A Short Course in R for Biologists "A Short Course in R for Biologists" is a two-day course given in four three-hour sessions entitled: Introduction to R, Introduction to Bioconductor, Introduction to Microarray Analysis, and Introduction to NGS Data Analysis. Day Morning Session, 9:30 AM-12:30 PM Afternoon Session, 1:30 PM-4:30 PM Oct 22 Introduction to R Introduction to Bioconductor Oct 23 Introduction to Microarray Analysis Introduction to NGS Data Analysis Registration Required PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Web-based resources for this class: (See Below for PDF versions) Introduction to R for Biologists (David Wheeler) Introduction to Bioconductor (David Wheeler) Introduction to R (Sean Davis) Vignettes (Sean Davis) Data Files (Fathi Elloumi) R script (Fathi Elloumi) The course will include frequent, short hands-on periods so students should bring their own laptops with a working installation of R, version 3.1 or later. In addition, several R packages will be used which must be installed prior to the course. R is a console application. Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier. If you are comfortable running programs, viewing output, and editing files at the terminal, you will not need RStudio in order to take the course. However, RStudio offers quite an array of functions that you may still find useful and it is well worth a look. R Installation The R program and instructions for its installation under Linux, Mac OSX, and Windows can be found here: http://cran.r-project.org/ Bioconductor and Bioconductor Package Installation Complete instructions for the installation of the basic and additional Bioconductor packages are found here: http://www.bioconductor.org/install/ In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class: Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene affy simpleaffy arrayQualityMetrics limma survival ggplot2 hthgu133acdf hthgu133a.db gplots Briefly, the following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above: # First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene","affy","simpleaffy","arrayQualityMetrics","limma","survival","ggplot2","hthgu133acdf","hthgu133a.db","gplots")) RStudio Installation Install the "€œDesktop, Open Source Edition"€: http://www.rstudio.com/products/RStudio/#Desk Class Outline Day 1 (Oct 22), Morning Session: Introduction to R The R environment Starting an R Session, Setting Options Listing Variables, Editing Commands, Using the R History Getting Help on an R Function Logging a Session to a File Running External R Code Installing and Loading Packages Ending a Session, Saving Your Work The Elements of R Numeric Character Logical Missing Values R Data Structures Vectors Matrices Lists Data.Frames Factors Functions Other Complex Structures Procedures Reading and Writing Data Exploring and Summarizing Data Dealing with Missing Data Restructuring Data Relabeling Data Subsetting Data Operating on Rows or Columns of Data Saving R Objects for Later Use Graphing Data Simple Statistical Tests Example: A Simple Analysis of Probe Intensity Data Project: Creating a Graphical Function in 4 Easy Steps Step 1: Create an X-Y Plot to Compare Two Arrays Step 2: Package the X-Y Plot as a Function Step 3: Create a Median Array as a Better Standard for Comparison Step 4: Rotate and Scale the Plot-€“Voila, You Have Created a MAPlot! Day 1 (Oct 22), Afternoon Session: Introduction to Bioconductor Installing Bioconductor An Overview of Bioconductor Packages Fundamental Packages Biobase: the Foundation Biostrings: A Representation of Biological Sequences BSgenome: A Representation of Complete Genomic Sequences GenomicRanges: Manipulation of Genomic Intervals GenomicFeatures: Manipulation of Genomic Features GenomicAlgnments: Manipulation of Short Genomic Alignments Two Fundamental Structures to Contain Experiment Data The ExpressionSet for Array Data Constructing an ExpressionSet Analyzing an ExpressionSet The SummarizedExperiment for NGS Sequence Data Constructing a SummarizedExperiment Analyzing a SummarizedExperiment Day 2 (Oct 23), Morning Session: Introduction to Microarray Analysis The objective of this session is to initiate students in the analysis of microarrays using R and Bioconductor. To better help students take advantage of the microarray services offered by the Laboratory of Molecular Technology at NCI-Frederick, the focus of the course will be on the analysis of data from Affymetrix chips. It is assumed that the student has some knowledge of microarray workflows. Downloading Data from The Cancer Genome Atlas Databases Preliminary Steps: Array Pre-Processing Checking the Quality of Arrays Performing Array Normalization Identifying Differentially Expressed Genes Data Visualization Performing Principal Component Analysis (PCA) Computing and Interpreting Heatmaps Computing and Interpreting Kaplan Meir Curves Day 2 (Oct 23), Afternoon Session: Introduction to NGS Data Analysis Details to be announced 2015-10-22 09:30:00 FAES Room 3 – B1C207 In-Person David Wheeler PhD. (Laboratory of Biochemistry and Molecular Biology CCR NCI),Sean Davis (CU Anschutz) BTEP 0 R/Bioconductor Basics Workshop (2-day)
779
Description
A Short Course in R for Biologists

"A Short Course in R for Biologists" is a two-day course given in four three-hour sessions entitled: Introduction to R, Introduction to Bioconductor, Introduction to Microarray Analysis, and Introduction to NGS Data Analysis.

Day Morning Session, 9:30 AM-12:30 PM Afternoon Session, 1:30 PM-4:30 PM Nov 9 Introduction to R Introduction to Bioconductor Nov 10 Introduction to Microarray Analysis Introduction to NGS Data Analysis

Read More

A Short Course in R for Biologists

"A Short Course in R for Biologists" is a two-day course given in four three-hour sessions entitled: Introduction to R, Introduction to Bioconductor, Introduction to Microarray Analysis, and Introduction to NGS Data Analysis.

Day Morning Session, 9:30 AM-12:30 PM Afternoon Session, 1:30 PM-4:30 PM Nov 9 Introduction to R Introduction to Bioconductor Nov 10 Introduction to Microarray Analysis Introduction to NGS Data Analysis

Registration Required

PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

Web-based resources for this class: (See Below for PDF versions)

The course will include frequent, short hands-on periods so students should bring their own laptops with a working installation of R, version 3.1 or later. In addition, several R packages will be used which must be installed prior to the course.

R is a console application. Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier. If you are comfortable running programs, viewing output, and editing files at the terminal, you will not need RStudio in order to take the course. However, RStudio offers quite an array of functions that you may still find useful and it is well worth a look.

R Installation

The R program and instructions for its installation under Linux, Mac OSX, and Windows can be found here:

http://cran.r-project.org/

Bioconductor and Bioconductor Package Installation

Complete instructions for the installation of the basic and additional Bioconductor packages are found here:

http://www.bioconductor.org/install/

In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class:

Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene affy simpleaffy arrayQualityMetrics limma survival ggplot2 hthgu133acdf hthgu133a.db gplots

Briefly, the following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above:

# First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene","affy","simpleaffy","arrayQualityMetrics","limma","survival","ggplot2","hthgu133acdf","hthgu133a.db","gplots")) RStudio Installation

Install the "€œDesktop, Open Source Edition"€:

http://www.rstudio.com/products/RStudio/#Desk

Class Outline Day 1 (Nov 9), Morning Session: Introduction to R
  • The R environment
    • Starting an R Session, Setting Options
    • Listing Variables, Editing Commands, Using the R History
    • Getting Help on an R Function
    • Logging a Session to a File
    • Running External R Code
    • Installing and Loading Packages
    • Ending a Session, Saving Your Work
  • The Elements of R
    • Numeric
    • Character
    • Logical
    • Missing Values
  • R Data Structures
    • Vectors
    • Matrices
    • Lists
    • Data.Frames
    • Factors
    • Functions
    • Other Complex Structures
  • Procedures
    • Reading and Writing Data
    • Exploring and Summarizing Data
    • Dealing with Missing Data
    • Restructuring Data
    • Relabeling Data
    • Subsetting Data
    • Operating on Rows or Columns of Data
    • Saving R Objects for Later Use
    • Graphing Data
    • Simple Statistical Tests
    • Example: A Simple Analysis of Probe Intensity Data
  • Project: Creating a Graphical Function in 4 Easy Steps
    • Step 1: Create an X-Y Plot to Compare Two Arrays
    • Step 2: Package the X-Y Plot as a Function
    • Step 3: Create a Median Array as a Better Standard for Comparison
    • Step 4: Rotate and Scale the Plot-€“Voila, You Have Created a MAPlot!
Day 1 (Nov 9), Afternoon Session: Introduction to Bioconductor
  • Installing Bioconductor
  • An Overview of Bioconductor Packages
  • Fundamental Packages
    • Biobase: the Foundation
    • Biostrings: A Representation of Biological Sequences
    • BSgenome: A Representation of Complete Genomic Sequences
    • GenomicRanges: Manipulation of Genomic Intervals
    • GenomicFeatures: Manipulation of Genomic Features
    • GenomicAlgnments: Manipulation of Short Genomic Alignments
  • Two Fundamental Structures to Contain Experiment Data
    • The ExpressionSet for Array Data
      • Constructing an ExpressionSet
      • Analyzing an ExpressionSet
    • The SummarizedExperiment for NGS Sequence Data
      • Constructing a SummarizedExperiment
      • Analyzing a SummarizedExperiment
Day 2 (Nov 10), Morning Session: Introduction to Microarray Analysis

The objective of this session is to initiate students in the analysis of microarrays using R and Bioconductor. To better help students take advantage of the microarray services offered by the Laboratory of Molecular Technology at NCI-Frederick, the focus of the course will be on the analysis of data from Affymetrix chips. It is assumed that the student has some knowledge of microarray workflows.

  • Downloading Data from The Cancer Genome Atlas Databases
  • Preliminary Steps: Array Pre-Processing
    • Checking the Quality of Arrays
    • Performing Array Normalization
  • Identifying Differentially Expressed Genes
  • Data Visualization
    • Performing Principal Component Analysis (PCA)
    • Computing and Interpreting Heatmaps
    • Computing and Interpreting Kaplan Meir Curves
Day 2 (Nov 10), Afternoon Session: Introduction to NGS Data Analysis

Details to be announced

Register
Organizer
BTEP
When
Mon, Nov 09 - Tue, Nov 10, 2015 -9:30 am - 4:30 pm
Where
FAES - Classroom 7/6
A Short Course in R for Biologists "A Short Course in R for Biologists" is a two-day course given in four three-hour sessions entitled: Introduction to R, Introduction to Bioconductor, Introduction to Microarray Analysis, and Introduction to NGS Data Analysis. Day Morning Session, 9:30 AM-12:30 PM Afternoon Session, 1:30 PM-4:30 PM Nov 9 Introduction to R Introduction to Bioconductor Nov 10 Introduction to Microarray Analysis Introduction to NGS Data Analysis Registration Required PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Web-based resources for this class: (See Below for PDF versions) Introduction to R for Biologists (David Wheeler) Introduction to Bioconductor (David Wheeler) Introduction to R (Sean Davis) Vignettes (Sean Davis) Data Files (Fathi Elloumi) R script (Fathi Elloumi) The course will include frequent, short hands-on periods so students should bring their own laptops with a working installation of R, version 3.1 or later. In addition, several R packages will be used which must be installed prior to the course. R is a console application. Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier. If you are comfortable running programs, viewing output, and editing files at the terminal, you will not need RStudio in order to take the course. However, RStudio offers quite an array of functions that you may still find useful and it is well worth a look. R Installation The R program and instructions for its installation under Linux, Mac OSX, and Windows can be found here: http://cran.r-project.org/ Bioconductor and Bioconductor Package Installation Complete instructions for the installation of the basic and additional Bioconductor packages are found here: http://www.bioconductor.org/install/ In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class: Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene affy simpleaffy arrayQualityMetrics limma survival ggplot2 hthgu133acdf hthgu133a.db gplots Briefly, the following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above: # First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene","affy","simpleaffy","arrayQualityMetrics","limma","survival","ggplot2","hthgu133acdf","hthgu133a.db","gplots")) RStudio Installation Install the "€œDesktop, Open Source Edition"€: http://www.rstudio.com/products/RStudio/#Desk Class Outline Day 1 (Nov 9), Morning Session: Introduction to R The R environment Starting an R Session, Setting Options Listing Variables, Editing Commands, Using the R History Getting Help on an R Function Logging a Session to a File Running External R Code Installing and Loading Packages Ending a Session, Saving Your Work The Elements of R Numeric Character Logical Missing Values R Data Structures Vectors Matrices Lists Data.Frames Factors Functions Other Complex Structures Procedures Reading and Writing Data Exploring and Summarizing Data Dealing with Missing Data Restructuring Data Relabeling Data Subsetting Data Operating on Rows or Columns of Data Saving R Objects for Later Use Graphing Data Simple Statistical Tests Example: A Simple Analysis of Probe Intensity Data Project: Creating a Graphical Function in 4 Easy Steps Step 1: Create an X-Y Plot to Compare Two Arrays Step 2: Package the X-Y Plot as a Function Step 3: Create a Median Array as a Better Standard for Comparison Step 4: Rotate and Scale the Plot-€“Voila, You Have Created a MAPlot! Day 1 (Nov 9), Afternoon Session: Introduction to Bioconductor Installing Bioconductor An Overview of Bioconductor Packages Fundamental Packages Biobase: the Foundation Biostrings: A Representation of Biological Sequences BSgenome: A Representation of Complete Genomic Sequences GenomicRanges: Manipulation of Genomic Intervals GenomicFeatures: Manipulation of Genomic Features GenomicAlgnments: Manipulation of Short Genomic Alignments Two Fundamental Structures to Contain Experiment Data The ExpressionSet for Array Data Constructing an ExpressionSet Analyzing an ExpressionSet The SummarizedExperiment for NGS Sequence Data Constructing a SummarizedExperiment Analyzing a SummarizedExperiment Day 2 (Nov 10), Morning Session: Introduction to Microarray Analysis The objective of this session is to initiate students in the analysis of microarrays using R and Bioconductor. To better help students take advantage of the microarray services offered by the Laboratory of Molecular Technology at NCI-Frederick, the focus of the course will be on the analysis of data from Affymetrix chips. It is assumed that the student has some knowledge of microarray workflows. Downloading Data from The Cancer Genome Atlas Databases Preliminary Steps: Array Pre-Processing Checking the Quality of Arrays Performing Array Normalization Identifying Differentially Expressed Genes Data Visualization Performing Principal Component Analysis (PCA) Computing and Interpreting Heatmaps Computing and Interpreting Kaplan Meir Curves Day 2 (Nov 10), Afternoon Session: Introduction to NGS Data Analysis Details to be announced 2015-11-09 09:30:00 FAES - Classroom 7/6 In-Person David Wheeler PhD. (Laboratory of Biochemistry and Molecular Biology CCR NCI),Sean Davis (CU Anschutz) BTEP 0 R/Bioconductor Basics Workshop (2-day)
777
Description
REGISTRATION FULL - Please signup for Session 2 December 7/8

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software.

PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. ...Read More

REGISTRATION FULL - Please signup for Session 2 December 7/8

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software.

PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

Day 1 -  9:30-12:30
Introductory Lecture 
Sean Davis, MD, PhD - CCR, NCI

Link to Talk Slides on SlideShare

Day 1 -  1:30-4:30
Use of Open Source tools for RNA-Seq
Sean Davis, MD, PhD - CCR, NCI

http://watson.nci.nih.gov/~sdavis/tutorials/RNASeqBeginnerTutorial/RNASeqTutorial.html

 

Day 2 - 9:30-12:30
RNA-Seq Analysis using Partek Flow
Eric Seiser, PhD - Partek Field Application Specialist

An overview of getting started on the NIH Helix server and then hands-on RNA-seq training on Partek Flow. The training starts from importing raw sequence data in fastq format, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation. 

 

Day 2 - 1:30-4:30

Read count data analysis using Partek Genomic Suite
Eric Seiser, PhD - Partek Field Application Specialist

This class will provide a demo of microarray and RNA-seq integration within Partek Flow followed by hands-on training for downstream RNA-seq data analysis using Partek Genomic Suite.

Starting with normalized read count data generated from Partek Flow, data import into PGS will be illustrated.  This will be followed by a standard gene expression analysis workflow including QA/QC, differential expression detection and biological interpretation using Partek Pathway.

Objectives:

Students will learn how to use basic features of Partek Flow and Partek Genomics Suite, including:

·       Flow

  • Getting set up on NIH Helix server
  • Importing data
  • Performing QA/AC
  • Alignment
  • Gene/transcript abundance estimation
  • Differential expression detection
  • Go Enrichment analysis
  • Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.)
  • Microarray analysis and integration with RNA-seq data.

·       PGS

  • Importing Partek Flow project and text file format
  • Performing QA/QC of imported data
  • Differential expression detection
  • Pathway analysis
  • Visualization (PCA, dot plot, heatmap etc.)

 

 

Register
Organizer
BTEP
When
Tue, Dec 01 - Wed, Dec 02, 2015 -9:30 am - 4:30 pm
Where
In-Person
REGISTRATION FULL - Please signup for Session 2 December 7/8 This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software. PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Day 1 -  9:30-12:30Introductory Lecture Sean Davis, MD, PhD - CCR, NCI Link to Talk Slides on SlideShare Day 1 -  1:30-4:30Use of Open Source tools for RNA-SeqSean Davis, MD, PhD - CCR, NCI http://watson.nci.nih.gov/~sdavis/tutorials/RNASeqBeginnerTutorial/RNASeqTutorial.html   Day 2 - 9:30-12:30RNA-Seq Analysis using Partek FlowEric Seiser, PhD - Partek Field Application Specialist An overview of getting started on the NIH Helix server and then hands-on RNA-seq training on Partek Flow. The training starts from importing raw sequence data in fastq format, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation.    Day 2 - 1:30-4:30 Read count data analysis using Partek Genomic SuiteEric Seiser, PhD - Partek Field Application Specialist This class will provide a demo of microarray and RNA-seq integration within Partek Flow followed by hands-on training for downstream RNA-seq data analysis using Partek Genomic Suite. Starting with normalized read count data generated from Partek Flow, data import into PGS will be illustrated.  This will be followed by a standard gene expression analysis workflow including QA/QC, differential expression detection and biological interpretation using Partek Pathway. Objectives: Students will learn how to use basic features of Partek Flow and Partek Genomics Suite, including: ·       Flow Getting set up on NIH Helix server Importing data Performing QA/AC Alignment Gene/transcript abundance estimation Differential expression detection Go Enrichment analysis Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.) Microarray analysis and integration with RNA-seq data. ·       PGS Importing Partek Flow project and text file format Performing QA/QC of imported data Differential expression detection Pathway analysis Visualization (PCA, dot plot, heatmap etc.)     2015-12-01 09:30:00 In-Person Sean Davis (CU Anschutz) BTEP 0 RNA-Seq Data Analysis Workshop (2-day) - Session 1 [Dec 1/2]
778
Description

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software.

PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be ...Read More

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software.

PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

Day 1 -  9:30-12:30
Introductory Lecture 
Sean Davis, MD, PhD - CCR, NCI

Link to Talk Slides on SlideShare

Day 1 -  1:30-4:30
Use of Open Source tools for RNA-Seq
Sean Davis, MD, PhD - CCR, NCI

http://watson.nci.nih.gov/~sdavis/tutorials/RNASeqBeginnerTutorial/RNASeqTutorial.html

Day 2 - 9:30-12:30
RNA-Seq Analysis using Partek Flow
Eric Seiser, PhD - Partek Field Application Specialist

An overview of getting started on the NIH Helix server and then hands-on RNA-seq training on Partek Flow. The training starts from importing raw sequence data in fastq format, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation. 

 

Day 2 - 1:30-4:30

Read count data analysis using Partek Genomic Suite
Eric Seiser, PhD - Partek Field Application Specialist

This class will provide a demo of microarray and RNA-seq integration within Partek Flow followed by hands-on training for downstream RNA-seq data analysis using Partek Genomic Suite.

Starting with normalized read count data generated from Partek Flow, data import into PGS will be illustrated.  This will be followed by a standard gene expression analysis workflow including QA/QC, differential expression detection and biological interpretation using Partek Pathway.

Objectives:

Students will learn how to use basic features of Partek Flow and Partek Genomics Suite, including:

·       Flow

  • Getting set up on NIH Helix server
  • Importing data
  • Performing QA/AC
  • Alignment
  • Gene/transcript abundance estimation
  • Differential expression detection
  • Go Enrichment analysis
  • Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.)
  • Microarray analysis and integration with RNA-seq data.

·       PGS

  • Importing Partek Flow project and text file format
  • Performing QA/QC of imported data
  • Differential expression detection
  • Pathway analysis
  • Visualization (PCA, dot plot, heatmap etc.)

 

Register
Organizer
BTEP
When
Mon, Dec 07 - Tue, Dec 08, 2015 -9:30 am - 4:30 pm
Where
FAES - Classroom 3 (Bldg 10: Room: B1C205)
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software. PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Day 1 -  9:30-12:30Introductory Lecture Sean Davis, MD, PhD - CCR, NCI Link to Talk Slides on SlideShare Day 1 -  1:30-4:30Use of Open Source tools for RNA-SeqSean Davis, MD, PhD - CCR, NCI http://watson.nci.nih.gov/~sdavis/tutorials/RNASeqBeginnerTutorial/RNASeqTutorial.html Day 2 - 9:30-12:30RNA-Seq Analysis using Partek FlowEric Seiser, PhD - Partek Field Application Specialist An overview of getting started on the NIH Helix server and then hands-on RNA-seq training on Partek Flow. The training starts from importing raw sequence data in fastq format, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation.    Day 2 - 1:30-4:30 Read count data analysis using Partek Genomic SuiteEric Seiser, PhD - Partek Field Application Specialist This class will provide a demo of microarray and RNA-seq integration within Partek Flow followed by hands-on training for downstream RNA-seq data analysis using Partek Genomic Suite. Starting with normalized read count data generated from Partek Flow, data import into PGS will be illustrated.  This will be followed by a standard gene expression analysis workflow including QA/QC, differential expression detection and biological interpretation using Partek Pathway. Objectives: Students will learn how to use basic features of Partek Flow and Partek Genomics Suite, including: ·       Flow Getting set up on NIH Helix server Importing data Performing QA/AC Alignment Gene/transcript abundance estimation Differential expression detection Go Enrichment analysis Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.) Microarray analysis and integration with RNA-seq data. ·       PGS Importing Partek Flow project and text file format Performing QA/QC of imported data Differential expression detection Pathway analysis Visualization (PCA, dot plot, heatmap etc.)   2015-12-07 09:30:00 FAES - Classroom 3 (Bldg 10: Room: B1C205) In-Person Sean Davis (CU Anschutz) BTEP 0 RNA-Seq Data Analysis Workshop (2-day) - Session 2 [Dec 7/8]
776
Description
REGISTRATION IS FULL FOR THIS WORKSHOP (25 ATTENDEES; 6 WAITLIST)

The Cancer Genome Atlas (TCGA) is a large-scale study that has cataloged genomic data accumulated for many different types of cancers, and includes mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation.  Being publicly distributed, it has become a major resource for cancer researchers in target discovery and in the biological interpretation and assessment of the clinical ...Read More

REGISTRATION IS FULL FOR THIS WORKSHOP (25 ATTENDEES; 6 WAITLIST)

The Cancer Genome Atlas (TCGA) is a large-scale study that has cataloged genomic data accumulated for many different types of cancers, and includes mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation.  Being publicly distributed, it has become a major resource for cancer researchers in target discovery and in the biological interpretation and assessment of the clinical impact of genes of interest.  This 2-day workshop will familiarize the audience with the types of data available and analytical tools, including a number of software packages, that enable end-users to easily and effectively mine TCGA data.

PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

Direction to FAES Academic Center Classrooms - please go to this webpage: https://faes.org/content/campus-life

Day 1 AM (Thu, Jan 7) - 9:30 am-12:30 pm  Introductory Lecture
(Maxwell Lee, PhD - CCR, NCI)

This session will include:

  • A brief history of TCGA
  • Overview of TCGA data portal
  • TCGA data access policy and download
  • Discussion of relevant TCGA publications
Day 1 PM (Thu, Jan 7) - 1:30 - 4:30 pm  Web-based exploration of TCGA data
(Parthav Jailwala, MS - CCBR, NCI)
  1. In this session, after an introductory overview of graphical tools and features in FireBrowse, we will carryout a brief hands-on exercise of running different queries using the online FireBrowse portal.

    • FireBrowse is a simple and elegant way to explore cancer data, backed by a powerful computational infrastructure, application programming interface (API), graphical tools and online reports.  Graphical tools like viewGene to explore expression levels, and iCoMut to explore the comprehensive analysis profile of each TCGA disease study within a single, interactive figure are novel features of this portal.

  2. In this session, after an introductory overview of NG-CHM user interface and features, we will carryout a brief hands-on exercise of generating different types of NG-CHMs on TCGA datasets.

    • Next-Generation (Clustered) Heat Maps (NG-CHMs) is another web-based tool for displaying clustered heat maps with links for statistical information, databases and other related analyses. These interactive heat maps enable the user to see an overview of the entire heatmap, and via interactive navigation controls, to zoom and pan across the heatmap to see details of the heatmap at many levels of resolution.

 

Day 2 AM (Fri, Jan 8) - 9:30 am -12:30 pm  BioDiscovery Nexus
(Andrea O'Hara, PhD - Field Application Scientist, BioDiscovery)

Nexus Copy Number is a platform independent copy number analysis software that includes co-visualization of sequence variants and gene expression data at both the individual and population wide levels. With an easy to use visual interface, the software allows for quick review and detailed analysis of population-wide copy number alterations across the entire genome.  NCI’s site license includes unlimited access to TCGA Premier, a database of re-processed, curated and reviewed TCGA samples.
 
In this workshop, you will learn how to use Nexus Copy Number software to mine TCGA copy number data. The Cancer Genome Atlas (TCGA) contains various types of genomic data from a wide variety of cancers, including several rare tumor types. The training session will focus on access of the TCGA data within the software and a detailed evaluation of one TCGA data set to identify statistically significant changes within the sample population.
 
Learning Objectives:

  • Access and integration of CNV, sequence variant and RNA-Seq expression TCGA data directly from Nexus.
  • Visualization and statistical approaches for discovery.
  • Sample stratification by clinical annotation factors or biomarkers.
  • Finding CNVs predictive of survival or other outcome data.
  • Generate publication-ready figures and charts during analysis.
Day 2 PM (Fri, Jan 8) - 1:30 - 4:30 pm  cBioPortal
(Nikolaus Schultz, PhD - Memorial Sloan-Kettering Cancer Center and Anand S. Merchant, MD, PhD. - CCBR, NCI)

This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets. As of early 2016 the portal contains multidimensional data from 105 cancer genomics studies. This session will include:

  • Introduction to cBioPortal - Niki Schultz
  • OncoQueryLanguage (OQL) - Key words and Codes
  • Features, Analytics, and Interpreting Results
  • Tutorials/Hands-on Exercises - Anand S. Merchant
Register
Organizer
BTEP
When
Thu, Jan 07 - Fri, Jan 08, 2016 -9:30 am - 4:30 pm
Where
NIH Bldg 10 FAES Room 3 - B1C207 FAES
REGISTRATION IS FULL FOR THIS WORKSHOP (25 ATTENDEES; 6 WAITLIST) The Cancer Genome Atlas (TCGA) is a large-scale study that has cataloged genomic data accumulated for many different types of cancers, and includes mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation.  Being publicly distributed, it has become a major resource for cancer researchers in target discovery and in the biological interpretation and assessment of the clinical impact of genes of interest.  This 2-day workshop will familiarize the audience with the types of data available and analytical tools, including a number of software packages, that enable end-users to easily and effectively mine TCGA data. PLEASE NOTE: This 2 day workshop is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Direction to FAES Academic Center Classrooms - please go to this webpage: https://faes.org/content/campus-life Day 1 AM (Thu, Jan 7) - 9:30 am-12:30 pm  Introductory Lecture (Maxwell Lee, PhD - CCR, NCI) This session will include: A brief history of TCGA Overview of TCGA data portal TCGA data access policy and download Discussion of relevant TCGA publications Day 1 PM (Thu, Jan 7) - 1:30 - 4:30 pm  Web-based exploration of TCGA data (Parthav Jailwala, MS - CCBR, NCI) In this session, after an introductory overview of graphical tools and features in FireBrowse, we will carryout a brief hands-on exercise of running different queries using the online FireBrowse portal. FireBrowse is a simple and elegant way to explore cancer data, backed by a powerful computational infrastructure, application programming interface (API), graphical tools and online reports.  Graphical tools like viewGene to explore expression levels, and iCoMut to explore the comprehensive analysis profile of each TCGA disease study within a single, interactive figure are novel features of this portal. In this session, after an introductory overview of NG-CHM user interface and features, we will carryout a brief hands-on exercise of generating different types of NG-CHMs on TCGA datasets. Next-Generation (Clustered) Heat Maps (NG-CHMs) is another web-based tool for displaying clustered heat maps with links for statistical information, databases and other related analyses. These interactive heat maps enable the user to see an overview of the entire heatmap, and via interactive navigation controls, to zoom and pan across the heatmap to see details of the heatmap at many levels of resolution.   Day 2 AM (Fri, Jan 8) - 9:30 am -12:30 pm  BioDiscovery Nexus (Andrea O'Hara, PhD - Field Application Scientist, BioDiscovery) Nexus Copy Number is a platform independent copy number analysis software that includes co-visualization of sequence variants and gene expression data at both the individual and population wide levels. With an easy to use visual interface, the software allows for quick review and detailed analysis of population-wide copy number alterations across the entire genome.  NCI’s site license includes unlimited access to TCGA Premier, a database of re-processed, curated and reviewed TCGA samples.   In this workshop, you will learn how to use Nexus Copy Number software to mine TCGA copy number data. The Cancer Genome Atlas (TCGA) contains various types of genomic data from a wide variety of cancers, including several rare tumor types. The training session will focus on access of the TCGA data within the software and a detailed evaluation of one TCGA data set to identify statistically significant changes within the sample population.  Learning Objectives: Access and integration of CNV, sequence variant and RNA-Seq expression TCGA data directly from Nexus. Visualization and statistical approaches for discovery. Sample stratification by clinical annotation factors or biomarkers. Finding CNVs predictive of survival or other outcome data. Generate publication-ready figures and charts during analysis. Day 2 PM (Fri, Jan 8) - 1:30 - 4:30 pm  cBioPortal (Nikolaus Schultz, PhD - Memorial Sloan-Kettering Cancer Center and Anand S. Merchant, MD, PhD. - CCBR, NCI) This publicly accessible web-based resource provides visualization, analysis and download of large-scale cancer genomics data sets. As of early 2016 the portal contains multidimensional data from 105 cancer genomics studies. This session will include: Introduction to cBioPortal - Niki Schultz OncoQueryLanguage (OQL) - Key words and Codes Features, Analytics, and Interpreting Results Tutorials/Hands-on Exercises - Anand S. Merchant 2016-01-07 09:30:00 NIH Bldg 10 FAES Room 3 - B1C207 FAES In-Person Parthav Jailwala (CCBR),Maxwell Lee (CCR NCI) BTEP 0 TCGA Data Analysis Workshop (2-day)
775
Description
BTEP Workshop on Data Visualization, Exploration and Analysis NOTE:This is a Bring Your Own Computer (BYOC) class, and will be simultaneously shared via GoToMeeting with attendees at the Advanced Technology Research Facility (ATRF) in Frederick, MD. Kindly select the location you plan to attend at the top of the registration page. Date: February 8, 2016 (Monday)
Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Two Venues:

...Read More

BTEP Workshop on Data Visualization, Exploration and Analysis NOTE:This is a Bring Your Own Computer (BYOC) class, and will be simultaneously shared via GoToMeeting with attendees at the Advanced Technology Research Facility (ATRF) in Frederick, MD. Kindly select the location you plan to attend at the top of the registration page. Date: February 8, 2016 (Monday)
Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Two Venues:

Live Workshop - NIH Bethesda - Bldg 10, FAES Room 3 (B1C207)
Remote Simulcast – ATRF, Room E1106 - 8560 Progress Dr, Frederick, MD

For more information on the Frederick simulcast, please contact:
Tracie Frederick, Technology Informationist, Scientific Library
Phone: 301-846-1094
Email: frederickt@mail.nih.gov

AGENDA 9:30 am – 12:30 pm – Data Visualization with UCSC Cancer Browser

Presenter: Mary Goldman

https://genome-cancer.ucsc.edu/

The Xena Platform from the UC Santa Cruz Genomics Institute integrates and visualizes functional genomics data from easy-to-use data hubs, allowing you to view data from large public datasets, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), as well as your own private secure data, together or separately. In this hands-on workshop we will view various -omic data types, including, but not limited to, positional mutation data, copy number variation, gene and exon expression, and phenotype/clinical data such as age and subtype. We will view example 'private' data in conjunction with public data from TCGA. We will dynamically generate KM plots as well as view data as a spreadsheet heatmap, bar graphs, and scatter plots. Xena is also integrated with Galaxy, giving access to a myriad of bioinformatics tools for analysis.

1:30 pm – 4:30 pm – Data Exploration with Qlucore

Presenter:  Sara Strandberg

Qlucore Omics Explorer is an exploration tools to analyze your experiment data yourself. It empowers biologists and bench scientists to easily visualize and analyze large numerical data sets such as Gene expression (array and RNA seq), DNA methylation, Proteomics, Metabolomics and Flow Cytometry data. Hand-outs and documentation will be provided to attendees on the day of workshop.

During the training you will learn how to:

1.     Import and visualize data with various plot types  - Heatmaps, PCA, Bar plots, Box plots, etc.

2.     Identify discriminating variables using statistical tests - t-test, ANOVA, regression analysis

3.     Use visualization to enhance the analysis and interpret results

4.     Achieve biological insight and work with hypothesis generation

5.     Export your result, such as variable lists and images

IMPORTANT!

Prior to the training please take the following steps.

1. Download and install the Qlucore software:

  • Go to www.qlucore.com/evaluation
  • Complete the quick registration process (if you do not have a login username/password)
  • Download and install the preferred version of the software (MAC or PC)

2. Activate Training License:

  • Save the license file provided below as Course Material 1 - QluCoreLicenseFile (qlucore_license_file.lic) on your Desktop
  • Open Qlucore Omics Explorer
  • Go to License/Import License File and select the license file that you just saved on Desktop

3. Download all of the files shared through the zipped file below - Course Material2: QlucoreTrainingMaterialsFeb82016

 

Register
Organizer
BTEP
When
Mon, Feb 08, 2016 - 9:30 am - 4:30 pm
Where
In-Person
BTEP Workshop on Data Visualization, Exploration and Analysis NOTE:This is a Bring Your Own Computer (BYOC) class, and will be simultaneously shared via GoToMeeting with attendees at the Advanced Technology Research Facility (ATRF) in Frederick, MD. Kindly select the location you plan to attend at the top of the registration page. Date: February 8, 2016 (Monday)Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Two Venues: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 3 (B1C207)Remote Simulcast – ATRF, Room E1106 - 8560 Progress Dr, Frederick, MD For more information on the Frederick simulcast, please contact:Tracie Frederick, Technology Informationist, Scientific LibraryPhone: 301-846-1094Email: frederickt@mail.nih.gov AGENDA 9:30 am – 12:30 pm – Data Visualization with UCSC Cancer Browser Presenter: Mary Goldman https://genome-cancer.ucsc.edu/ The Xena Platform from the UC Santa Cruz Genomics Institute integrates and visualizes functional genomics data from easy-to-use data hubs, allowing you to view data from large public datasets, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), as well as your own private secure data, together or separately. In this hands-on workshop we will view various -omic data types, including, but not limited to, positional mutation data, copy number variation, gene and exon expression, and phenotype/clinical data such as age and subtype. We will view example 'private' data in conjunction with public data from TCGA. We will dynamically generate KM plots as well as view data as a spreadsheet heatmap, bar graphs, and scatter plots. Xena is also integrated with Galaxy, giving access to a myriad of bioinformatics tools for analysis. 1:30 pm – 4:30 pm – Data Exploration with Qlucore Presenter:  Sara Strandberg Qlucore Omics Explorer is an exploration tools to analyze your experiment data yourself. It empowers biologists and bench scientists to easily visualize and analyze large numerical data sets such as Gene expression (array and RNA seq), DNA methylation, Proteomics, Metabolomics and Flow Cytometry data. Hand-outs and documentation will be provided to attendees on the day of workshop. During the training you will learn how to: 1.     Import and visualize data with various plot types  - Heatmaps, PCA, Bar plots, Box plots, etc. 2.     Identify discriminating variables using statistical tests - t-test, ANOVA, regression analysis 3.     Use visualization to enhance the analysis and interpret results 4.     Achieve biological insight and work with hypothesis generation 5.     Export your result, such as variable lists and images IMPORTANT! Prior to the training please take the following steps. 1. Download and install the Qlucore software: Go to www.qlucore.com/evaluation Complete the quick registration process (if you do not have a login username/password) Download and install the preferred version of the software (MAC or PC) 2. Activate Training License: Save the license file provided below as Course Material 1 - QluCoreLicenseFile (qlucore_license_file.lic) on your Desktop Open Qlucore Omics Explorer Go to License/Import License File and select the license file that you just saved on Desktop 3. Download all of the files shared through the zipped file below - Course Material2: QlucoreTrainingMaterialsFeb82016   2016-02-08 09:30:00 In-Person BTEP 0 Data Visualization, Exploration and Analysis
774
Description
BTEP Workshop on Pathway Analysis with MetaCore NOTE:This is a Bring Your Own Computer (BYOC) class, and will be simultaneously shared via GoToMeeting with attendees at the Advanced Technology Research Facility (ATRF) in Frederick, MD. Kindly select the location you plan to attend at the top of the registration page. Date: February 9, 2016 (Tuesday)
Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Two Venues:

Live ...Read More

BTEP Workshop on Pathway Analysis with MetaCore NOTE:This is a Bring Your Own Computer (BYOC) class, and will be simultaneously shared via GoToMeeting with attendees at the Advanced Technology Research Facility (ATRF) in Frederick, MD. Kindly select the location you plan to attend at the top of the registration page. Date: February 9, 2016 (Tuesday)
Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Two Venues:

Live Workshop - NIH Bethesda - Bldg 10, FAES Room 7 (B1C206)
Remote Simulcast – ATRF, Room E1106 - 8560 Progress Dr, Frederick, MD

For more information on the Frederick simulcast, please contact:
Tracie Frederick, Technology Informationist, Scientific Library
Phone: 301-846-1094
Email: frederickt@mail.nih.gov

AGENDA Tuesday, February 9, 2016 – Pathway Analysis with MetaCore

MetaCore is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. MetaCore is based on a proprietary manually curated database of human protein-protein, protein-DNA and protein-compound interactions, metabolic and signaling pathways for human, mouse and rat, supported by proprietary ontologies and controlled vocabulary. The analytical package includes easy-to-use, intuitive tools for searching and data visualization, enabling the identification of the most relevant biological pathways, networks, and processes in our "virtual lab."

The core functionalities in MetaCore that will be covered include:

  • Key Pathway Advisor to find drivers of expression change from transcriptomic data
  • Expression data upload, filtering, and setting experimental backgrounds
  • Knowledge mining for information about a gene or disease
  • Basic enrichment and Comparison analysis
Register
Organizer
BTEP
When
Tue, Feb 09, 2016 - 9:30 am - 4:30 pm
Where
In-Person
BTEP Workshop on Pathway Analysis with MetaCore NOTE:This is a Bring Your Own Computer (BYOC) class, and will be simultaneously shared via GoToMeeting with attendees at the Advanced Technology Research Facility (ATRF) in Frederick, MD. Kindly select the location you plan to attend at the top of the registration page. Date: February 9, 2016 (Tuesday)Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Two Venues: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 7 (B1C206)Remote Simulcast – ATRF, Room E1106 - 8560 Progress Dr, Frederick, MD For more information on the Frederick simulcast, please contact:Tracie Frederick, Technology Informationist, Scientific LibraryPhone: 301-846-1094Email: frederickt@mail.nih.gov AGENDA Tuesday, February 9, 2016 – Pathway Analysis with MetaCore MetaCore is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. MetaCore is based on a proprietary manually curated database of human protein-protein, protein-DNA and protein-compound interactions, metabolic and signaling pathways for human, mouse and rat, supported by proprietary ontologies and controlled vocabulary. The analytical package includes easy-to-use, intuitive tools for searching and data visualization, enabling the identification of the most relevant biological pathways, networks, and processes in our "virtual lab." The core functionalities in MetaCore that will be covered include: Key Pathway Advisor to find drivers of expression change from transcriptomic data Expression data upload, filtering, and setting experimental backgrounds Knowledge mining for information about a gene or disease Basic enrichment and Comparison analysis 2016-02-09 09:30:00 In-Person BTEP 0 Pathway Analysis with MetaCore
773
Description
BTEP Workshop on Exome-Seq Data Analysis and Variant Annotation (2-day) This workshop will cover the basics and best practices of exome-seq analysis including downstream interpretation of variants using a variety of in-house, open-source and commercial web tools (CCBR Exome-Seq Pipeliner, AVIA, Ingenuity Variant Analysis, and CRAVAT/MuPit). Please note that this workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that ...Read More
BTEP Workshop on Exome-Seq Data Analysis and Variant Annotation (2-day) This workshop will cover the basics and best practices of exome-seq analysis including downstream interpretation of variants using a variety of in-house, open-source and commercial web tools (CCBR Exome-Seq Pipeliner, AVIA, Ingenuity Variant Analysis, and CRAVAT/MuPit). Please note that this workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that location. Dates: March 8-9, 2016 (Tuesday and Wednesday) Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm (both days) Venues: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 6 Remote Simulcast – Scientific Library Training Room, Bldg 549, NCI-Frederick

For more information on the Frederick simulcast, please contact:

Tracie Frederick,
Technology Informationist,
Scientific Library, NCI at Frederick
Phone: 301-846-1094
Email: frederickt@mail.nih.gov

WORKSHOP AGENDA

Day 1 - Tuesday, March 8, 2016

9:30 am to 12:30 pm -  Introductory Lectures

Chunhua Yan, PhD -  Primer on Next Generation and Exome Sequencing

This will be an introduction to NGS in general and Exome-Seq in particular, covering:

    •    Next generation sequencing technology
    •    Exome sequencing (Cost, Speed, Gene coverage, Biological implication)
    •    Experimental design (Sample size, Coverage, Sample submission)
    •    Mutation Calling (Dream challenge, Genome in Bottle)

Justin Lack, PhD - Exome-Seq Data Analysis Pipeline: From Reads to Results

This talk will provide an overview of the exome-seq pipeline work-flow with recommended best practices.

Some of the topics covered will be:
    •    Read quality trimming and adapter clipping,
    •    Initial QC and read mapping challenges,
    •    Impact and removal of PCR duplicates,
    •    Local realignment around indels, quality recalibration,
    •    Germline variant calling in the Haplotype Caller,
    •    Somatic variant detection using MuTect,
    •    All-in-one annotator (AVIA), and
    •    Example of a down-stream analysis of a tumor/germline comparison data set.

12:30 - 1:30 pm LUNCH BREAK

1:30-4:30 pm - Open-Source Software Tools for Analysis of Exome-Seq Data

  • David Wheeler, PhD: Brief Introduction to the graphical user interface (GUI) of the CCBR Exome­-Seq Pipeliner

This presentation will introduce the concept of pipelines in general and touch on the robustness and reproducibility of pipelines, as well as parallel execution, tracking of inputs/outputs and reports from pipelines. There will be a  brief discussion on the modular features of Snakemake as a segue into the Pipeliner program and a pipeline definition. This will be followed by a demonstration of the pipeline and its availability for use of exome-seq data analysis.

  • Hue Vuong, PhD: Introduction and Tutorial on AVIA

The Annotation, Visualization, and Impact Analysis application, AVIA (https://avia-abcc.ncifcrf.gov), is an interactive web-based annotation server used to explore and interpret large sets of single nucleotide polymorphisms (SNPs) and small insertion/deletions (indels).  Along with assigning gene impact of genomic variants, AVIA helps users to perform custom annotations of the variant from various disparate data sources, such as SIFT, Polyphen2, TargetScan, nonB, etc.   Using AVIA, users will be able to annotate files, filter variants, and view gene level annotations for their variants.  Users may upload VCF4, BED, CLC bio variant files in text or compressed formats ( zip, gz, tar).  Users can also  explore gene level effects from PharmGKB, Drug Bank, GO Ontology, DAVID, etc.  In this hands-on workshop, users will learn how to submit a set of variants to AVIA and how to navigate the results page to find variants of possible significance.

  • Maggie Cam, PhD: Integrative Genomics Viewer (IGV) Tutorial

The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations. The hands-on tutorial will walk attendees on how to visualizing variant (VCF) and alignment (BAM) files using IGV.

Day 2 - Wednesday, March 9, 2016

9:30 am - 12:30 pm - Commercial Software for Exome-Seq Data: QIAGEN's Ingenuity Variant Analysis

 Devendra (Dev) Mistry, Field Application Specialist

Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based both upon published biological evidence and your own knowledge of disease biology. This workshop will focus on how the users can upload their datasets, efficiently use different filters within variant analysis to identify causal variants, export data and will also go over the recent IVA updates. With IVA, you can interrogate your variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up.

12:30 - 1:30 pm LUNCH BREAK

1:30-4:30 pm  - CRAVAT/MuPIT: Academic Open-Source Tool for Analysis of Genomic Variants 

Michael Ryan, PhD

CRAVAT (www.cravat.us) is a free tool for high-throughput analysis of human sequencing variants developed by the Karchin lab at Johns Hopkins and In Silico Solutions.  CRAVAT accepts very large variant data files containing single nucleotide substitutions as well as indels and returns a wide variety of annotations and scores that help with identification of important variants.  The workshop will provide some background on CRAVAT and lots of hands-on exercises to learn how to use the tool and interpret the results.

MuPIT (mupit.icm.jhu) is a sister tool to CRAVAT that shows human mutations on 3D protein structures.  The focus of the workshop will be a series of exercises to learn how to visualize your mutations in MuPIT, how CRAVAT and MuPIT are integrated, and how to manipulate, investigate, and understand the results.

Register
Organizer
BTEP
When
Tue, Mar 08 - Wed, Mar 09, 2016 -9:30 am - 4:30 pm
Where
NIH Bldg 10 FAES and NCI-Frederick Bldg 549 Library
BTEP Workshop on Exome-Seq Data Analysis and Variant Annotation (2-day) This workshop will cover the basics and best practices of exome-seq analysis including downstream interpretation of variants using a variety of in-house, open-source and commercial web tools (CCBR Exome-Seq Pipeliner, AVIA, Ingenuity Variant Analysis, and CRAVAT/MuPit). Please note that this workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that location. Dates: March 8-9, 2016 (Tuesday and Wednesday) Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm (both days) Venues: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 6 Remote Simulcast – Scientific Library Training Room, Bldg 549, NCI-Frederick For more information on the Frederick simulcast, please contact: Tracie Frederick, Technology Informationist, Scientific Library, NCI at Frederick Phone: 301-846-1094 Email: frederickt@mail.nih.gov WORKSHOP AGENDA Day 1 - Tuesday, March 8, 2016 9:30 am to 12:30 pm -  Introductory Lectures Chunhua Yan, PhD -  Primer on Next Generation and Exome Sequencing This will be an introduction to NGS in general and Exome-Seq in particular, covering:     •    Next generation sequencing technology     •    Exome sequencing (Cost, Speed, Gene coverage, Biological implication)     •    Experimental design (Sample size, Coverage, Sample submission)     •    Mutation Calling (Dream challenge, Genome in Bottle) Justin Lack, PhD - Exome-Seq Data Analysis Pipeline: From Reads to Results This talk will provide an overview of the exome-seq pipeline work-flow with recommended best practices. Some of the topics covered will be:     •    Read quality trimming and adapter clipping,     •    Initial QC and read mapping challenges,     •    Impact and removal of PCR duplicates,     •    Local realignment around indels, quality recalibration,     •    Germline variant calling in the Haplotype Caller,     •    Somatic variant detection using MuTect,     •    All-in-one annotator (AVIA), and     •    Example of a down-stream analysis of a tumor/germline comparison data set. 12:30 - 1:30 pm LUNCH BREAK 1:30-4:30 pm - Open-Source Software Tools for Analysis of Exome-Seq Data David Wheeler, PhD: Brief Introduction to the graphical user interface (GUI) of the CCBR Exome­-Seq Pipeliner This presentation will introduce the concept of pipelines in general and touch on the robustness and reproducibility of pipelines, as well as parallel execution, tracking of inputs/outputs and reports from pipelines. There will be a  brief discussion on the modular features of Snakemake as a segue into the Pipeliner program and a pipeline definition. This will be followed by a demonstration of the pipeline and its availability for use of exome-seq data analysis. Hue Vuong, PhD: Introduction and Tutorial on AVIA The Annotation, Visualization, and Impact Analysis application, AVIA (https://avia-abcc.ncifcrf.gov), is an interactive web-based annotation server used to explore and interpret large sets of single nucleotide polymorphisms (SNPs) and small insertion/deletions (indels).  Along with assigning gene impact of genomic variants, AVIA helps users to perform custom annotations of the variant from various disparate data sources, such as SIFT, Polyphen2, TargetScan, nonB, etc.   Using AVIA, users will be able to annotate files, filter variants, and view gene level annotations for their variants.  Users may upload VCF4, BED, CLC bio variant files in text or compressed formats ( zip, gz, tar).  Users can also  explore gene level effects from PharmGKB, Drug Bank, GO Ontology, DAVID, etc.  In this hands-on workshop, users will learn how to submit a set of variants to AVIA and how to navigate the results page to find variants of possible significance. Maggie Cam, PhD: Integrative Genomics Viewer (IGV) Tutorial The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations. The hands-on tutorial will walk attendees on how to visualizing variant (VCF) and alignment (BAM) files using IGV. Day 2 - Wednesday, March 9, 2016 9:30 am - 12:30 pm - Commercial Software for Exome-Seq Data: QIAGEN's Ingenuity Variant Analysis  Devendra (Dev) Mistry, Field Application Specialist Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based both upon published biological evidence and your own knowledge of disease biology. This workshop will focus on how the users can upload their datasets, efficiently use different filters within variant analysis to identify causal variants, export data and will also go over the recent IVA updates. With IVA, you can interrogate your variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. 12:30 - 1:30 pm LUNCH BREAK 1:30-4:30 pm  - CRAVAT/MuPIT: Academic Open-Source Tool for Analysis of Genomic Variants  Michael Ryan, PhD CRAVAT (www.cravat.us) is a free tool for high-throughput analysis of human sequencing variants developed by the Karchin lab at Johns Hopkins and In Silico Solutions.  CRAVAT accepts very large variant data files containing single nucleotide substitutions as well as indels and returns a wide variety of annotations and scores that help with identification of important variants.  The workshop will provide some background on CRAVAT and lots of hands-on exercises to learn how to use the tool and interpret the results. MuPIT (mupit.icm.jhu) is a sister tool to CRAVAT that shows human mutations on 3D protein structures.  The focus of the workshop will be a series of exercises to learn how to visualize your mutations in MuPIT, how CRAVAT and MuPIT are integrated, and how to manipulate, investigate, and understand the results. 2016-03-08 09:30:00 NIH Bldg 10 FAES and NCI-Frederick Bldg 549 Library In-Person Justin Lack (NIAID CBR),Maggie Cam (NCI CCBR),David Wheeler PhD. (Laboratory of Biochemistry and Molecular Biology CCR NCI),Devendra Mistry (QIAGEN) BTEP 0 Exome-Seq Data Analysis and Variant Annotation
772
Description
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very ...Read More

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. This workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that location. Please register only if you intend to attend the workshop.

Dates: April 4-5, 2016 (Monday and Tuesday)

Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm (both days)

Venues:

Live Workshop - NIH Bethesda - Bldg 10, FAES Room 6

Remote Simulcast – Scientific Library Training Room, Bldg 549, NCI-Frederick

For more information on the Frederick simulcast, please contact:

Tracie Frederick,
Technology Informationist,
Scientific Library, NCI at Frederick
Phone: 301-846-1094
Email: frederickt@mail.nih.gov

WORKSHOP AGENDA

Monday, April 4, 2016

Day 1 -   9:30 am - 12:30 pm

Introductory Lecture 
Sean Davis, MD, PhD - CCR, NCI

Link to Talk Slides on SlideShare

Day 1 -  1:30-4:30 pm

Use of Open Source tools for RNA-Seq
Sean Davis, MD, PhD - CCR, NCI

http://watson.nci.nih.gov/~sdavis/tutorials/RNASeqBeginnerTutorial/RNASeqTutorial.html

 

Tuesday, April 5, 2016

Day 2 - 9:30 am - 12:30 pm

RNA-Seq Analysis using Partek Flow
Eric Seiser, PhD - Partek Field Application Specialist

An overview of getting started on the NIH Helix server and then hands-on RNA-seq training on Partek Flow. The training starts from importing raw sequence data in fastq format, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation. 

 

Day 2 - 1:30-4:30 pm

Read count data analysis using Partek Genomic Suite
Eric Seiser, PhD - Partek Field Application Specialist

This class will provide a demo of microarray and RNA-seq integration within Partek Flow followed by hands-on training for downstream RNA-seq data analysis using Partek Genomic Suite.

Starting with normalized read count data generated from Partek Flow, data import into PGS will be illustrated.  This will be followed by a standard gene expression analysis workflow including QA/QC, differential expression detection and biological interpretation using Partek Pathway.

Objectives:

Students will learn how to use basic features of Partek Flow and Partek Genomics Suite, including:

·       Flow

  • Getting set up on NIH Helix server
  • Importing data
  • Performing QA/AC
  • Alignment
  • Gene/transcript abundance estimation
  • Differential expression detection
  • Go Enrichment analysis
  • Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.)
  • Microarray analysis and integration with RNA-seq data.

·       PGS

  • Importing Partek Flow project and text file format
  • Performing QA/QC of imported data
  • Differential expression detection
  • Pathway analysis
  • Visualization (PCA, dot plot, heatmap etc.)

Files required for Partek are available under Course Materials below.

Partek Genomics Suite with Pathway Training License Installation instructions

For Mac Laptop:

1. Download Partek Genomics Suite from http://www.partek.com/html/updates.html
2. Then install it from an account that has administrative privileges. After installation, place the license file in the "/Users/Shared" folder.

For Windows Laptop:

1. Download Partek Genomics Suite from http://www.partek.com/html/updates.html
2. Then install it from an account that has administrative privileges. After installation, place the license file in the "C:Program FilesPartek Genomics Suite 6.6license" folder.

Pathway is bundled with the Genomics Suite and this license supports both. This means that the Pathway-specific menu items within GS will work.
Now you are ready to analyze your own data with full access to all the powerful statistics, visualization, and analysis features that Partek Genomics Suite offers.
 
Technical Support:
support@partek.com
North America: +1.314-884.6172

Register
Organizer
BTEP
When
Mon, Apr 04 - Tue, Apr 05, 2016 -9:30 am - 4:30 pm
Where
NIH Bldg 10 FAES Room 6 (Live); NCI-Frederick Bldg 549 Training Room (Simulcast)
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include presentations on using both commercial (Partek) and open source software. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. This workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that location. Please register only if you intend to attend the workshop. Dates: April 4-5, 2016 (Monday and Tuesday) Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm (both days) Venues: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 6 Remote Simulcast – Scientific Library Training Room, Bldg 549, NCI-Frederick For more information on the Frederick simulcast, please contact: Tracie Frederick, Technology Informationist, Scientific Library, NCI at Frederick Phone: 301-846-1094 Email: frederickt@mail.nih.gov WORKSHOP AGENDA Monday, April 4, 2016 Day 1 -   9:30 am - 12:30 pm Introductory Lecture Sean Davis, MD, PhD - CCR, NCI Link to Talk Slides on SlideShare Day 1 -  1:30-4:30 pm Use of Open Source tools for RNA-SeqSean Davis, MD, PhD - CCR, NCI http://watson.nci.nih.gov/~sdavis/tutorials/RNASeqBeginnerTutorial/RNASeqTutorial.html   Tuesday, April 5, 2016 Day 2 - 9:30 am - 12:30 pm RNA-Seq Analysis using Partek FlowEric Seiser, PhD - Partek Field Application Specialist An overview of getting started on the NIH Helix server and then hands-on RNA-seq training on Partek Flow. The training starts from importing raw sequence data in fastq format, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation.    Day 2 - 1:30-4:30 pm Read count data analysis using Partek Genomic SuiteEric Seiser, PhD - Partek Field Application Specialist This class will provide a demo of microarray and RNA-seq integration within Partek Flow followed by hands-on training for downstream RNA-seq data analysis using Partek Genomic Suite. Starting with normalized read count data generated from Partek Flow, data import into PGS will be illustrated.  This will be followed by a standard gene expression analysis workflow including QA/QC, differential expression detection and biological interpretation using Partek Pathway. Objectives: Students will learn how to use basic features of Partek Flow and Partek Genomics Suite, including: ·       Flow Getting set up on NIH Helix server Importing data Performing QA/AC Alignment Gene/transcript abundance estimation Differential expression detection Go Enrichment analysis Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.) Microarray analysis and integration with RNA-seq data. ·       PGS Importing Partek Flow project and text file format Performing QA/QC of imported data Differential expression detection Pathway analysis Visualization (PCA, dot plot, heatmap etc.) Files required for Partek are available under Course Materials below. Partek Genomics Suite with Pathway Training License Installation instructions For Mac Laptop: 1. Download Partek Genomics Suite from http://www.partek.com/html/updates.html 2. Then install it from an account that has administrative privileges. After installation, place the license file in the "/Users/Shared" folder. For Windows Laptop: 1. Download Partek Genomics Suite from http://www.partek.com/html/updates.html 2. Then install it from an account that has administrative privileges. After installation, place the license file in the "C:Program FilesPartek Genomics Suite 6.6license" folder. Pathway is bundled with the Genomics Suite and this license supports both. This means that the Pathway-specific menu items within GS will work. Now you are ready to analyze your own data with full access to all the powerful statistics, visualization, and analysis features that Partek Genomics Suite offers.   Technical Support:support@partek.com North America: +1.314-884.6172 2016-04-04 09:30:00 NIH Bldg 10 FAES Room 6 (Live); NCI-Frederick Bldg 549 Training Room (Simulcast) In-Person Sean Davis (CU Anschutz) BTEP 0 RNA-Seq Data Analysis Workshop
771
Description
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq Data Analysis. There will also be a talk on ENCODE and a comprehensive discussion of topics ranging from experimental design to data visualization. This workshop will include presentations on using open source applications (MACS, HOMER, MEME, SICER) as well as commercial software (Genomatix).  To access the NCI Genomatix server, to request for a password through ...Read More
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq Data Analysis. There will also be a talk on ENCODE and a comprehensive discussion of topics ranging from experimental design to data visualization. This workshop will include presentations on using open source applications (MACS, HOMER, MEME, SICER) as well as commercial software (Genomatix).  To access the NCI Genomatix server, to request for a password through IT helpdesk to genomeanalyzer.nci.nih.gov. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. This workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that location. Please register only if you intend to attend the workshop. Date: May 16-17, 2016 (Monday and Tuesday) Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Locations: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 4 Remote Simulcast - NCI-Frederick - Scientific Library Training Room, Bldg 549 For more information on the Frederick simulcast, please contact:   Tracie Frederick, Technology Informationist, Scientific Library NCI at Frederick Phone: 301-846-1094 Email: frederickt@mail.nih.gov  

Workshop Agenda

Monday, May 16, 2016

Day 1 - Session 1: 9:30 am – 12:30 pm

9:30 am – 11 am

Presenter: Peter Fitzgerald, Ph.D, CCBR, OSTR, NCI

Title:  Introduction to ChIP-Seq

  • Introduction
    • Historical Perspective and Technical Variations
    • Experimental methodology
    • Comparison to ChIP-Chip
  • Data Analysis
    • Experimental Design
    • Quality Control 
    • Peak Calling (Different methodologies)
    • Major Sources of Error
    • Reasons why Experiments Fail
  • Sequence Specific Binding
    • Identification of Motifs
    • Overexpressed sequences
    • Pathways
  • Resources
    • Public Repositories
    • Literature References
    • Software listings

 

11 am – 12:30 pm

Presenter: Bong-Hyun Kim, Ph.D., CCBR, Leidos Biomed

Title: Understanding ENCODE (ENCyclopedia Of DNA Elements)

  • ENCODE Guidelines and best practices
  • Access policies and data retrieval mechanisms
  • Other public databases – Epigenome, Mouse Encode

Day 1 – Session 2

1:30 pm – 4:30 pm

Presenter: Bong-Hyun Kim, Ph.D., CCBR, Leidos Biomed

Title: ChIP-Seq Analysis Workflow

  • Data QC metrics, plots and online tools
  • Narrow Peak calling algorithms (e.g. MACS)
  • Annotation (e.g. HOMER)
  • Motif analysis (e.g. MEME)

Tuesday, May 17, 2016

Day 2 – Session 1

9:30 am –12:30 pm

Presenters: Chongzhi Zang, Ph.D., Dana-Farber Cancer Institute

                     Weiqun Peng, Ph.D., George Washington University

Title: Analyzing ChIP-seq data with SICER (Spatial Clustering for Identification of ChIP-Enriched Regions)

  • ChIP-seq overview 
  • Characteristics of histone mark ChIP-seq data
  • SICER algorithm: basic idea and model
  • Interactive SICER tutorial (on Galaxy): 
    • Data format and description
    • Run SICER with control
    • Run SICER without control
    • Run SICER for differential peak calling
    • Other useful tools

Day 2 – Session 2

1:30 pm – 4:30 pm

Presenter:  Susan Dombrowski, Ph.D, Genomatix

Title: Peeking into the Biology of your ChIP-Seq Peaks with Genomatix

  • Automated ChIP-Seq workflow:
    • Peak Calling: MACS, SICER, NGS Analyzer
Read and Peak Classification
Sequence Extraction
Transcription Factor Binding Site Overrepresentation
Motif Detection: CoreSearch

    • Meta Analysis and Integration of RNA-Seq data with ChIP-Seq peaks
    • Overview of RNA-Seq: Class Demo
    • Positional Correlation of ChIP-Seq peaks with differentially-expressed transcripts
    • Downstream Target Prediction
    • Defining gene regulatory "frameworks" in co-expressed genes

Software requirements for the computer system to run Genomatix:

1. Flash 10.1 or higher 2. Latest version of Java 3. Microsoft Excel 4. Web Browser: All web browsers are supported, but US versions are preferred and recommended.

 

Register
Organizer
BTEP
When
Mon, May 16 - Tue, May 17, 2016 -9:30 am - 4:30 pm
Where
In-Person
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq Data Analysis. There will also be a talk on ENCODE and a comprehensive discussion of topics ranging from experimental design to data visualization. This workshop will include presentations on using open source applications (MACS, HOMER, MEME, SICER) as well as commercial software (Genomatix).  To access the NCI Genomatix server, to request for a password through IT helpdesk to genomeanalyzer.nci.nih.gov. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. This workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that location. Please register only if you intend to attend the workshop. Date: May 16-17, 2016 (Monday and Tuesday) Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Locations: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 4 Remote Simulcast - NCI-Frederick - Scientific Library Training Room, Bldg 549 For more information on the Frederick simulcast, please contact:   Tracie Frederick, Technology Informationist, Scientific Library NCI at Frederick Phone: 301-846-1094 Email: frederickt@mail.nih.gov   Workshop Agenda Monday, May 16, 2016 Day 1 - Session 1: 9:30 am – 12:30 pm 9:30 am – 11 am Presenter: Peter Fitzgerald, Ph.D, CCBR, OSTR, NCI Title:  Introduction to ChIP-Seq Introduction Historical Perspective and Technical Variations Experimental methodology Comparison to ChIP-Chip Data Analysis Experimental Design Quality Control  Peak Calling (Different methodologies) Major Sources of Error Reasons why Experiments Fail Sequence Specific Binding Identification of Motifs Overexpressed sequences Pathways Resources Public Repositories Literature References Software listings   11 am – 12:30 pm Presenter: Bong-Hyun Kim, Ph.D., CCBR, Leidos Biomed Title: Understanding ENCODE (ENCyclopedia Of DNA Elements) ENCODE Guidelines and best practices Access policies and data retrieval mechanisms Other public databases – Epigenome, Mouse Encode Day 1 – Session 2 1:30 pm – 4:30 pm Presenter: Bong-Hyun Kim, Ph.D., CCBR, Leidos Biomed Title: ChIP-Seq Analysis Workflow Data QC metrics, plots and online tools Narrow Peak calling algorithms (e.g. MACS) Annotation (e.g. HOMER) Motif analysis (e.g. MEME) Tuesday, May 17, 2016 Day 2 – Session 1 9:30 am –12:30 pm Presenters: Chongzhi Zang, Ph.D., Dana-Farber Cancer Institute                      Weiqun Peng, Ph.D., George Washington University Title: Analyzing ChIP-seq data with SICER (Spatial Clustering for Identification of ChIP-Enriched Regions) ChIP-seq overview  Characteristics of histone mark ChIP-seq data SICER algorithm: basic idea and model Interactive SICER tutorial (on Galaxy):  Data format and description Run SICER with control Run SICER without control Run SICER for differential peak calling Other useful tools Day 2 – Session 2 1:30 pm – 4:30 pm Presenter:  Susan Dombrowski, Ph.D, Genomatix Title: Peeking into the Biology of your ChIP-Seq Peaks with Genomatix Automated ChIP-Seq workflow: Peak Calling: MACS, SICER, NGS Analyzer
Read and Peak Classification
Sequence Extraction
Transcription Factor Binding Site Overrepresentation
Motif Detection: CoreSearch
 Meta Analysis and Integration of RNA-Seq data with ChIP-Seq peaks Overview of RNA-Seq: Class Demo Positional Correlation of ChIP-Seq peaks with differentially-expressed transcripts Downstream Target Prediction Defining gene regulatory "frameworks" in co-expressed genes Software requirements for the computer system to run Genomatix: 1. Flash 10.1 or higher 2. Latest version of Java 3. Microsoft Excel 4. Web Browser: All web browsers are supported, but US versions are preferred and recommended.   2016-05-16 09:30:00 In-Person Peter FitzGerald (GAU),Wiequn Peng (GWU) BTEP 0 Workshop on Analysis of ChIP-Seq Data
770
Description
The 'NCBI Resources for CCR Scientists' workshop, on June 6 and June 8, 2016, will focus on teaching a wide range of resources developed within NCBI that are relevant for CCR scientists. It will include both hands-on and lecture components. Attending the workshop will enable one to understand the tools and databases available at NCBI and how to best use them for your research. A team of experts from NCBI will be presenting these sessions over ...Read More
The 'NCBI Resources for CCR Scientists' workshop, on June 6 and June 8, 2016, will focus on teaching a wide range of resources developed within NCBI that are relevant for CCR scientists. It will include both hands-on and lecture components. Attending the workshop will enable one to understand the tools and databases available at NCBI and how to best use them for your research. A team of experts from NCBI will be presenting these sessions over two separate days. NOTE: No sessions on June 7. This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please register only if you intend to attend the workshop. Locations: Live Workshop - NIH Bethesda - Bldg 10
  • FAES Room 4 (June 6th) and
  • FAES Room 3 (June 8th)
Remote Simulcast - NCI-Frederick - Scientific Library Training Room, Bldg 549 For more information on the Frederick simulcast, please contact:   Tracie Frederick, Technology Informationist, Scientific Library NCI at Frederick Phone: 301-846-1094 Email: frederickt@mail.nih.gov

WORKSHOP AGENDA

Day 1 - Monday, June 6, 2016

9:30 am - 12:30 pm: Using NCBI's Gene Expression Omnibus (GEO) to Explore Gene Expression

Instructor: Majda Valjavec-Gratian, Ph.D.

This 3-hour mainly hands-on workshop will show you how find and analyze relevant microarray and RNA-Seq datasets in NCBI's Gene Expression Omnibus resources. After learning about data concepts in GEO, you will use both precomputed analyses in GEO Profiles and the GEO 2 R tool with non-curated experiments to investigate expression of genes of interest.

12:30 - 1:30 pm Lunch Break

1:30 - 4:30 pm: A Practical Guide to NCBI BLAST

Instructor: Peter Cooper, Ph.D.

This session highlights important features and demonstrates the practical aspects of using the NCBI BLAST service, the most popular sequence similarity service in the world. You will learn about useful but under-used features of the service. These include access from the Entrez sequence databases; the new genome BLAST service quick finder; the integration and expansion of Align-2-Sequences; organism limits and other filters; re-organized databases; formatting options and downloading options; and TreeView displays. You will also learn how to use other important sequence analysis services associated with BLAST including Primer BLAST, an oligonucleotide primer designer and specificity checker; the multiple protein sequence alignment tool, COBALT; and SmartBLAST, a new tool for rapid protein identification. These aspects of BLAST provide easier access and results that are more comprehensive and easier to interpret.

Day 2 - Wednesday, June 8, 2016

9:30 am - 12:30 pm: Accessing NCBI Human Variation and Medical Genetics Resources

Instructor: Peter Cooper, Ph.D

In this session, you will learn to use and access resources associated with human sequence variations and phenotypes associated with specific human genes and phenotypes. The webinar will emphasize the Gene, MedGen and ClinVar resources to search by gene, phenotype and variant. You will learn how to map variation from dbSNP and dbVAR onto genes, transcripts, proteins, and genomic regions and how to find genetic tests in GTR. You will also gain experience using additional tools and viewers including PheGenI, a browser for genotype associations, the Variation Viewer and the 1000 Genomes Browser. These provide useful ways to search for, map and browse variants as well as upload and download data in genomic context.

12:30 - 1:30 pm Lunch Break

1:30 - 2:30 pm: Introduction to NCBI command line tools on Cloud Services: EDirect and Standalone BLAST

Instructors: Wayne Matten, Ph.D. and Peter Cooper, Ph.D.

This session provides a quick introduction to NCBI command line tools in a cloud service. You will access an NCBI Amazon Machine Image and use the EDirect commandline interface to the Entrez system and standalone BLAST (including VDB BLAST) to perform basic text and sequence similarity searches. This workshop is essential preparation for the advanced workshop on the next-generation sequence analysis, which BTEP hopes to offer later this year.

 

2:30 - 4:30 pm: Sequence Read Archive (SRA), including hands-on with SRA-Blast in the AWS-Cloud

Instructor: Ben Busby, Ph.D.

This final session will describe how NCBI has enhanced several of its genomics resources in the last several months and how some of its public databases can be queried for computational biology and clinical questions, particularly those involving cancer biology.  The improved access to the underlying genomic data in several ways will be showcased, including making it possible to BLAST into our large genomic databases, and how to use popular genomics tools such as GATK and HISAT2 directly with SRA.  There will be a demo and hands-on exercise on how to use SRA-search and SRA-BLAST.

 

 

Register
Organizer
BTEP
When
Mon, Jun 06 - Wed, Jun 08, 2016 -9:30 am - 4:30 pm
Where
In-Person
The 'NCBI Resources for CCR Scientists' workshop, on June 6 and June 8, 2016, will focus on teaching a wide range of resources developed within NCBI that are relevant for CCR scientists. It will include both hands-on and lecture components. Attending the workshop will enable one to understand the tools and databases available at NCBI and how to best use them for your research. A team of experts from NCBI will be presenting these sessions over two separate days. NOTE: No sessions on June 7. This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please register only if you intend to attend the workshop. Locations: Live Workshop - NIH Bethesda - Bldg 10 FAES Room 4 (June 6th) and FAES Room 3 (June 8th) Remote Simulcast - NCI-Frederick - Scientific Library Training Room, Bldg 549 For more information on the Frederick simulcast, please contact:   Tracie Frederick, Technology Informationist, Scientific Library NCI at Frederick Phone: 301-846-1094 Email: frederickt@mail.nih.gov WORKSHOP AGENDA Day 1 - Monday, June 6, 2016 9:30 am - 12:30 pm: Using NCBI's Gene Expression Omnibus (GEO) to Explore Gene Expression Instructor: Majda Valjavec-Gratian, Ph.D. This 3-hour mainly hands-on workshop will show you how find and analyze relevant microarray and RNA-Seq datasets in NCBI's Gene Expression Omnibus resources. After learning about data concepts in GEO, you will use both precomputed analyses in GEO Profiles and the GEO 2 R tool with non-curated experiments to investigate expression of genes of interest. 12:30 - 1:30 pm Lunch Break 1:30 - 4:30 pm: A Practical Guide to NCBI BLAST Instructor: Peter Cooper, Ph.D. This session highlights important features and demonstrates the practical aspects of using the NCBI BLAST service, the most popular sequence similarity service in the world. You will learn about useful but under-used features of the service. These include access from the Entrez sequence databases; the new genome BLAST service quick finder; the integration and expansion of Align-2-Sequences; organism limits and other filters; re-organized databases; formatting options and downloading options; and TreeView displays. You will also learn how to use other important sequence analysis services associated with BLAST including Primer BLAST, an oligonucleotide primer designer and specificity checker; the multiple protein sequence alignment tool, COBALT; and SmartBLAST, a new tool for rapid protein identification. These aspects of BLAST provide easier access and results that are more comprehensive and easier to interpret. Day 2 - Wednesday, June 8, 2016 9:30 am - 12:30 pm: Accessing NCBI Human Variation and Medical Genetics Resources Instructor: Peter Cooper, Ph.D In this session, you will learn to use and access resources associated with human sequence variations and phenotypes associated with specific human genes and phenotypes. The webinar will emphasize the Gene, MedGen and ClinVar resources to search by gene, phenotype and variant. You will learn how to map variation from dbSNP and dbVAR onto genes, transcripts, proteins, and genomic regions and how to find genetic tests in GTR. You will also gain experience using additional tools and viewers including PheGenI, a browser for genotype associations, the Variation Viewer and the 1000 Genomes Browser. These provide useful ways to search for, map and browse variants as well as upload and download data in genomic context. 12:30 - 1:30 pm Lunch Break 1:30 - 2:30 pm: Introduction to NCBI command line tools on Cloud Services: EDirect and Standalone BLAST Instructors: Wayne Matten, Ph.D. and Peter Cooper, Ph.D. This session provides a quick introduction to NCBI command line tools in a cloud service. You will access an NCBI Amazon Machine Image and use the EDirect commandline interface to the Entrez system and standalone BLAST (including VDB BLAST) to perform basic text and sequence similarity searches. This workshop is essential preparation for the advanced workshop on the next-generation sequence analysis, which BTEP hopes to offer later this year.   2:30 - 4:30 pm: Sequence Read Archive (SRA), including hands-on with SRA-Blast in the AWS-Cloud Instructor: Ben Busby, Ph.D. This final session will describe how NCBI has enhanced several of its genomics resources in the last several months and how some of its public databases can be queried for computational biology and clinical questions, particularly those involving cancer biology.  The improved access to the underlying genomic data in several ways will be showcased, including making it possible to BLAST into our large genomic databases, and how to use popular genomics tools such as GATK and HISAT2 directly with SRA.  There will be a demo and hands-on exercise on how to use SRA-search and SRA-BLAST.     2016-06-06 09:30:00 In-Person Peter Cooper (NCBI),Wayne Matten (Division of NLM) BTEP 0 BTEP Workshop on NCBI Resources for CCR Scientists
769
Description
This 2-day workshop, including a BYOD (Bring Your Own Data) exercise, will provide an introduction and hands-on training for Cytoscape, which is an open source software platform for visualizing  networks and integrating these networks with annotations, gene expression profiles and other state data.

With over 800 publications citing Cytoscape, it has become the primary tool for visualization of networks, including biological pathways, protein-protein interaction data, protein similarity data, cell-cell, and even residue-residue interactions. ...Read More

This 2-day workshop, including a BYOD (Bring Your Own Data) exercise, will provide an introduction and hands-on training for Cytoscape, which is an open source software platform for visualizing  networks and integrating these networks with annotations, gene expression profiles and other state data.

With over 800 publications citing Cytoscape, it has become the primary tool for visualization of networks, including biological pathways, protein-protein interaction data, protein similarity data, cell-cell, and even residue-residue interactions. In this workshop, you'll learn the basics of network analysis and visualization, utilizing Cytoscape to import network data from public resources such as STRING, Reactome, or Wikipathways, creating input files with network data in Excel format. You will also be able to augment network data with your own experimental results or data downloaded from sites such as cBioPortal. Once imported, you'll learn how to analyze the results using a variety of Cytoscape "apps" and visualize the data in a form useful for exploring the data or for producing meaningful images for presentations or publications. This workshop includes significant opportunity for hands-on exploration of Cytoscape and for working with your own data, so bring in Excel spreadsheets or CSV files with your experimental (for example, spreadsheet with data from microarray) or downloaded data that you would like to see visualized in a network. The Cytoscape core download provides a basic set of features for data integration, analysis, and visualization. Additional features are available as Apps (formerly called Plugins). The App Store has a collections of apps, which are groupings of apps useful for particular analyses. Apps are available for network and molecular profiling analyses, new layouts, additional file format support, scripting, and connection with databases.

The main website is here: http://www.cytoscape.org/.

An example of a recent publication is here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906568/ [Yin, Ji-Gang et al. “Gene Expression Profiling Analysis of Ovarian Cancer.” Oncology Letters 12.1: 405–412. 16 Aug. 2016]

You can review the complete list of publications here: http://cytoscape-publications.tumblr.com/

Scooter Morris, a member of the Cytoscape Team from USCF, will be here to provide training for this workshop, which promises to be very exciting and useful for the CCR scientific community.

Important: Attendees are required to pre-load the necessary software (listed below) on the laptop (PC, Mac) they will bring to the workshop. If you plan to attend, please try to do this ahead of the workshop. Depending on your level of computer privileges, you may need to submit a request to the NCI Service Desk (https://service.cancer.gov) to complete installation of JAVA, Cytoscape and the Omics App.

Listed below are the links to download the requirements, in this particular order:

[1] Latest JAVA (Version 8) for your platform: http://www.oracle.com/technetwork/java/javase/downloads/jre8-downloads-2...

[2] Latest version of Cytoscape 3.4.0: http://www.cytoscape.org/download-platforms.html

  • Please choose the correct platform (Windows, MacOS, Linux).

[3] Link to download the Omics ‘App’ for workshop: http://apps.cytoscape.org/apps/omicsanalysiscollection

  • This can also be done after opening Cytoscape: Click on Apps --> Apps Manager --> OmicsAnalysisCollection --> Install

Date: September 1-2, 2016 (Thursday and Friday)

Time and Location:

September 1 - 9:30 am - 4:00 pm; Bldg 10, FAES Classrooms 1 & 2

September 2 - 9:30 am - 12:30 pm; Bldg 10, FAES Classrooms 6 & 7

Workshop Agenda

Day 1: 9:30 am – 12:30 pm

  • Introductions and setup (15 minutes)
  • Biological Networks (60 minutes): Understanding the terminology and basis of network theory as well as approaches for network construction
    • Network Taxonomy
    • Analytical Approaches
    • Visualization

Break

  • Introduction to Cytoscape [Hands-on] (60 minutes): Fundamental aspects of the tool and walk through the analysis steps
    • Data model
    • Basic user interface
    • Visual Styles
    • Apps and the App Store
  • App Demos and Use Cases (30 minutes)
  • Clustering
  • Over-representation analysis

Lunch Break

Day 1: 1:30 pm – 4:30 pm

  • Introduction to Cytoscape II (45 minutes): Perform different demos that are built into the tool and have attendees do hands-on tutorials
  • Tips & Tricks (15 minutes)
  • Working with Data (30 minutes)
    • Importing networks (STRING)
    • Importing networks (Excel)
    • Adding data to networks

Break

  • Analysis with Microarray Data (30 minutes)
  • In-depth Examples with Your Data
  • Questions and Answers

Day 2: 9:30 am -12:30 pm

  • Refresher for topics covered on Day 1
  • From Public Data to Cytoscape
  • “Bring Your Own Data" (BYOD): In-depth analysis of user data
    • Specific cases based on user-driven requests
    • Two excel files are available below as examples
      • TCGA Breast Cancer Data from 2015 - both mRNA and protein expression data

 

Register
Organizer
BTEP
When
Thu, Sep 01 - Fri, Sep 02, 2016 -9:30 am - 12:30 pm
Where
NIH Bldg 10, FAES Room 1/2 (9/1/16) & Room 6/7 (9/2/16)
This 2-day workshop, including a BYOD (Bring Your Own Data) exercise, will provide an introduction and hands-on training for Cytoscape, which is an open source software platform for visualizing  networks and integrating these networks with annotations, gene expression profiles and other state data. With over 800 publications citing Cytoscape, it has become the primary tool for visualization of networks, including biological pathways, protein-protein interaction data, protein similarity data, cell-cell, and even residue-residue interactions. In this workshop, you'll learn the basics of network analysis and visualization, utilizing Cytoscape to import network data from public resources such as STRING, Reactome, or Wikipathways, creating input files with network data in Excel format. You will also be able to augment network data with your own experimental results or data downloaded from sites such as cBioPortal. Once imported, you'll learn how to analyze the results using a variety of Cytoscape "apps" and visualize the data in a form useful for exploring the data or for producing meaningful images for presentations or publications. This workshop includes significant opportunity for hands-on exploration of Cytoscape and for working with your own data, so bring in Excel spreadsheets or CSV files with your experimental (for example, spreadsheet with data from microarray) or downloaded data that you would like to see visualized in a network. The Cytoscape core download provides a basic set of features for data integration, analysis, and visualization. Additional features are available as Apps (formerly called Plugins). The App Store has a collections of apps, which are groupings of apps useful for particular analyses. Apps are available for network and molecular profiling analyses, new layouts, additional file format support, scripting, and connection with databases. The main website is here: http://www.cytoscape.org/. An example of a recent publication is here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906568/ [Yin, Ji-Gang et al. “Gene Expression Profiling Analysis of Ovarian Cancer.” Oncology Letters 12.1: 405–412. 16 Aug. 2016] You can review the complete list of publications here: http://cytoscape-publications.tumblr.com/ Scooter Morris, a member of the Cytoscape Team from USCF, will be here to provide training for this workshop, which promises to be very exciting and useful for the CCR scientific community. Important: Attendees are required to pre-load the necessary software (listed below) on the laptop (PC, Mac) they will bring to the workshop. If you plan to attend, please try to do this ahead of the workshop. Depending on your level of computer privileges, you may need to submit a request to the NCI Service Desk (https://service.cancer.gov) to complete installation of JAVA, Cytoscape and the Omics App. Listed below are the links to download the requirements, in this particular order: [1] Latest JAVA (Version 8) for your platform: http://www.oracle.com/technetwork/java/javase/downloads/jre8-downloads-2... Note for 64 bit Windows users – please download the *64* bit Java version, as Oracle still downloads the 32-bit version by default. The *64* bit version is at: http://javadl.oracle.com/webapps/download/AutoDL?BundleId=211999 [2] Latest version of Cytoscape 3.4.0: http://www.cytoscape.org/download-platforms.html Please choose the correct platform (Windows, MacOS, Linux). [3] Link to download the Omics ‘App’ for workshop: http://apps.cytoscape.org/apps/omicsanalysiscollection This can also be done after opening Cytoscape: Click on Apps --> Apps Manager --> OmicsAnalysisCollection --> Install Date: September 1-2, 2016 (Thursday and Friday) Time and Location: September 1 - 9:30 am - 4:00 pm; Bldg 10, FAES Classrooms 1 & 2 September 2 - 9:30 am - 12:30 pm; Bldg 10, FAES Classrooms 6 & 7 Workshop Agenda Day 1: 9:30 am – 12:30 pm Introductions and setup (15 minutes) Biological Networks (60 minutes): Understanding the terminology and basis of network theory as well as approaches for network construction Network Taxonomy Analytical Approaches Visualization Break Introduction to Cytoscape [Hands-on] (60 minutes): Fundamental aspects of the tool and walk through the analysis steps Data model Basic user interface Visual Styles Apps and the App Store App Demos and Use Cases (30 minutes) Clustering Over-representation analysis Lunch Break Day 1: 1:30 pm – 4:30 pm Introduction to Cytoscape II (45 minutes): Perform different demos that are built into the tool and have attendees do hands-on tutorials Tips & Tricks (15 minutes) Working with Data (30 minutes) Importing networks (STRING) Importing networks (Excel) Adding data to networks Break Analysis with Microarray Data (30 minutes) In-depth Examples with Your Data Questions and Answers Day 2: 9:30 am -12:30 pm Refresher for topics covered on Day 1 From Public Data to Cytoscape “Bring Your Own Data" (BYOD): In-depth analysis of user data Specific cases based on user-driven requests Two excel files are available below as examples TCGA Breast Cancer Data from 2015 - both mRNA and protein expression data   2016-09-01 09:30:00 NIH Bldg 10, FAES Room 1/2 (9/1/16) & Room 6/7 (9/2/16) In-Person Scooter Morris PhD. (Cytoscape Team) BTEP 0 Visualizing Complex Networks using Cytoscape
768
Description
The maximum number of registrations (25) allowed has been reached for this workshop. You will be put on the waitlist and informed if any cancellations do occur. You are welcome to come on the morning of the workshop and be seated on a first-come, first-serve basis, if there are any last-minute dropouts.

Learn the fundamentals about microarray technology and designing experiments for your research using this technology. After attending this 2-day workshop, you ...Read More

The maximum number of registrations (25) allowed has been reached for this workshop. You will be put on the waitlist and informed if any cancellations do occur. You are welcome to come on the morning of the workshop and be seated on a first-come, first-serve basis, if there are any last-minute dropouts.

Learn the fundamentals about microarray technology and designing experiments for your research using this technology. After attending this 2-day workshop, you will understand the best practices to perform analysis of gene expression data from microarrays, and know how do that using GEO2R (open source tool) and Partek Genomics Suite (NCI-licensed commercial tool). As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using multiple sets of tools and applications. A significant portion of the final session on the second day is dedicated to allow attendees to independently work on a publicly available dataset and implement the analysis workflow using the knowledge gained over the course of the workshop.

PLEASE NOTE: This workshop is a BYOC (Bring Your Own LapTop Computer) class, and requires installation of Partek Genomics Suite on your laptop ahead of Day  2 of the workshop. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer, please indicate such in the Comment section on the registration form.

Workshop Agenda

Day 1 – October 3, 2016 (Monday)

9:30 am – 10 am                 Welcome and Workshop Overview (Anand S. Merchant, Ph.D. – CCBR)

10:00 am - 12:30 pm           Introductory Lecture: Microarray Technology and Data Analysis (Maggie Cam, PhD – CCR)

Introduction

  • Historical Perspective
  • Microarray Technologies, Sample Processing Methods
  • Microarray comparisons to RNA-Seq

Data Analysis

  • Experimental Design
  • QC methods
  • Preprocessing: Normalization and low level analysis algorithms

Statistical Analysis

  • Common statistical models used for analysis of microarray data
  • Examples of blocking
  • Batch effects and removal methods

11:15 – 11:30 am BREAK

Visualization and Clustering

  • Volcano Plot
  • Principal Components Analysis
  • Hierarchical Clustering
  • K-means Clustering

Validation and Downstream Analysis

  • Validation methods
  • Major Software applications
  • Public Repositories of Microarray Data
  • Gene Ontology Enrichment and Pathway analysis tools

12:30 – 1:30 pm LUNCH BREAK

1:30 - 2:45 pm                  GEO2R: Open-source web tool for querying publicly available datasets (Parthav Jailwala, MSc- CCBR)

GEO2R is an interactive web tool that allows users to compare two or more groups of samples in a GEO Series in order to identify genes that are differentially expressed across experimental conditions. GEO2R performs comparisons on original submitter-supplied processed data tables using the GEOquery and limma R packages from the Bioconductor project. Bioconductor is an open source software project based on the R programming language that provides tools for the analysis of high-throughput genomic data. The GEOquery R package parses GEO data into R data structures that can be used by other R packages. The limma (Linear Models for Microarray Analysis) R package has emerged as one of the most widely used statistical tests for identifying differentially expressed genes. It handles a wide range of experimental designs and data types and applies multiple-testing corrections on P-values to help correct for the occurrence of false positives. Thus, GEO2R provides a simple interface that allows users to perform R statistical analysis without command line expertise.

Lecture and Hands-on session for GEO2R

  • Background on GEO datasets
  • What is GEO2R and how can it help you
  • How to use GEO2R
  • Options and features
  • Limitations and caveat
  • Hands-on exercise

2:45 - 3:00 pm – BREAK

3:00 – 4:00 pm                  CCBR Microarray Data Analysis Pipeline Demonstration (Fathi Elloumi, Ph.D., CCBR)

Attendees will learn about the complete workflow implemented by CCBR for microarray data analysis. The speaker will demonstrate the practical steps based on the theoretical concepts discussed in the morning, and will discuss topics that include:

  • Objectives of and requirements for pipeline
  • Input data types, files and format
  • Initial quality checks of the data
  • Visuals of ‘good’ and ‘bad’ data
  • Differential expression analysis
  • Statistical parameters
  • Downstream enrichment analysis
  • Planned future enhancements

Day 2 – October 4, 2016 (Tuesday)

9:30 am - 12:30 pm          Gene Expression Workflow with Partek Genomics Suite (Eric Seiser, PhD – FAS, Partek)

The training will include a guided analysis of an Affymetrix gene expression data set to showcase and familiarize users with the Gene Expression analysis workflow covering the topics listed below.  Following this analysis, attendees will be presented with the task of obtaining a data set from the NCBI Gene Expression Omnibus (GEO) and running an independent analysis of the data to attempt to replicate the findings of the publication.  Attendees will be given a list of analysis goals and will have the opportunity to ask for help from the instructor as they work through this analysis.  The goal of this hands on time will be to provide attendees with experience analyzing real world data independently.         

  • Import data – Affymetrix CEL files
  • Perform QA/QC of imported data
  • Exploratory data analysis – Principal Component Analysis (PCA)
  • Detect differential expression (ANOVA) – two factor analysis
  • Gene list creation (Venn diagram creation and list overlap)
  • Visualization (PCA, histogram, box plot, dot plot, volcano plot, heatmap etc.)       

12:30 – 1:30 pm LUNCH BREAK      

1:30 - 4:00 pm           Hands-on Gene Expression Data Analysis with PGS - Continued

  • Additional features (1:30 – 2:30 pm)
    • Biological interpretation – through use of Gene Ontology and KEGG pathways
    • Integration with other data – combining gene and miRNA expression data
    • Other topics - Batch Effect Removal, Survival analysis
  • Independent hands-on exercise (with GEO dataset) for attendees (2:30 – 4:00 pm)
    • Download and Import data
    • Use PCA to identify factors for statistical modeling
    • Identify deferentially expressed genes
    • Generate a heatmap
    • Find important pathways

 

Register
Organizer
BTEP
When
Mon, Oct 03 - Tue, Oct 04, 2016 -9:30 am - 4:00 pm
Where
NIH Bldg 10, FAES Room 4 (B1C205)
The maximum number of registrations (25) allowed has been reached for this workshop. You will be put on the waitlist and informed if any cancellations do occur. You are welcome to come on the morning of the workshop and be seated on a first-come, first-serve basis, if there are any last-minute dropouts. Learn the fundamentals about microarray technology and designing experiments for your research using this technology. After attending this 2-day workshop, you will understand the best practices to perform analysis of gene expression data from microarrays, and know how do that using GEO2R (open source tool) and Partek Genomics Suite (NCI-licensed commercial tool). As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, batch correction, statistics, and how to extract biological meaning from the results using multiple sets of tools and applications. A significant portion of the final session on the second day is dedicated to allow attendees to independently work on a publicly available dataset and implement the analysis workflow using the knowledge gained over the course of the workshop. PLEASE NOTE: This workshop is a BYOC (Bring Your Own LapTop Computer) class, and requires installation of Partek Genomics Suite on your laptop ahead of Day  2 of the workshop. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer, please indicate such in the Comment section on the registration form. Workshop Agenda Day 1 – October 3, 2016 (Monday) 9:30 am – 10 am                 Welcome and Workshop Overview (Anand S. Merchant, Ph.D. – CCBR) 10:00 am - 12:30 pm           Introductory Lecture: Microarray Technology and Data Analysis (Maggie Cam, PhD – CCR) Introduction Historical Perspective Microarray Technologies, Sample Processing Methods Microarray comparisons to RNA-Seq Data Analysis Experimental Design QC methods Preprocessing: Normalization and low level analysis algorithms Statistical Analysis Common statistical models used for analysis of microarray data Examples of blocking Batch effects and removal methods 11:15 – 11:30 am BREAK Visualization and Clustering Volcano Plot Principal Components Analysis Hierarchical Clustering K-means Clustering Validation and Downstream Analysis Validation methods Major Software applications Public Repositories of Microarray Data Gene Ontology Enrichment and Pathway analysis tools 12:30 – 1:30 pm LUNCH BREAK 1:30 - 2:45 pm                  GEO2R: Open-source web tool for querying publicly available datasets (Parthav Jailwala, MSc- CCBR) GEO2R is an interactive web tool that allows users to compare two or more groups of samples in a GEO Series in order to identify genes that are differentially expressed across experimental conditions. GEO2R performs comparisons on original submitter-supplied processed data tables using the GEOquery and limma R packages from the Bioconductor project. Bioconductor is an open source software project based on the R programming language that provides tools for the analysis of high-throughput genomic data. The GEOquery R package parses GEO data into R data structures that can be used by other R packages. The limma (Linear Models for Microarray Analysis) R package has emerged as one of the most widely used statistical tests for identifying differentially expressed genes. It handles a wide range of experimental designs and data types and applies multiple-testing corrections on P-values to help correct for the occurrence of false positives. Thus, GEO2R provides a simple interface that allows users to perform R statistical analysis without command line expertise. Lecture and Hands-on session for GEO2R Background on GEO datasets What is GEO2R and how can it help you How to use GEO2R Options and features Limitations and caveat Hands-on exercise 2:45 - 3:00 pm – BREAK 3:00 – 4:00 pm                  CCBR Microarray Data Analysis Pipeline Demonstration (Fathi Elloumi, Ph.D., CCBR) Attendees will learn about the complete workflow implemented by CCBR for microarray data analysis. The speaker will demonstrate the practical steps based on the theoretical concepts discussed in the morning, and will discuss topics that include: Objectives of and requirements for pipeline Input data types, files and format Initial quality checks of the data Visuals of ‘good’ and ‘bad’ data Differential expression analysis Statistical parameters Downstream enrichment analysis Planned future enhancements Day 2 – October 4, 2016 (Tuesday) 9:30 am - 12:30 pm          Gene Expression Workflow with Partek Genomics Suite (Eric Seiser, PhD – FAS, Partek) The training will include a guided analysis of an Affymetrix gene expression data set to showcase and familiarize users with the Gene Expression analysis workflow covering the topics listed below.  Following this analysis, attendees will be presented with the task of obtaining a data set from the NCBI Gene Expression Omnibus (GEO) and running an independent analysis of the data to attempt to replicate the findings of the publication.  Attendees will be given a list of analysis goals and will have the opportunity to ask for help from the instructor as they work through this analysis.  The goal of this hands on time will be to provide attendees with experience analyzing real world data independently.          Import data – Affymetrix CEL files Perform QA/QC of imported data Exploratory data analysis – Principal Component Analysis (PCA) Detect differential expression (ANOVA) – two factor analysis Gene list creation (Venn diagram creation and list overlap) Visualization (PCA, histogram, box plot, dot plot, volcano plot, heatmap etc.)        12:30 – 1:30 pm LUNCH BREAK       1:30 - 4:00 pm           Hands-on Gene Expression Data Analysis with PGS - Continued Additional features (1:30 – 2:30 pm) Biological interpretation – through use of Gene Ontology and KEGG pathways Integration with other data – combining gene and miRNA expression data Other topics - Batch Effect Removal, Survival analysis Independent hands-on exercise (with GEO dataset) for attendees (2:30 – 4:00 pm) Download and Import data Use PCA to identify factors for statistical modeling Identify deferentially expressed genes Generate a heatmap Find important pathways   2016-10-03 09:30:00 NIH Bldg 10, FAES Room 4 (B1C205) In-Person Maggie Cam (NCI CCBR),Parthav Jailwala (CCBR) BTEP 0 Microarrays: Basics, Best Practices, and Analysis Tools (2-day)
767
Description

ABOUT THE COURSE

R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. It is a console application that compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. It is available for download through CRAN, which is a network ...Read More

ABOUT THE COURSE

R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. It is a console application that compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. It is available for download through CRAN, which is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Bioconductor uses the R statistical programming language, and is open source and open development as well. It provides tools for the analysis and comprehension of high-throughput genomic data. The course will include multiple, short hands-on exercises spread out throughout the two lecture sessions.

PLEASE NOTE: This 1-day workshop is a BYOC (Bring your own Laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

REQUIRED INSTALLATION:  Students who bring their own laptops should ensure that R v3.3.1 and Bioconductor v3.4, is installed on their computers. In addition, several R packages (listed below) will be used which must be installed prior to the course. Please follow the instructions mentioned further below complete installation of these packages required for the workshop.

R Installation

The R program and instructions for its installation can be found by clicking the link provided below.

Please choose the correct environment - Linux, Mac OSX, or Windows - that is applicable to your system.

https://cran.r-project.org/

Bioconductor and Bioconductor Package Installation

Complete instructions for the installation of the basic and additional Bioconductor packages are found here:

http://www.bioconductor.org/install/

In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class:

Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene  

Command-line instructions for Bioconductor and packages: The following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above.

# First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene")) RStudio Installation (not required for workshop, but some users may find it useful)

Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier.  It offers quite an array of functions that you may still find useful and it is well worth a look. Install the "€œDesktop, Open Source Edition"€:

http://www.rstudio.com/products/RStudio/#Desk

Workshop Agenda

Morning Session - 9:30 am -12:30 pm

Introduction to R

  • The R environment
    • Starting an R Session, Setting Options
    • Listing Variables, Editing Commands, Using the R History
    • Getting Help on an R Function
    • Logging a Session to a File
    • Running External R Code
    • Installing and Loading Packages
    • Ending a Session, Saving Your Work
  • The Elements of R
    • Numeric
    • Character
    • Logical
    • Missing Values
  • R Data Structures
    • Vectors
    • Matrices
    • Lists
    • Data.Frames
    • Factors
    • Functions
    • Other Complex Structures
  • Procedures
    • Reading and Writing Data
    • Exploring and Summarizing Data
    • Dealing with Missing Data
    • Restructuring Data
    • Relabeling Data
    • Subsetting Data
    • Operating on Rows or Columns of Data
    • Saving R Objects for Later Use
    • Graphing Data
    • Simple Statistical Tests
    • Example: A Simple Analysis of Probe Intensity Data
  • Project: Creating a Graphical Function in 4 Easy Steps
    • Step 1: Create a Heatmap of Gene Expression Data
    • Step 2: Package Heatmap as a Function
    • Step 3: Add some Custom Formatting
    • Step 4: Save for Future Use and – Voila, You Have Created your own Heatmap Library!
12:30 - 1:00 pm   LUNCH BREAK

Afternoon Session - 1:00 - 4:00 pm

Introduction to Bioconductor

  • Installing Bioconductor
  • An Overview of Bioconductor Packages
  • Fundamental Packages
    • Biobase: the Foundation
    • Biostrings: A Representation of Biological Sequences
    • BSgenome: A Representation of Complete Genomic Sequences
    • GenomicRanges: Manipulation of Genomic Intervals
    • GenomicFeatures: Manipulation of Genomic Features
    • GenomicAlgnments: Manipulation of Short Genomic Alignments
  • Two Fundamental Structures to Contain Experiment Data
    • The ExpressionSet for Array Data
      • Constructing an ExpressionSet
      • Analyzing an ExpressionSet
    • The SummarizedExperiment for NGS Sequence Data
      • Constructing a SummarizedExperiment
      • Analyzing a SummarizedExperiment
Register
Organizer
BTEP
When
Tue, Nov 22, 2016 - 9:30 am - 4:00 pm
Where
NIH Bldg 10 FAES Room 4 (B1C205)
ABOUT THE COURSE R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. It is a console application that compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. It is available for download through CRAN, which is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Bioconductor uses the R statistical programming language, and is open source and open development as well. It provides tools for the analysis and comprehension of high-throughput genomic data. The course will include multiple, short hands-on exercises spread out throughout the two lecture sessions. PLEASE NOTE: This 1-day workshop is a BYOC (Bring your own Laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. REQUIRED INSTALLATION:  Students who bring their own laptops should ensure that R v3.3.1 and Bioconductor v3.4, is installed on their computers. In addition, several R packages (listed below) will be used which must be installed prior to the course. Please follow the instructions mentioned further below complete installation of these packages required for the workshop. R Installation The R program and instructions for its installation can be found by clicking the link provided below. Please choose the correct environment - Linux, Mac OSX, or Windows - that is applicable to your system. https://cran.r-project.org/ Bioconductor and Bioconductor Package Installation Complete instructions for the installation of the basic and additional Bioconductor packages are found here: http://www.bioconductor.org/install/ In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class: Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene   Command-line instructions for Bioconductor and packages: The following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above. # First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene")) RStudio Installation (not required for workshop, but some users may find it useful) Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier.  It offers quite an array of functions that you may still find useful and it is well worth a look. Install the "€œDesktop, Open Source Edition"€: http://www.rstudio.com/products/RStudio/#Desk Workshop Agenda Morning Session - 9:30 am -12:30 pm Introduction to R The R environment Starting an R Session, Setting Options Listing Variables, Editing Commands, Using the R History Getting Help on an R Function Logging a Session to a File Running External R Code Installing and Loading Packages Ending a Session, Saving Your Work The Elements of R Numeric Character Logical Missing Values R Data Structures Vectors Matrices Lists Data.Frames Factors Functions Other Complex Structures Procedures Reading and Writing Data Exploring and Summarizing Data Dealing with Missing Data Restructuring Data Relabeling Data Subsetting Data Operating on Rows or Columns of Data Saving R Objects for Later Use Graphing Data Simple Statistical Tests Example: A Simple Analysis of Probe Intensity Data Project: Creating a Graphical Function in 4 Easy Steps Step 1: Create a Heatmap of Gene Expression Data Step 2: Package Heatmap as a Function Step 3: Add some Custom Formatting Step 4: Save for Future Use and – Voila, You Have Created your own Heatmap Library! 12:30 - 1:00 pm   LUNCH BREAK Afternoon Session - 1:00 - 4:00 pm Introduction to Bioconductor Installing Bioconductor An Overview of Bioconductor Packages Fundamental Packages Biobase: the Foundation Biostrings: A Representation of Biological Sequences BSgenome: A Representation of Complete Genomic Sequences GenomicRanges: Manipulation of Genomic Intervals GenomicFeatures: Manipulation of Genomic Features GenomicAlgnments: Manipulation of Short Genomic Alignments Two Fundamental Structures to Contain Experiment Data The ExpressionSet for Array Data Constructing an ExpressionSet Analyzing an ExpressionSet The SummarizedExperiment for NGS Sequence Data Constructing a SummarizedExperiment Analyzing a SummarizedExperiment 2016-11-22 09:30:00 NIH Bldg 10 FAES Room 4 (B1C205) In-Person David Wheeler PhD. (Laboratory of Biochemistry and Molecular Biology CCR NCI) BTEP 0 R/Bioconductor Basics Workshop
765
Description

Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of publicly available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding ...Read More

Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of publicly available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding of the interplay between spatially related biological elements. These visualization tools are relevant whether one is interested in expression analysis (RNA-Seq), variant detection (Exome-Seq), protein binding (ChIP-Seq), or any other genome scale data.

This seminar will provide an overview of the available tools, and highlight useful features of some of the more popular genome browsers. Highlighted topics will include:

  • An overview of the tools available - their strength and weaknesses and where to find them
  • An overview of the different classes of browsers
  • An discussion of the relevant file types accepted by most browsers
  • Details on how to navigate the UCSC Genome Browser
  • How to integrate your own or publically available data into browsers
  • How to capture and share specific views of data
  • How to get more detailed views of your data with tools like IGB and IGV and how to integrate them into other tools
  • and more....
Register
Organizer
BTEP
When
Tue, Dec 13, 2016 - 2:30 pm - 4:00 pm
Where
In-Person
Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of publicly available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding of the interplay between spatially related biological elements. These visualization tools are relevant whether one is interested in expression analysis (RNA-Seq), variant detection (Exome-Seq), protein binding (ChIP-Seq), or any other genome scale data. This seminar will provide an overview of the available tools, and highlight useful features of some of the more popular genome browsers. Highlighted topics will include: An overview of the tools available - their strength and weaknesses and where to find them An overview of the different classes of browsers An discussion of the relevant file types accepted by most browsers Details on how to navigate the UCSC Genome Browser How to integrate your own or publically available data into browsers How to capture and share specific views of data How to get more detailed views of your data with tools like IGB and IGV and how to integrate them into other tools and more.... 2016-12-13 14:30:00 In-Person Peter FitzGerald (GAU) BTEP 0 Genome Browsers - Tools for Visualizing Genomic Scale Data
766
Description
iPathwayGuide is a gene and protein expression analysis tool that uses a systems biology approach to identify significantly impacted pathways, gene ontology terms, diseases, and predicted microRNAs based on the given gene or protein differential expression signature. It uses an advanced pathway analysis approach that considers the role, positioning, and relationships of a given gene within a pathway, resulting in a significantly fewer false positives associated with pathway analysis. More information can be found ...Read More
iPathwayGuide is a gene and protein expression analysis tool that uses a systems biology approach to identify significantly impacted pathways, gene ontology terms, diseases, and predicted microRNAs based on the given gene or protein differential expression signature. It uses an advanced pathway analysis approach that considers the role, positioning, and relationships of a given gene within a pathway, resulting in a significantly fewer false positives associated with pathway analysis. More information can be found at this link: http://www.advaitabio.com/ipathwayguide.html PLEASE NOTE: This 1-day workshop is a BYOC (Bring your own Laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. The software is designed to work with all the latest major browser platforms: Google Chrome, Mozilla Firefox, Apple Safari (Mac only, iOS not supported yet), as well as Microsoft Internet Explorer 11 (though some image download capabilities may not function​).

WORKSHOP AGENDA - Monday, Dec 19, 2016

Morning Session

9:30 am -10:55 am                   

Introduction to iPathwayGuide

  • Overview: modules, User Interface (UI), cloud access, sharing
  • Scoring Method: Impact Analysis, types of evidence, multiple correction
  • Advanced features: printable report & meta-analysis

Uploading data: lecture & follow along w/ demo

  • Data Analysis Pipeline
  • Supported organisms and file types

  • Uploading CEL files
  • Custom format: gene symbol, log-fold change, p-values
  • Thresholds for DE
  • TRY: Upload GEO2R file
  • TRY: Fill in Title & Description, Contrast Names
  • TRY: Choose thresholds for Differential Expression

11:05 am -12:30 pm      Follow along w/ demo

Dataset 1: NanoString Pan-Cancer panel, Breast Cancer samples (human primary tissue)

  • Impact Analysis

  • Dataset background
  • TRY: Accept share
  • Summary Page
  • DE Genes
  • Pathways
  • Printable report
  • Q&A

12:30 - 1:00 pm   LUNCH BREAK

Afternoon session

1:00 pm - 2:25 pm      Hands-on practice

Dataset 2: Genome-wide analysis of gene expression regulated by VRK1 kinase in cancer cell lines (GSE86942)

  • miRNA Inference
  • Gene Ontologies
  • Diseases
  • TRY: Share Report
  • TRY: Generate Meta-analysis, Identify biomarkers, Export Results
  • Q&A

2:35 pm - 4:00 pm       Data Analysis Exercise

Dataset 3: Anaplastic Large Cell Lymphoma of Childhood (GSE78513, human primary tissue)

  • TRY: Accept share
  • Send a request for help/ feedback
  • Generate meta-analysis 
    • Select comparable contrasts
  • Identify putative mechanisms on a high impact pathway
  • Identify probable miRNAs
  • Identify relevant GO Terms
  • Identify relevant diseases
  • Generate printable report
  • Design a meta-analysis study
    • Which combination of regions is appropriate?
    • Which modules are informative?
    • Results: view rank diagram, find biomarkers
    • Export pertinent figures & tables
  • Share via email
  • Report findings to class
  • Q&A
Register
Organizer
BTEP
When
Mon, Dec 19, 2016 - 9:30 am - 4:00 pm
Where
NIH Bldg 10 FAES Room 4 (B1C205)
iPathwayGuide is a gene and protein expression analysis tool that uses a systems biology approach to identify significantly impacted pathways, gene ontology terms, diseases, and predicted microRNAs based on the given gene or protein differential expression signature. It uses an advanced pathway analysis approach that considers the role, positioning, and relationships of a given gene within a pathway, resulting in a significantly fewer false positives associated with pathway analysis. More information can be found at this link: http://www.advaitabio.com/ipathwayguide.html PLEASE NOTE: This 1-day workshop is a BYOC (Bring your own Laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. The software is designed to work with all the latest major browser platforms: Google Chrome, Mozilla Firefox, Apple Safari (Mac only, iOS not supported yet), as well as Microsoft Internet Explorer 11 (though some image download capabilities may not function​). WORKSHOP AGENDA - Monday, Dec 19, 2016 Morning Session 9:30 am -10:55 am                    Introduction to iPathwayGuide Overview: modules, User Interface (UI), cloud access, sharing Scoring Method: Impact Analysis, types of evidence, multiple correction Advanced features: printable report & meta-analysis Uploading data: lecture & follow along w/ demo Data Analysis Pipeline Supported organisms and file types
 Uploading CEL files Custom format: gene symbol, log-fold change, p-values Thresholds for DE TRY: Upload GEO2R file TRY: Fill in Title & Description, Contrast Names TRY: Choose thresholds for Differential Expression 11:05 am -12:30 pm      Follow along w/ demo Dataset 1: NanoString Pan-Cancer panel, Breast Cancer samples (human primary tissue) Impact Analysis
 Dataset background TRY: Accept share Summary Page DE Genes Pathways Printable report Q&A 12:30 - 1:00 pm   LUNCH BREAK Afternoon session 1:00 pm - 2:25 pm      Hands-on practice Dataset 2: Genome-wide analysis of gene expression regulated by VRK1 kinase in cancer cell lines (GSE86942) miRNA Inference Gene Ontologies Diseases TRY: Share Report TRY: Generate Meta-analysis, Identify biomarkers, Export Results Q&A 2:35 pm - 4:00 pm       Data Analysis Exercise Dataset 3: Anaplastic Large Cell Lymphoma of Childhood (GSE78513, human primary tissue) TRY: Accept share Send a request for help/ feedback Generate meta-analysis  Select comparable contrasts Identify putative mechanisms on a high impact pathway Identify probable miRNAs Identify relevant GO Terms Identify relevant diseases Generate printable report Design a meta-analysis study Which combination of regions is appropriate? Which modules are informative? Results: view rank diagram, find biomarkers Export pertinent figures & tables Share via email Report findings to class Q&A 2016-12-19 09:30:00 NIH Bldg 10 FAES Room 4 (B1C205) In-Person Cordelia Ziraldo (Advaita Bioinformatics) BTEP 0 iPathwayGuide Workshop
764
Description

ABOUT THE COURSE

R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. It is a console application that compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. It is available for download through CRAN, which is a network ...Read More

ABOUT THE COURSE

R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. It is a console application that compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. It is available for download through CRAN, which is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Bioconductor uses the R statistical programming language, and is open source and open development as well. It provides tools for the analysis and comprehension of high-throughput genomic data. The course will include multiple, short hands-on exercises spread out throughout the two lecture sessions.

PLEASE NOTE: This 1-day workshop is a BYOC (Bring your own Laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

REQUIRED INSTALLATION:  Students who bring their own laptops should ensure that R v3.3.1 and Bioconductor v3.4, is installed on their computers. In addition, several R packages (listed below) will be used which must be installed prior to the course. Please follow the instructions mentioned further below complete installation of these packages required for the workshop.

R Installation

The R program and instructions for its installation can be found by clicking the link provided below.

Please choose the correct environment - Linux, Mac OSX, or Windows - that is applicable to your system.

https://cran.r-project.org/

Bioconductor and Bioconductor Package Installation

Complete instructions for the installation of the basic and additional Bioconductor packages are found here:

http://www.bioconductor.org/install/

In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class:

Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene  

Command-line instructions for Bioconductor and packages: The following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above.

# First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene")) RStudio Installation (not required for workshop, but some users may find it useful)

Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier.  It offers quite an array of functions that you may still find useful and it is well worth a look. Install the "€œDesktop, Open Source Edition"€:

http://www.rstudio.com/products/RStudio/#Desk

Workshop Agenda

Morning Session - 9:30 am -12:30 pm

Introduction to R

  • The R environment
    • Starting an R Session, Setting Options
    • Listing Variables, Editing Commands, Using the R History
    • Getting Help on an R Function
    • Logging a Session to a File
    • Running External R Code
    • Installing and Loading Packages
    • Ending a Session, Saving Your Work
  • The Elements of R
    • Numeric
    • Character
    • Logical
    • Missing Values
  • R Data Structures
    • Vectors
    • Matrices
    • Lists
    • Data.Frames
    • Factors
    • Functions
    • Other Complex Structures
  • Procedures
    • Reading and Writing Data
    • Exploring and Summarizing Data
    • Dealing with Missing Data
    • Restructuring Data
    • Relabeling Data
    • Subsetting Data
    • Operating on Rows or Columns of Data
    • Saving R Objects for Later Use
    • Graphing Data
    • Simple Statistical Tests
    • Example: A Simple Analysis of Probe Intensity Data
  • Project: Creating a Graphical Function in 4 Easy Steps
    • Step 1: Create a Heatmap of Gene Expression Data
    • Step 2: Package Heatmap as a Function
    • Step 3: Add some Custom Formatting
    • Step 4: Save for Future Use and – Voila, You Have Created your own Heatmap Library!
12:30 - 1:00 pm   LUNCH BREAK

Afternoon Session - 1:00 - 4:00 pm

Introduction to Bioconductor

  • Installing Bioconductor
  • An Overview of Bioconductor Packages
  • Fundamental Packages
    • Biobase: the Foundation
    • Biostrings: A Representation of Biological Sequences
    • BSgenome: A Representation of Complete Genomic Sequences
    • GenomicRanges: Manipulation of Genomic Intervals
    • GenomicFeatures: Manipulation of Genomic Features
    • GenomicAlgnments: Manipulation of Short Genomic Alignments
  • Two Fundamental Structures to Contain Experiment Data
    • The ExpressionSet for Array Data
      • Constructing an ExpressionSet
      • Analyzing an ExpressionSet
    • The SummarizedExperiment for NGS Sequence Data
      • Constructing a SummarizedExperiment
      • Analyzing a SummarizedExperiment
Register
Organizer
BTEP
When
Tue, Dec 20, 2016 - 9:30 am - 4:00 pm
Where
NIH Bldg 10 FAES Room 4 (B1C205)
ABOUT THE COURSE R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. It is a console application that compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. It is available for download through CRAN, which is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Bioconductor uses the R statistical programming language, and is open source and open development as well. It provides tools for the analysis and comprehension of high-throughput genomic data. The course will include multiple, short hands-on exercises spread out throughout the two lecture sessions. PLEASE NOTE: This 1-day workshop is a BYOC (Bring your own Laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. REQUIRED INSTALLATION:  Students who bring their own laptops should ensure that R v3.3.1 and Bioconductor v3.4, is installed on their computers. In addition, several R packages (listed below) will be used which must be installed prior to the course. Please follow the instructions mentioned further below complete installation of these packages required for the workshop. R Installation The R program and instructions for its installation can be found by clicking the link provided below. Please choose the correct environment - Linux, Mac OSX, or Windows - that is applicable to your system. https://cran.r-project.org/ Bioconductor and Bioconductor Package Installation Complete instructions for the installation of the basic and additional Bioconductor packages are found here: http://www.bioconductor.org/install/ In addition to the basic Bioconductor package, please install these additional Bioconductor packages prior to the start of the class: Biostrings BSgenome BSgenome.Celegans.UCSC.ce6 TxDb.Celegans.UCSC.ce6.ensGene GenomicFeatures GenomicRanges GenomicAlignments TxDb.Hsapiens.UCSC.hg19.knownGene   Command-line instructions for Bioconductor and packages: The following code, executed from within an R session, should serve to install the basic Bioconductor package as well as the additional packages listed above. # First, download the Bioconductor installer, biocLite() source("http://bioconductor.org/biocLite.R") # Now, use the installer to install several packages at once # The base package, Biobase, will be installed automatically biocLite(pkgs=c("Biostrings", "BSgenome", "BSgenome.Celegans.UCSC.ce6", "TxDb.Celegans.UCSC.ce6.ensGene", "GenomicFeatures", "GenomicRanges", "GenomicAlignments", "TxDb.Hsapiens.UCSC.hg19.knownGene")) RStudio Installation (not required for workshop, but some users may find it useful) Students who prefer a more graphically-oriented working environment will find that using RStudio as an environment in which to run R makes life much easier.  It offers quite an array of functions that you may still find useful and it is well worth a look. Install the "€œDesktop, Open Source Edition"€: http://www.rstudio.com/products/RStudio/#Desk Workshop Agenda Morning Session - 9:30 am -12:30 pm Introduction to R The R environment Starting an R Session, Setting Options Listing Variables, Editing Commands, Using the R History Getting Help on an R Function Logging a Session to a File Running External R Code Installing and Loading Packages Ending a Session, Saving Your Work The Elements of R Numeric Character Logical Missing Values R Data Structures Vectors Matrices Lists Data.Frames Factors Functions Other Complex Structures Procedures Reading and Writing Data Exploring and Summarizing Data Dealing with Missing Data Restructuring Data Relabeling Data Subsetting Data Operating on Rows or Columns of Data Saving R Objects for Later Use Graphing Data Simple Statistical Tests Example: A Simple Analysis of Probe Intensity Data Project: Creating a Graphical Function in 4 Easy Steps Step 1: Create a Heatmap of Gene Expression Data Step 2: Package Heatmap as a Function Step 3: Add some Custom Formatting Step 4: Save for Future Use and – Voila, You Have Created your own Heatmap Library! 12:30 - 1:00 pm   LUNCH BREAK Afternoon Session - 1:00 - 4:00 pm Introduction to Bioconductor Installing Bioconductor An Overview of Bioconductor Packages Fundamental Packages Biobase: the Foundation Biostrings: A Representation of Biological Sequences BSgenome: A Representation of Complete Genomic Sequences GenomicRanges: Manipulation of Genomic Intervals GenomicFeatures: Manipulation of Genomic Features GenomicAlgnments: Manipulation of Short Genomic Alignments Two Fundamental Structures to Contain Experiment Data The ExpressionSet for Array Data Constructing an ExpressionSet Analyzing an ExpressionSet The SummarizedExperiment for NGS Sequence Data Constructing a SummarizedExperiment Analyzing a SummarizedExperiment 2016-12-20 09:30:00 NIH Bldg 10 FAES Room 4 (B1C205) In-Person David Wheeler PhD. (Laboratory of Biochemistry and Molecular Biology CCR NCI) BTEP 0 R/Bioconductor Basics Workshop
763
Description

The Bioinformatics Training and Education Program (BTEP), Office of Science and Technology Resources (OSTR) is excited to announce a series of workshops focused on the analysis of next-generation sequencing (NGS) data. The in-depth, comprehensive series includes lecture and hands-on components on the most popular and relevant topics for the CCR scientific community.

This opening presentation will provide an in-depth overview of Next Generation Sequencing Technologies (did you know there are more than 50 <...Read More

The Bioinformatics Training and Education Program (BTEP), Office of Science and Technology Resources (OSTR) is excited to announce a series of workshops focused on the analysis of next-generation sequencing (NGS) data. The in-depth, comprehensive series includes lecture and hands-on components on the most popular and relevant topics for the CCR scientific community.

This opening presentation will provide an in-depth overview of Next Generation Sequencing Technologies (did you know there are more than 50 named NGS sequencing methodologies), CCR sequencing resources, and CCR bioinformatic resources for data analysis. The is a unique opportunity to hear about the latest next-gen sequencing techniques, CCR’s core facilities, as well as how to make the best use of them in your research. Come ask questions of a panel of experts (from the CCR Sequencing Facility and the CCR Collaborative Bioinformatics Resource). Learn about “best practice” guidelines and standards for performing optimized, cost-effective experiments and how to make best use of your cores.

These presentations are being made available via GoToWebinar

Please sign-up for this WebCast at:
https://attendee.gotowebinar.com/register/8115044904966606850
 

Part 1: NGS Technologies, Techniques and Facilities

  • Time: 9:30 am – 12:30 pm
  • Location: Bldg 37m Rm 6041

Part 2: Best Practices in NGS Data Analysis

  • Time: 1:30 pm – 4:00 pm
  • Location: NIH Bldg 10, FAES Classroom 4 (B1C205)
Register
Organizer
BTEP
When
Mon, Jan 23, 2017 - 9:30 am - 4:00 pm
Where
In-Person
The Bioinformatics Training and Education Program (BTEP), Office of Science and Technology Resources (OSTR) is excited to announce a series of workshops focused on the analysis of next-generation sequencing (NGS) data. The in-depth, comprehensive series includes lecture and hands-on components on the most popular and relevant topics for the CCR scientific community. This opening presentation will provide an in-depth overview of Next Generation Sequencing Technologies (did you know there are more than 50 named NGS sequencing methodologies), CCR sequencing resources, and CCR bioinformatic resources for data analysis. The is a unique opportunity to hear about the latest next-gen sequencing techniques, CCR’s core facilities, as well as how to make the best use of them in your research. Come ask questions of a panel of experts (from the CCR Sequencing Facility and the CCR Collaborative Bioinformatics Resource). Learn about “best practice” guidelines and standards for performing optimized, cost-effective experiments and how to make best use of your cores. These presentations are being made available via GoToWebinar Please sign-up for this WebCast at:https://attendee.gotowebinar.com/register/8115044904966606850   Part 1: NGS Technologies, Techniques and Facilities Time: 9:30 am – 12:30 pm Location: Bldg 37m Rm 6041 Part 2: Best Practices in NGS Data Analysis Time: 1:30 pm – 4:00 pm Location: NIH Bldg 10, FAES Classroom 4 (B1C205) 2017-01-23 09:30:00 In-Person BTEP 0 Best Practices: Experimental Design, Sample Preparation, NGS Technologies
757
Description
nSolver™ Analysis Software is a free analysis platform for storage, custom QC, and custom normalization of nCounter data. Generate highly-customized exports, basic statistical outputs, and publication-quality figures quickly and easily with the included tools. nSolver is a biologist-friendly quality control, normalization, visualization and analysis software dedicated to best practices with nCounter data. The nCounter Technology is a multiplexed amplification-free, library-free digital gene expression data quantifying DNA, mRNAs, proteins, phosphorylation and miRNAs.

NOTE: ...Read More

nSolver™ Analysis Software is a free analysis platform for storage, custom QC, and custom normalization of nCounter data. Generate highly-customized exports, basic statistical outputs, and publication-quality figures quickly and easily with the included tools. nSolver is a biologist-friendly quality control, normalization, visualization and analysis software dedicated to best practices with nCounter data. The nCounter Technology is a multiplexed amplification-free, library-free digital gene expression data quantifying DNA, mRNAs, proteins, phosphorylation and miRNAs.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please register only if you intend to attend the workshop.

Preparations ahead of the workshop for those interested in attending:

1.  Download Sample/Demo Data under Course Material1 below. 2. Software Requirements: To download the nSolver v3.0.22 software, please register and then login on: www.nanostring.com. Based on your computer system, please choose the correct installation file (Mac or Windows-64-bit or Windows-32-bit). 3. Download nCounter Advanced Analysis Module v1.1.4  - Course Material2 4. Instructions for the Advanced Analysis module is available as PDF - Course Material3

WORKSHOP AGENDA

11:30 am -12:30 pm                  nCounter Data and nSolver Software Overview

12:30-1:00 Lunch Break

1:00-3:00 pm                           Tutorial (bring your own data or use demo data)

 

Register
Organizer
BTEP
When
Wed, Feb 15, 2017 - 11:30 am - 3:00 pm
Where
In-Person
nSolver™ Analysis Software is a free analysis platform for storage, custom QC, and custom normalization of nCounter data. Generate highly-customized exports, basic statistical outputs, and publication-quality figures quickly and easily with the included tools. nSolver is a biologist-friendly quality control, normalization, visualization and analysis software dedicated to best practices with nCounter data. The nCounter Technology is a multiplexed amplification-free, library-free digital gene expression data quantifying DNA, mRNAs, proteins, phosphorylation and miRNAs. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please register only if you intend to attend the workshop. Preparations ahead of the workshop for those interested in attending: 1.  Download Sample/Demo Data under Course Material1 below. 2. Software Requirements: To download the nSolver v3.0.22 software, please register and then login on: www.nanostring.com. Based on your computer system, please choose the correct installation file (Mac or Windows-64-bit or Windows-32-bit). 3. Download nCounter Advanced Analysis Module v1.1.4  - Course Material2 4. Instructions for the Advanced Analysis module is available as PDF - Course Material3 WORKSHOP AGENDA 11:30 am -12:30 pm                  nCounter Data and nSolver Software Overview 12:30-1:00 Lunch Break 1:00-3:00 pm                           Tutorial (bring your own data or use demo data)   2017-02-15 11:30:00 In-Person Greg Gonye Ph.D. (Nanostring Technologies) BTEP 0 Workshop on Analysis of NanoString Data
762
Description
This BTEP Workshop will cover the fundamentals and best practices of Exome-Seq analysis, including downstream interpretation of variants using a variety of in-house and NCI-licensed software solutions. There will be hands-on training on CCBR Exome-Seq Pipeline, CLC Biomedical Workbench, Genomatix GeneGrid, Ingenuity Variant Analysis and BioDiscovery Nexus Copy Number.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to ...Read More

This BTEP Workshop will cover the fundamentals and best practices of Exome-Seq analysis, including downstream interpretation of variants using a variety of in-house and NCI-licensed software solutions. There will be hands-on training on CCBR Exome-Seq Pipeline, CLC Biomedical Workbench, Genomatix GeneGrid, Ingenuity Variant Analysis and BioDiscovery Nexus Copy Number.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please register only if you intend to attend the workshop.

Dates: February 21-22, 2017 (Tuesday and Wednesday)

Time: 9:30 am – 4:00 pm

Location: NIH Bldg 10, FAES Classroom 4

SOFTWARE REQUIREMENTS:

[1] Download the Trial License for CLC Biomedical Genomics Workbench here: https://www.qiagenbioinformatics.com/products/biomedical-genomics-workbe...

[2] Download the Trial License for Ingenuity Variant Analysis here: https://www.qiagenbioinformatics.com/products/ingenuity-variant-analysis/

[3] For access to BioDiscovery Nexus Copy Number, please login to https://service.cancer.gov/, and navigate to Scientific Software under 'Request Something'.

[4] Information on access to Genomatix GeneGrid will be provided to attendees present at the workshop, prior to the start of the session.

WORKSHOP AGENDA

Day 1: Tuesday, February 21, 2017

9:30 – 10:30 am         

Title: Introduction to Exome-Seq: What, Why, How?

Presenter: Chunhua Yan, PhD

This will be an introduction to Exome-Seq, covering:

    •    Brief overview of next-generation sequencing technology
    •    Exome sequencing (Cost, Speed, Gene coverage, Biological implication)
    •    Experimental design (Sample size, Coverage, Whole/Targeted exome-seq, Sample submission)
    •    Mutation calling resources (Dream Challenge, Genome in A Bottle, exome databases)

10:30 am – 12:30 pm

Title: Exome-Seq Data Analysis Pipeline: From Reads to Results

Presenter: Justin Lack, PhD

This talk will provide an overview of the CCBR Exome-Seq pipeline work-flow with recommended best practices.

Some of the topics covered will be:

  • Raw data processing and QC
  • Short read mapping and alignment QC, 
    
  • Approaches to improving processing alignments
  • Germline SNP and small INDEL calling,
    
  • Somatic SNP and small INDEL calling,
  
  • Germline and somatic structural variant calling,
  • Multi-tool variant annotation (AVIA, SnpEff, Oncotator, etc.)
  • Example processing and analysis of a tumor/germline comparison data set

LUNCH BREAK 12:30 – 1:00 pm

1:00-4:00 pm

Title: CLC Biomedical Workbench for Analysis of Exome-Seq Data

Presenter: Jennifer Poitras, Field Application Specialist

Biomedical Genomics Workbench is a comprehensive and accurate data analysis platform that enables you to find the signal in the noise in your cancer and hereditary disease NGS data. With its broad selection of end-to-end analysis workflows, tools, and visualization modules, it enables easy and accurate discovery, verification, and validation of novel disease biomarkers. In this training, we will use prebuilt workflows, or analysis pipelines, to identify somatic variants in tumor samples and tumor/normal pairs. Within the workflow, we will map reads to the reference, identify variants, and annotate those variants not only with nucleic and amino acid changes, but also with information from third party sources, such as 1,000 genomes, dbSNP, and ClinVar. By the end of the training, you will appreciate that Biomedical Genomics Workbench is your one stop shop for analysis and visualization of NGS data.

Day 2: Wednesday, February 22, 2017

9:30 am - 11:00am    

Title: Using the Genomatix GeneGrid Analyzer for Your Exome-Seq Data
Presenter: Justin Lack, Ph.D.

 This talk will cover the ease-of-use and application(s) of GeneGrid:

  • Import and annotate variants
  • Compare samples for multiple experimental designs
  • Filter and prioritize variants
  • Generate extensive reports
  • Analyze affected pathway
  • Browse variants on the genome

11:00 am – 12:30 pm

Title: Ingenuity Variant Analysis (IVA) Software for Identifying Clinically Impactful Variants

Presenter: Jennifer Poitras, Field Application Specialist

Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based both upon published biological evidence and your own knowledge of disease biology. This workshop will focus on how the users can upload their datasets, efficiently use different filters within variant analysis to identify causal variants, export data and will also go over the recent IVA updates. With IVA, you can interrogate your variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up.

LUNCH BREAK 12:30 – 1:00 pm

1:00 – 2:00 pm            OPEN Q & A with Presenters

2:00 - 4:00 pm            

Title: Using BioDiscovery Nexus for Copy Number Analysis

Presenter: Andrea O'Hara, Field Application Specialist

Nexus Copy Number version 8.0, offers copy number estimation from whole-genome sequencing (WGS), whole-exome sequencing (WES), and targeted sequencing panels. The sophisticated algorithm in Nexus Copy Number requires only BAM files as input and in addition to copy number, also derives B-allele frequencies (BAF) from BAM files. The interactive visualization and powerful statistical tools allow detection of structural variations (e.g. copy number, homozygous regions), association with sequence variations (point mutations, InDels, inversions, etc.), and identification of statistically significant co-occurring up/down regulated genes (from mRNA, miRNA, and RNA-Seq data). In this workshop, we will evaluate matched and unmatched tumor-normal cohorts for copy number and sequence variant analysis; we will use the sophisticated built-in statistical analyses and integrated graphical display to rapidly explore and mine vast amounts of data in minutes. Comprehensive downstream analysis will include statistical comparisons, concordance, clustering, survival and enrichment analysis.

 

Register
Organizer
BTEP
When
Tue, Feb 21 - Wed, Feb 22, 2017 -9:30 am - 4:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
This BTEP Workshop will cover the fundamentals and best practices of Exome-Seq analysis, including downstream interpretation of variants using a variety of in-house and NCI-licensed software solutions. There will be hands-on training on CCBR Exome-Seq Pipeline, CLC Biomedical Workbench, Genomatix GeneGrid, Ingenuity Variant Analysis and BioDiscovery Nexus Copy Number. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please register only if you intend to attend the workshop. Dates: February 21-22, 2017 (Tuesday and Wednesday) Time: 9:30 am – 4:00 pm Location: NIH Bldg 10, FAES Classroom 4 SOFTWARE REQUIREMENTS: [1] Download the Trial License for CLC Biomedical Genomics Workbench here: https://www.qiagenbioinformatics.com/products/biomedical-genomics-workbe... [2] Download the Trial License for Ingenuity Variant Analysis here: https://www.qiagenbioinformatics.com/products/ingenuity-variant-analysis/ [3] For access to BioDiscovery Nexus Copy Number, please login to https://service.cancer.gov/, and navigate to Scientific Software under 'Request Something'. [4] Information on access to Genomatix GeneGrid will be provided to attendees present at the workshop, prior to the start of the session. WORKSHOP AGENDA Day 1: Tuesday, February 21, 2017 9:30 – 10:30 am          Title: Introduction to Exome-Seq: What, Why, How? Presenter: Chunhua Yan, PhD This will be an introduction to Exome-Seq, covering:     •    Brief overview of next-generation sequencing technology     •    Exome sequencing (Cost, Speed, Gene coverage, Biological implication)     •    Experimental design (Sample size, Coverage, Whole/Targeted exome-seq, Sample submission)     •    Mutation calling resources (Dream Challenge, Genome in A Bottle, exome databases) 10:30 am – 12:30 pm Title: Exome-Seq Data Analysis Pipeline: From Reads to Results Presenter: Justin Lack, PhD This talk will provide an overview of the CCBR Exome-Seq pipeline work-flow with recommended best practices. Some of the topics covered will be: Raw data processing and QC Short read mapping and alignment QC, 
     Approaches to improving processing alignments Germline SNP and small INDEL calling,
     Somatic SNP and small INDEL calling,
   Germline and somatic structural variant calling, Multi-tool variant annotation (AVIA, SnpEff, Oncotator, etc.) Example processing and analysis of a tumor/germline comparison data set LUNCH BREAK 12:30 – 1:00 pm 1:00-4:00 pm Title: CLC Biomedical Workbench for Analysis of Exome-Seq Data Presenter: Jennifer Poitras, Field Application Specialist Biomedical Genomics Workbench is a comprehensive and accurate data analysis platform that enables you to find the signal in the noise in your cancer and hereditary disease NGS data. With its broad selection of end-to-end analysis workflows, tools, and visualization modules, it enables easy and accurate discovery, verification, and validation of novel disease biomarkers. In this training, we will use prebuilt workflows, or analysis pipelines, to identify somatic variants in tumor samples and tumor/normal pairs. Within the workflow, we will map reads to the reference, identify variants, and annotate those variants not only with nucleic and amino acid changes, but also with information from third party sources, such as 1,000 genomes, dbSNP, and ClinVar. By the end of the training, you will appreciate that Biomedical Genomics Workbench is your one stop shop for analysis and visualization of NGS data. Day 2: Wednesday, February 22, 2017 9:30 am - 11:00am     Title: Using the Genomatix GeneGrid Analyzer for Your Exome-Seq Data Presenter: Justin Lack, Ph.D.  This talk will cover the ease-of-use and application(s) of GeneGrid: Import and annotate variants Compare samples for multiple experimental designs Filter and prioritize variants Generate extensive reports Analyze affected pathway Browse variants on the genome 11:00 am – 12:30 pm Title: Ingenuity Variant Analysis (IVA) Software for Identifying Clinically Impactful Variants Presenter: Jennifer Poitras, Field Application Specialist Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based both upon published biological evidence and your own knowledge of disease biology. This workshop will focus on how the users can upload their datasets, efficiently use different filters within variant analysis to identify causal variants, export data and will also go over the recent IVA updates. With IVA, you can interrogate your variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. LUNCH BREAK 12:30 – 1:00 pm 1:00 – 2:00 pm            OPEN Q & A with Presenters 2:00 - 4:00 pm             Title: Using BioDiscovery Nexus for Copy Number Analysis Presenter: Andrea O'Hara, Field Application Specialist Nexus Copy Number version 8.0, offers copy number estimation from whole-genome sequencing (WGS), whole-exome sequencing (WES), and targeted sequencing panels. The sophisticated algorithm in Nexus Copy Number requires only BAM files as input and in addition to copy number, also derives B-allele frequencies (BAF) from BAM files. The interactive visualization and powerful statistical tools allow detection of structural variations (e.g. copy number, homozygous regions), association with sequence variations (point mutations, InDels, inversions, etc.), and identification of statistically significant co-occurring up/down regulated genes (from mRNA, miRNA, and RNA-Seq data). In this workshop, we will evaluate matched and unmatched tumor-normal cohorts for copy number and sequence variant analysis; we will use the sophisticated built-in statistical analyses and integrated graphical display to rapidly explore and mine vast amounts of data in minutes. Comprehensive downstream analysis will include statistical comparisons, concordance, clustering, survival and enrichment analysis.   2017-02-21 09:30:00 Bldg 10 FAES room 4 (B1C205) In-Person Justin Lack (NIAID CBR) BTEP 0 From the Beginning: Exome-Seq Data Analysis (2 day)
761
Description
BTEP Workshop on RNA-Seq Data Analysis (2-day)

This 2-day workshop, which includes both lecture and hands-on components, will cover the fundamentals of and best practices for RNA-Seq Data Analysis. Learn everything from experimental design and sample prep requirements, to alignment, quantification, generation of differentially expressed genes, and understanding the results after completion of analysis. There will be presentations and training on using both open source (CCBR RNA-Seq Pipeline) and commercial (Partek Flow) software.

<...Read More
BTEP Workshop on RNA-Seq Data Analysis (2-day)

This 2-day workshop, which includes both lecture and hands-on components, will cover the fundamentals of and best practices for RNA-Seq Data Analysis. Learn everything from experimental design and sample prep requirements, to alignment, quantification, generation of differentially expressed genes, and understanding the results after completion of analysis. There will be presentations and training on using both open source (CCBR RNA-Seq Pipeline) and commercial (Partek Flow) software.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. 

Date: March 20-21, 2017 Time: 9:30 am – 4:00 pm Location: NIH Bldg 10 FAES Classroom 4

For CCR staff located outside of the Bethesda campus, the talks will be webcast for your convenience. Please register for the webinar here: https://attendee.gotowebinar.com/register/469987726031396353

WORKSHOP AGENDA

Monday, March 20 - Morning Session

9:35 - 10:35 am            Introduction to RNA-Seq                                      Speaker: Maggie Cam, Ph.D.   The first part of the session will provide an introduction to the technology, its various applications, comparison to microarray, advantages as well as limitations, and costs associated with doing this NGS experiment. The second part of the talk will focus on best practices to be adopted when considering RNA-Seq for your research, including sample quality, sequencing depth, replicates, analytical methods, and results generated with this approach.   10:40 - 11:40 am          Introduction to Single Cell RNA-Seq                                      Speaker: Michael Kelly, PhD   This presentation will discuss how scRNASeq is different from conventional RNA-Seq, special challenges, specific applications, experimental and analytical requirements.

 

11:45 am -12:30 pm     Understanding the NGS vocabulary and file formats                                      Speaker: Peter C. Fitzgerald, PhD   This talk will discuss file formats and terms commonly used for NGS data analysis.   LUNCH BREAK  12:30 - 1:00 pm   Monday, March 20 - Afternoon Session   1:00 - 4:00 pm         CCBR RNA-Seq Pipeline                                 Speaker: Fathi Elloumi, PhD   This session will be a comprehensive review of the pipeline built and used by CCBR for analysis of RNA-Seq data, and will cover:   1. Introduction 2. Analytical Steps
  • Initial QC
  • Alignment - aligners, transcriptome vs genome, non-aligners
  • Post-Alignment QC
  • Expression quantitation, FPKM, counts, Junctions reads
  • Normalization (different methods)
  • Differential Expression gene/transcriptome lists
  • Isoform calling
3. Demo on the RNA-Seq pipeline 4. Detailed review of pre-analyzed results
  • Multi-QC results
  • Differentially Expressed Genes 
  • Isoform Expression (high-level review)
5. Next Steps
  • Viewers - Manual validation (IGV)
  • Principal Component Analysis (PCA)
  • Clustering - Different methods
  • Pathway Enrichment (GSEA)

6. Final, brief hands-on session for ONLY those who fulfill the following requirements:

  1. Already have an existing Biowulf/Helix Account
  2. Basic working knowledge of command line on Terminal
  3. Functional XQuartz application (https://www.xquartz.org/releases/XQuartz-2.7.11.html) 

This is applicable for only this last session on Day 1. Not necessary for other sessions on Day 1 or Day 2.

Day 2 - Tuesday, March 21

9:30 am - 10:00 am          Set up time for Partek Flow and Partek Genomics Suite     10:00 - 12:30 pm               RNA-Seq Analysis using Partek Flow                                            Presenter: Eric Seiser, PhD - Partek Field Application Specialist   An overview of getting started on the NIH Helix server and then a live demo of RNA-seq analysis in Partek Flow. The training will highlight key concepts in RNA-seq analysis and their implementation Flow.  This will be followed by a demo utilizing Partek Flow to importing raw sequence data in fastq format from a published study, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation.  

LUNCH BREAK    12:30 - 1:00 pm

1:00 - 4:00 pm            Read count data analysis using Partek Flow and Genomics Suite                                     Presenter: Eric Seiser, PhD - Partek Field Application Specialist   This second part of the training session will allow users to take raw RNA-seq data from a recently published study and independently build a complete analysis pipeline within Partek Flow, allowing students to ask questions as they analyze the data.   This will be followed by an overview of RNA-seq functionality in Partek Genomics Suite, focusing on plotting options, data integration, and enrichment analysis.  The final part of the training will allow users hand-on training to import the results from their Flow analysis pipeline and explore the downstream features in Genomics Suite.  This time will also be used as a general question and answer session.   Students will learn how to use basic features of Partek Flow and Partek Genomics Suite, including:
·       Partek Flow
    •    Getting set up on NIH Helix server
    •    Importing data
    •    Performing QA/AC
    •    Alignment
    •    Gene/transcript abundance estimation
    •    Differential expression detection
    •    Go Enrichment analysis
    •    Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.)
    •    Microarray analysis and integration with RNA-seq data.
·       Partek Genomics Suite
    •    Importing Partek Flow project and text file format
    •    Visualization (PCA, dot plot, heatmap etc.)
    •    Pathway analysis
    •    Integration of genomic data.   * Instructions for setting up access to Partek Flow will be provided to attendees at the workshop. **Information for installing Partek Genomics Suite will be sent to registrants ahead of the workshop.
Register
Organizer
BTEP
When
Mon, Mar 20 - Tue, Mar 21, 2017 -9:30 am - 4:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
BTEP Workshop on RNA-Seq Data Analysis (2-day) This 2-day workshop, which includes both lecture and hands-on components, will cover the fundamentals of and best practices for RNA-Seq Data Analysis. Learn everything from experimental design and sample prep requirements, to alignment, quantification, generation of differentially expressed genes, and understanding the results after completion of analysis. There will be presentations and training on using both open source (CCBR RNA-Seq Pipeline) and commercial (Partek Flow) software. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.  Date: March 20-21, 2017 Time: 9:30 am – 4:00 pm Location: NIH Bldg 10 FAES Classroom 4 For CCR staff located outside of the Bethesda campus, the talks will be webcast for your convenience. Please register for the webinar here: https://attendee.gotowebinar.com/register/469987726031396353 WORKSHOP AGENDA Monday, March 20 - Morning Session 9:35 - 10:35 am            Introduction to RNA-Seq                                      Speaker: Maggie Cam, Ph.D.   The first part of the session will provide an introduction to the technology, its various applications, comparison to microarray, advantages as well as limitations, and costs associated with doing this NGS experiment. The second part of the talk will focus on best practices to be adopted when considering RNA-Seq for your research, including sample quality, sequencing depth, replicates, analytical methods, and results generated with this approach.   10:40 - 11:40 am          Introduction to Single Cell RNA-Seq                                      Speaker: Michael Kelly, PhD   This presentation will discuss how scRNASeq is different from conventional RNA-Seq, special challenges, specific applications, experimental and analytical requirements.   11:45 am -12:30 pm     Understanding the NGS vocabulary and file formats                                      Speaker: Peter C. Fitzgerald, PhD   This talk will discuss file formats and terms commonly used for NGS data analysis.   LUNCH BREAK  12:30 - 1:00 pm   Monday, March 20 - Afternoon Session   1:00 - 4:00 pm         CCBR RNA-Seq Pipeline                                 Speaker: Fathi Elloumi, PhD   This session will be a comprehensive review of the pipeline built and used by CCBR for analysis of RNA-Seq data, and will cover:   1. Introduction 2. Analytical Steps Initial QC Alignment - aligners, transcriptome vs genome, non-aligners Post-Alignment QC Expression quantitation, FPKM, counts, Junctions reads Normalization (different methods) Differential Expression gene/transcriptome lists Isoform calling 3. Demo on the RNA-Seq pipeline 4. Detailed review of pre-analyzed results Multi-QC results Differentially Expressed Genes  Isoform Expression (high-level review) 5. Next Steps Viewers - Manual validation (IGV) Principal Component Analysis (PCA) Clustering - Different methods Pathway Enrichment (GSEA) 6. Final, brief hands-on session for ONLY those who fulfill the following requirements: Already have an existing Biowulf/Helix Account Basic working knowledge of command line on Terminal Functional XQuartz application (https://www.xquartz.org/releases/XQuartz-2.7.11.html)  This is applicable for only this last session on Day 1. Not necessary for other sessions on Day 1 or Day 2. Day 2 - Tuesday, March 21 9:30 am - 10:00 am          Set up time for Partek Flow and Partek Genomics Suite     10:00 - 12:30 pm               RNA-Seq Analysis using Partek Flow                                            Presenter: Eric Seiser, PhD - Partek Field Application Specialist   An overview of getting started on the NIH Helix server and then a live demo of RNA-seq analysis in Partek Flow. The training will highlight key concepts in RNA-seq analysis and their implementation Flow.  This will be followed by a demo utilizing Partek Flow to importing raw sequence data in fastq format from a published study, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation.   LUNCH BREAK    12:30 - 1:00 pm 1:00 - 4:00 pm            Read count data analysis using Partek Flow and Genomics Suite                                     Presenter: Eric Seiser, PhD - Partek Field Application Specialist   This second part of the training session will allow users to take raw RNA-seq data from a recently published study and independently build a complete analysis pipeline within Partek Flow, allowing students to ask questions as they analyze the data.   This will be followed by an overview of RNA-seq functionality in Partek Genomics Suite, focusing on plotting options, data integration, and enrichment analysis.  The final part of the training will allow users hand-on training to import the results from their Flow analysis pipeline and explore the downstream features in Genomics Suite.  This time will also be used as a general question and answer session.   Students will learn how to use basic features of Partek Flow and Partek Genomics Suite, including:·       Partek Flow     •    Getting set up on NIH Helix server     •    Importing data     •    Performing QA/AC     •    Alignment     •    Gene/transcript abundance estimation     •    Differential expression detection     •    Go Enrichment analysis     •    Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.)     •    Microarray analysis and integration with RNA-seq data.·       Partek Genomics Suite     •    Importing Partek Flow project and text file format     •    Visualization (PCA, dot plot, heatmap etc.)     •    Pathway analysis     •    Integration of genomic data.   * Instructions for setting up access to Partek Flow will be provided to attendees at the workshop. **Information for installing Partek Genomics Suite will be sent to registrants ahead of the workshop. 2017-03-20 09:30:00 Bldg 10 FAES room 4 (B1C205) In-Person Maggie Cam (NCI CCBR) BTEP 0 Comprehending the Message: RNA-Seq Data Analysis
755
Description
The CCR Bioinformatics Training and Education Program (BTEP) is excited to arrange its inaugural informational presentation for the CCR scientific community at NCI-Frederick. Date: Wednesday, March 29, 2017 Time: 11:00 am - 12:00 pm Location: ATRF Room E-1106 The talk will cover, among other topics:
  • the history and mission of the program,
  • the team and support infrastructure,
  • information on the BTEP website
  • schedule ...Read More
The CCR Bioinformatics Training and Education Program (BTEP) is excited to arrange its inaugural informational presentation for the CCR scientific community at NCI-Frederick. Date: Wednesday, March 29, 2017 Time: 11:00 am - 12:00 pm Location: ATRF Room E-1106 The talk will cover, among other topics:
  • the history and mission of the program,
  • the team and support infrastructure,
  • information on the BTEP website
  • schedule of future workshops and events,
  • collaborations with other groups and institutions across NIH
No registration required. Presentation will be webcast through Webex (see below).

Webex details to join in online

Topic: Informational Session on BTEP at ATRF
Date and Time:Wednesday, March 29, 2017 11:00 am, Eastern Daylight Time (New York, GMT-04:00)
Event number: 731 630 993
Event password: pRcEwp2$
Event address for attendees: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=e538eeeba889db9f2137b17...

-------------------------------------------------------
Audio conference information
-------------------------------------------------------
Call-in toll-free number (US/Canada): 1-855-244-8681
Call-in toll number (US/Canada): 1-650-479-3207
Global call-in numbers: https://cbiit.webex.com/cbiit/globalcallin.php?serviceType=EC&ED=5439598...
Toll-free dialing restrictions: https://www.webex.com/pdf/tollfree_restrictions.pdf
Access code: 731 630 993

https://www.webex.com

IMPORTANT NOTICE: This WebEx service includes a feature that allows audio and any documents and other materials exchanged or viewed during the session to be recorded. You should inform all meeting attendees prior to recording if you intend to record the meeting. Please note that any such recordings may be subject to discovery in the event of litigation.

 

Register
Organizer
BTEP
When
Wed, Mar 29, 2017 - 11:00 am - 12:00 pm
Where
ATRF Rm E1106
The CCR Bioinformatics Training and Education Program (BTEP) is excited to arrange its inaugural informational presentation for the CCR scientific community at NCI-Frederick. Date: Wednesday, March 29, 2017 Time: 11:00 am - 12:00 pm Location: ATRF Room E-1106 The talk will cover, among other topics: the history and mission of the program, the team and support infrastructure, information on the BTEP website schedule of future workshops and events, collaborations with other groups and institutions across NIH No registration required. Presentation will be webcast through Webex (see below). Webex details to join in online Topic: Informational Session on BTEP at ATRF Date and Time:Wednesday, March 29, 2017 11:00 am, Eastern Daylight Time (New York, GMT-04:00) Event number: 731 630 993 Event password: pRcEwp2$ Event address for attendees: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=e538eeeba889db9f2137b17... ------------------------------------------------------- Audio conference information ------------------------------------------------------- Call-in toll-free number (US/Canada): 1-855-244-8681 Call-in toll number (US/Canada): 1-650-479-3207 Global call-in numbers: https://cbiit.webex.com/cbiit/globalcallin.php?serviceType=EC&ED=5439598... Toll-free dialing restrictions: https://www.webex.com/pdf/tollfree_restrictions.pdf Access code: 731 630 993 https://www.webex.com IMPORTANT NOTICE: This WebEx service includes a feature that allows audio and any documents and other materials exchanged or viewed during the session to be recorded. You should inform all meeting attendees prior to recording if you intend to record the meeting. Please note that any such recordings may be subject to discovery in the event of litigation.   2017-03-29 11:00:00 ATRF Rm E1106 In-Person BTEP 0 Informational Session on BTEP
756
Description
The CCR Bioinformatics Training and Education Program (BTEP) is pleased to organize a workshop on Partek Flow for scientists at NCI-Frederick. Partek Flow software is designed specifically for the analysis needs of next generation sequencing (NGS) applications including RNA, small RNA, and DNA sequencing. With an easy-to-use graphical interface, one can perform alignment, quantification, quality control, statistics and visualization for their NGS data. Date: Wednesday, March 29, 2017 Time: 2:00 - 4:30 pm <...Read More
The CCR Bioinformatics Training and Education Program (BTEP) is pleased to organize a workshop on Partek Flow for scientists at NCI-Frederick. Partek Flow software is designed specifically for the analysis needs of next generation sequencing (NGS) applications including RNA, small RNA, and DNA sequencing. With an easy-to-use graphical interface, one can perform alignment, quantification, quality control, statistics and visualization for their NGS data. Date: Wednesday, March 29, 2017 Time: 2:00 - 4:30 pm Location: NCI-F Building 549, Scientific Library Training Room

Registration is required.

Note: The workshop is limited to 12 seats (10 seats with desktops available for use, and 2 seats for those who can bring their own laptops) For more information about the venue, please contact: Alan Doss Informationist, Scientific Library Email: dossal@mail.nih.gov Phone: 301-846-1093   WORKSHOP AGENDA   2:00 - 4:30 pm               RNA-Seq Analysis using Partek Flow                                        Presenter: Eric Seiser, PhD - Partek Field Application Specialist   An overview of getting started on the NIH Helix server and then a live demo of RNA-seq analysis in Partek Flow. The training will highlight key concepts in RNA-seq analysis and their implementation Flow.  This will be followed by a hands-on session utilizing Partek Flow to importing raw sequence data in fastq format from a published study, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation.

Students will learn how to use basic features of Partek Flow, including:

    •    Getting set up on NIH Helix server
    •    Importing data
    •    Performing QA/AC
    •    Alignment
    •    Gene/transcript abundance estimation
    •    Differential expression detection
    •    Go Enrichment analysis
    •    Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.)
    •    Microarray analysis and integration with RNA-seq data

 

Register
Organizer
BTEP
When
Wed, Mar 29, 2017 - 2:00 pm - 4:30 pm
Where
NCI-F Bldg549, Scientific Library Training Room
The CCR Bioinformatics Training and Education Program (BTEP) is pleased to organize a workshop on Partek Flow for scientists at NCI-Frederick. Partek Flow software is designed specifically for the analysis needs of next generation sequencing (NGS) applications including RNA, small RNA, and DNA sequencing. With an easy-to-use graphical interface, one can perform alignment, quantification, quality control, statistics and visualization for their NGS data. Date: Wednesday, March 29, 2017 Time: 2:00 - 4:30 pm Location: NCI-F Building 549, Scientific Library Training Room Registration is required. Note: The workshop is limited to 12 seats (10 seats with desktops available for use, and 2 seats for those who can bring their own laptops) For more information about the venue, please contact: Alan Doss Informationist, Scientific Library Email: dossal@mail.nih.gov Phone: 301-846-1093   WORKSHOP AGENDA   2:00 - 4:30 pm               RNA-Seq Analysis using Partek Flow                                        Presenter: Eric Seiser, PhD - Partek Field Application Specialist   An overview of getting started on the NIH Helix server and then a live demo of RNA-seq analysis in Partek Flow. The training will highlight key concepts in RNA-seq analysis and their implementation Flow.  This will be followed by a hands-on session utilizing Partek Flow to importing raw sequence data in fastq format from a published study, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation. Students will learn how to use basic features of Partek Flow, including:     •    Getting set up on NIH Helix server     •    Importing data     •    Performing QA/AC     •    Alignment     •    Gene/transcript abundance estimation     •    Differential expression detection     •    Go Enrichment analysis     •    Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.)     •    Microarray analysis and integration with RNA-seq data   2017-03-29 14:00:00 NCI-F Bldg549, Scientific Library Training Room In-Person BTEP 0 Partek Flow Workshop for CCR Scientists at NCI-Frederick
760
Description
Probing DNA-Protein Interactions This 2-day workshop, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Topics to be covered include: experimental design; read alignment; peak calling; and biological interpretation. A hands-on component will include tutorials on Genomatix, and a demo of the CCBR ChIP-Seq pipeline (featuring MACS and SICER). There will also be a session on mining public data from ENCODE and other databases. NOTE: ...Read More
Probing DNA-Protein Interactions This 2-day workshop, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Topics to be covered include: experimental design; read alignment; peak calling; and biological interpretation. A hands-on component will include tutorials on Genomatix, and a demo of the CCBR ChIP-Seq pipeline (featuring MACS and SICER). There will also be a session on mining public data from ENCODE and other databases. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.  Date: April 17-18, 2017 (Monday and Tuesday) Time: 9:30 am to 4:00 pm Location: NIH Bldg 10 FAES Classroom 4 (B1C205)   Registration required. Please click on 'Register Here' link at the bottom of this webpage. WORKSHOP AGENDA

Day 1 Morning

9:30 - 11:30 am             Introductory Lecture
                                      Presenter: Peter FitzGerald, PhD - CCR, NCI

The opening talk on ChIP-Seq will introduce the fundamentals and best practices for the technology, and provide the foundation for:

  • Understanding the methodolgy
  • Comparisons to related techniques
  • Data Analysis
    • Experimental Design
    • Quality Control 
    • Peak Calling (Different methodologies)
    • Major Sources of Error
    • Causes of Fail Experiments
    • Validation Methods
  • Sequence Specific Binding
    • Identification of Motifs
    • Overexpressed sequences
    • Pathways
  • Resources
    • Public Repositories
    • Literature References
    • Software listings
11:30 am - 12:30 pm               An Overview on Experimental Subtypes and Variations of ChIP-Seq
                                                 Presenter: Alexei Lobanov, PhD, CCBR
 
This talk will highlight innovative variations of the classical ChIP-Seq technique that provide different insights and information to analyze the epigenome. There will be discussion on the experimental differences, considerations to choose a subtype based on the research question, and considerations to analyze the data generated from these techniques. Some of the specific subtypes that will be presented include:
  • DNase-seq
  • Assay for Transposase-Accessible Chromatin-seq (ATAC-seq)
  • Formaldehyde-assisted Isolation of Regulatory Elelments–seq (FAIRE-seq) reveal regions of open chromatin, not associated with any protein
  • MNase-seq identifies specifically positioned nucleosomes
  12:30 - 1:00 pm                       LUNCH BREAK   

 

Day 1 Afternoon

1:00 - 4: 00 pm              Analysis of ChIP-Seq data: Raw Data to Results                                        Presenter: Bong-Hyun Kim, PhD, CCBR   This session will cover, in comprehensive detail, the analytical pipeline that is implemented by CCBR. After reinforcing critical aspects and best practices to conduct an effective ChIP-Seq experiment, attendees will have the opportunity to follow a demo of the CCBR ChIP-Seq workflow using an example dataset. There will be detailed review of data QA/QC, visualization of results and the types of outputs from the pipeline.

Day 2 Morning

9:30 am -12:30 pm     Hands-on Tutorial for Analysis of ChIP-Seq data with the Genomatix Genome Analyzer (GGA)                                     Presenter: Thomas Werner, PhD  

The presenter will guide participants through hands-on-training with a ChIP-Seq experiment on the GGA software tool. The training will cover aspects from analysis of BAM files through annotation & statistics, use visualization tools from the Genomatix GePS pathway system and Genome Browser, and finish with a higher level downstream promoter analysis.

At the end of the workshop, participants will be able to:
1. Set up and generate a graphical overview of a ChIP-seq workflow that will include loading a sample data set into the workflow, discussion of parameters, executing the analysis, and examining the results.
2. Analyze results using:
  • Annotation & Statistics application to gain a deeper understanding of their content
  • Correlation of ChIP-Seq peaks with actual transcriptional changes to define real Transcription Factor (TF)-targets
  • Analysis of these target genes using GePS (Genomatix pathway system)
  • Data visualization in feature-rich Genome Browser
3. Do further downstream analysis by selecting peaks (bed files), converting them to sequence files, and use FrameWorker to find putative MORE (Multiple Organized Regulatory Elements) cassettes.
4. Use ModelInspector to determine whether a MORE-cassette is relevant for the biology in question.

 

12:30 - 1:30 pm                       LUNCH BREAK   

Day 2 Afternoon

1:30 - 3: 30 pm          Mining ChIP-Seq data from Public Databases                                    Presenter: Bong-Hyun Kim, PhD, CCBR   In this session, attendees will be able to learn how to explore information from publicly available ChIP-seq databases, and extract meaningful data that could be used for your research. Some of the databases to be discussed are:
  • RegulomeDB
  • ENCODE (ENCyclopedia Of DNA Elements)
  • Epigenome Roadmap
  • Mouse Encode & modENCODE
  • Cistrome
  • JASPAR database
Register
Organizer
BTEP
When
Mon, Apr 17 - Tue, Apr 18, 2017 -9:30 am - 4:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
Probing DNA-Protein Interactions This 2-day workshop, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Topics to be covered include: experimental design; read alignment; peak calling; and biological interpretation. A hands-on component will include tutorials on Genomatix, and a demo of the CCBR ChIP-Seq pipeline (featuring MACS and SICER). There will also be a session on mining public data from ENCODE and other databases. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.  Date: April 17-18, 2017 (Monday and Tuesday) Time: 9:30 am to 4:00 pm Location: NIH Bldg 10 FAES Classroom 4 (B1C205)   Registration required. Please click on 'Register Here' link at the bottom of this webpage. WORKSHOP AGENDA Day 1 Morning 9:30 - 11:30 am             Introductory Lecture                                      Presenter: Peter FitzGerald, PhD - CCR, NCI The opening talk on ChIP-Seq will introduce the fundamentals and best practices for the technology, and provide the foundation for: Understanding the methodolgy Comparisons to related techniques Data Analysis Experimental Design Quality Control  Peak Calling (Different methodologies) Major Sources of Error Causes of Fail Experiments Validation Methods Sequence Specific Binding Identification of Motifs Overexpressed sequences Pathways Resources Public Repositories Literature References Software listings 11:30 am - 12:30 pm               An Overview on Experimental Subtypes and Variations of ChIP-Seq                                                 Presenter: Alexei Lobanov, PhD, CCBR   This talk will highlight innovative variations of the classical ChIP-Seq technique that provide different insights and information to analyze the epigenome. There will be discussion on the experimental differences, considerations to choose a subtype based on the research question, and considerations to analyze the data generated from these techniques. Some of the specific subtypes that will be presented include: DNase-seq Assay for Transposase-Accessible Chromatin-seq (ATAC-seq) Formaldehyde-assisted Isolation of Regulatory Elelments–seq (FAIRE-seq) reveal regions of open chromatin, not associated with any protein MNase-seq identifies specifically positioned nucleosomes   12:30 - 1:00 pm                       LUNCH BREAK      Day 1 Afternoon 1:00 - 4: 00 pm              Analysis of ChIP-Seq data: Raw Data to Results                                        Presenter: Bong-Hyun Kim, PhD, CCBR   This session will cover, in comprehensive detail, the analytical pipeline that is implemented by CCBR. After reinforcing critical aspects and best practices to conduct an effective ChIP-Seq experiment, attendees will have the opportunity to follow a demo of the CCBR ChIP-Seq workflow using an example dataset. There will be detailed review of data QA/QC, visualization of results and the types of outputs from the pipeline. Day 2 Morning 9:30 am -12:30 pm     Hands-on Tutorial for Analysis of ChIP-Seq data with the Genomatix Genome Analyzer (GGA)                                     Presenter: Thomas Werner, PhD   The presenter will guide participants through hands-on-training with a ChIP-Seq experiment on the GGA software tool. The training will cover aspects from analysis of BAM files through annotation & statistics, use visualization tools from the Genomatix GePS pathway system and Genome Browser, and finish with a higher level downstream promoter analysis. At the end of the workshop, participants will be able to: 1. Set up and generate a graphical overview of a ChIP-seq workflow that will include loading a sample data set into the workflow, discussion of parameters, executing the analysis, and examining the results. 2. Analyze results using: Annotation & Statistics application to gain a deeper understanding of their content Correlation of ChIP-Seq peaks with actual transcriptional changes to define real Transcription Factor (TF)-targets Analysis of these target genes using GePS (Genomatix pathway system) Data visualization in feature-rich Genome Browser 3. Do further downstream analysis by selecting peaks (bed files), converting them to sequence files, and use FrameWorker to find putative MORE (Multiple Organized Regulatory Elements) cassettes. 4. Use ModelInspector to determine whether a MORE-cassette is relevant for the biology in question.   12:30 - 1:30 pm                       LUNCH BREAK    Day 2 Afternoon 1:30 - 3: 30 pm          Mining ChIP-Seq data from Public Databases                                    Presenter: Bong-Hyun Kim, PhD, CCBR   In this session, attendees will be able to learn how to explore information from publicly available ChIP-seq databases, and extract meaningful data that could be used for your research. Some of the databases to be discussed are: RegulomeDB ENCODE (ENCyclopedia Of DNA Elements) Epigenome Roadmap Mouse Encode & modENCODE Cistrome JASPAR database 2017-04-17 09:30:00 Bldg 10 FAES room 4 (B1C205) In-Person Peter FitzGerald (GAU),Alexei Lobanov (CCBR),Thomas Werner (Genomatix) BTEP 0 Stepping into the Regulome: ChIP-Seq/ENCODE Data Analysis
754
Description
This talk will be an introduction to Methyl-seq, where you will learn about leveraging the power of next-generation sequencing (NGS), both genome-wide and targeted approaches, that can provide insight into methylation patterns at a single nucleotide level. Members from the CCR-SF Informatics Group will be sharing their expertise and experience with this technology and emerging applications. Some of the topics covered will be:
  • Experimental design consideration
  • Sample submission, sequencing ...Read More
This talk will be an introduction to Methyl-seq, where you will learn about leveraging the power of next-generation sequencing (NGS), both genome-wide and targeted approaches, that can provide insight into methylation patterns at a single nucleotide level. Members from the CCR-SF Informatics Group will be sharing their expertise and experience with this technology and emerging applications. Some of the topics covered will be:
  • Experimental design consideration
  • Sample submission, sequencing and data processing workflows at SF
  • Brief overview of Methyl-Seq technologies
  • Methyl-Seq protocols available at SF and evaluation of newer applications
  • Methyl-Seq raw data processing, mapping and QC
  • Methylation count analysis for single sample
  • Multi-sample differential methylation analysis

Note: Please select NIH Main Campus as the location on the registration page. This event is being organized only on the Bethesda campus, not Frederick.

For those outside of campus interested in attending, please join via WebEx -

UR: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=ec897170ff6119f8baea6d8...
Event number: 732 957 108
Event password: eSb3A4h$

Register
Organizer
BTEP
When
Tue, Apr 25, 2017 - 2:00 pm - 4:00 pm
Where
NIH Bethesda B37, Rm 4041/4107
This talk will be an introduction to Methyl-seq, where you will learn about leveraging the power of next-generation sequencing (NGS), both genome-wide and targeted approaches, that can provide insight into methylation patterns at a single nucleotide level. Members from the CCR-SF Informatics Group will be sharing their expertise and experience with this technology and emerging applications. Some of the topics covered will be: Experimental design consideration Sample submission, sequencing and data processing workflows at SF Brief overview of Methyl-Seq technologies Methyl-Seq protocols available at SF and evaluation of newer applications Methyl-Seq raw data processing, mapping and QC Methylation count analysis for single sample Multi-sample differential methylation analysis Note: Please select NIH Main Campus as the location on the registration page. This event is being organized only on the Bethesda campus, not Frederick. For those outside of campus interested in attending, please join via WebEx - UR: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=ec897170ff6119f8baea6d8... Event number: 732 957 108 Event password: eSb3A4h$ 2017-04-25 14:00:00 NIH Bethesda B37, Rm 4041/4107 In-Person Yongmei Zhao (CCR-SF IFX Group) BTEP 0 Introduction to Methyl-Seq: Experimental Design, Technology and Data Analysis
753
Description
The CCR Bioinformatics Training and Education Program (BTEP) is pleased to organize a workshop on Partek Genomics Suite for scientists at NCI-Frederick. Partek® Genomics Suite® is a versatile scientific software with an easy-to-use graphical interface for expression and genomic data analysis, as well as statistics and visualization needs. There are comprehensive workflows for many data types, including microarray, qPCR platforms, and NGS as well. This workshop will focus on the gene expression workflows ...Read More
The CCR Bioinformatics Training and Education Program (BTEP) is pleased to organize a workshop on Partek Genomics Suite for scientists at NCI-Frederick. Partek® Genomics Suite® is a versatile scientific software with an easy-to-use graphical interface for expression and genomic data analysis, as well as statistics and visualization needs. There are comprehensive workflows for many data types, including microarray, qPCR platforms, and NGS as well. This workshop will focus on the gene expression workflows from microarray, discuss other relevant analytical modules available in the PGS system, and provide attendees an opportunity to perform hands-on training on this software. Date: Wednesday, April 26, 2017 Time: 11:00 am - 4:00 pm Location: NCI-F Building 549, Scientific Library Training Room Presenter: Eric Seiser, PhD - Partek Field Application Specialist

Registration is required.

Note: The workshop is limited to 12 seats (10 seats with desktops available for use, and 2 seats for those who can bring their own laptops).

For more information about the venue, please contact: Alan Doss Informationist, Scientific Library Email: dossal@mail.nih.gov Phone: 301-846-1093   WORKSHOP AGENDA  

11:00 am - 12:00 pm               Introduction to Partek Genomics Suite

This will be an overview of the software, including statistics, workflows and other analytical modules relevant for gene expression analysis.

12:00 - 1:00 pm                        LUNCH BREAK

1:00 - 4:00 pm                         Comprehensive hands-on training on PGS

This session will be covering end-to-end analysis of gene expression data (example set will be provided). The complete gene expression analysis workflow will be followed that includes QA/QC of the data, differential expression detection, and biological interpretation using Partek Pathway. The presenter will also show participants how to perform batch effect removal, integration with other relevant data, and generating applicable visuals for the data.

 

Register
Organizer
BTEP
When
Wed, Apr 26, 2017 - 11:00 am - 4:00 pm
Where
NCI-F Bldg549 Scientific Library Training Room
The CCR Bioinformatics Training and Education Program (BTEP) is pleased to organize a workshop on Partek Genomics Suite for scientists at NCI-Frederick. Partek® Genomics Suite® is a versatile scientific software with an easy-to-use graphical interface for expression and genomic data analysis, as well as statistics and visualization needs. There are comprehensive workflows for many data types, including microarray, qPCR platforms, and NGS as well. This workshop will focus on the gene expression workflows from microarray, discuss other relevant analytical modules available in the PGS system, and provide attendees an opportunity to perform hands-on training on this software. Date: Wednesday, April 26, 2017 Time: 11:00 am - 4:00 pm Location: NCI-F Building 549, Scientific Library Training Room Presenter: Eric Seiser, PhD - Partek Field Application Specialist Registration is required. Note: The workshop is limited to 12 seats (10 seats with desktops available for use, and 2 seats for those who can bring their own laptops). For more information about the venue, please contact: Alan Doss Informationist, Scientific Library Email: dossal@mail.nih.gov Phone: 301-846-1093   WORKSHOP AGENDA   11:00 am - 12:00 pm               Introduction to Partek Genomics Suite This will be an overview of the software, including statistics, workflows and other analytical modules relevant for gene expression analysis. 12:00 - 1:00 pm                        LUNCH BREAK 1:00 - 4:00 pm                         Comprehensive hands-on training on PGS This session will be covering end-to-end analysis of gene expression data (example set will be provided). The complete gene expression analysis workflow will be followed that includes QA/QC of the data, differential expression detection, and biological interpretation using Partek Pathway. The presenter will also show participants how to perform batch effect removal, integration with other relevant data, and generating applicable visuals for the data.   2017-04-26 11:00:00 NCI-F Bldg549 Scientific Library Training Room In-Person BTEP 0 Partek Genomics Suite Workshop at NCI-Frederick
752
Description
Statistics for Biologists A high-level overview of basic statistical tools and statistical reasoning skills used in the study of biological data will be presented in this installment. This is an introductory course, requiring very little prerequisite statistical expertise. Specific topics include: Exploratory data analysis, Statistical inference, and Sample size considerations in study design.

The first of these series will cover the following topics:

  • Exploratory Data Analysis (EDA) <...Read More
Statistics for Biologists A high-level overview of basic statistical tools and statistical reasoning skills used in the study of biological data will be presented in this installment. This is an introductory course, requiring very little prerequisite statistical expertise. Specific topics include: Exploratory data analysis, Statistical inference, and Sample size considerations in study design.

The first of these series will cover the following topics:

  • Exploratory Data Analysis (EDA)
    • Data/ Plot Types
    • Conditional Probability/ Contingency Tables
  • Central Limit Theorem
    • Population Samples
    • Sampling
  • Inference
    • Hypothesis Testing; Types of errors
    • P-values and beyond (p-hacking)
    • Confidence Intervals
  • Power and Sample Size Calculations
Register
Organizer
BTEP
When
Tue, May 16, 2017 - 2:30 pm - 4:30 pm
Where
In-Person
Statistics for Biologists A high-level overview of basic statistical tools and statistical reasoning skills used in the study of biological data will be presented in this installment. This is an introductory course, requiring very little prerequisite statistical expertise. Specific topics include: Exploratory data analysis, Statistical inference, and Sample size considerations in study design. The first of these series will cover the following topics: Exploratory Data Analysis (EDA) Data/ Plot Types Conditional Probability/ Contingency Tables Central Limit Theorem Population Samples Sampling Inference Hypothesis Testing; Types of errors P-values and beyond (p-hacking) Confidence Intervals Power and Sample Size Calculations 2017-05-16 14:30:00 In-Person S. Ravichandran (Advanced Biomedical Computing Center Leidos Biomed FNLCR) BTEP 0 Statistics for Biologists Series
759
Description
Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding of the ...Read More
Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding of the interplay between spatially related biological elements. These visualization, pathways and enrichment analysis tools are relevant whether one is interested in expression analysis (RNA-Seq), variant detection (Exome-Seq), protein binding (ChIP-Seq), or any other genome scale data.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please also review the software requirements and instructions provided below.

  • Registration is required. Important: Registration will close on Thursday, May 18th at 5:00 pm.
  • Note: On the registration page, you have the option to (a) attend all four sessions, or (b) select the session/s that you would like to attend. Please choose carefully and only if you are committed to attending the session/s.
Software requirements and instructions for hands-on training on the respective applications:
  • Preferred browsers are Chrome, Firefox, or Safari for all sessions. Internet Explorer (IE) will not be compatible with most of these analysis tools.
  • Please bring your PIV cards (and card readers) in order to set up VPN or access to NIH Wireless in the Building 10 FAES classroom.
  • For the Ingenuity Pathway Analysis (IPA) session, please make sure you have an active account and the application opens up succesfully on your computer prior to the workshop. Kindy submit a request to NCI IT (https://service.cancer.gov) for accessing this software through the NIH institutional license. Review the instructions provided in the PDF file under Course Material1 below. Additionally, please make sure your computer meets our specifications for running IPA, as described here: http://ingenuity.force.com/ipa/IPATutorials?id=kA2500000008ak0CAA
  • For the MetaCore session, please make sure that you have an active account and that the web-based application opens up correcetly on your computer browser. Kindy submit a request to NCI IT (https://service.cancer.gov) for setting up an account to access this software through the NIH institutional license. If any issues arise, please send an email to Maria Ryan - maria.ryan@Clarivate.com. Additionally, please download the training files under Course Materials2 below.
  • Training accounts for the open-source tools (CRAVAT, MuPIT, and NG-CHM) will be provided at the workshop
  • For QluCore Omics Explorer (QOE) - please click this link: http://www.qlucore.com/evaluation. After registering on their website, please download the appropriate (Windows or Mac) QOE Trial Software onto your computer. You will need administrative privileges on the computer, so please submit a request to IT to complete the installation if necessary. Once installed, access can be activated by using the trial license file (.lic) that is contained within the file provided under Course Materials3 below. That unzipped folder also contains training files required for the workshop.
WORKSHOP AGENDA

Day 1 - Monday, May 22, 2017

9:30 - 10:00 am                      Introduction to Workshop Concepts and Sessions                                                Presenter: Anand S. Merchant, MD, PhD   10:00 am - 12:30 pm             MetaCore                                               Presenter: Matthew Wampole, PhD   MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. In this session, we’ll be analyzing data published recently in PNAS about NOTCH1 signaling in chronic lymphocytic leukemia (http://www.pnas.org/content/114/14/E2911.abstract). From this publication, we’ll explore what pathways are enriched by up regulated genes by ICN1-HA from RNA-seq and bound to NOTCH1 in CHiP-seq experiments. Use overconnectivity analysis to associate transcription factors with the regulation of these upregulated genes.  Compare RNA-seq expression data chronic lymphocytic leukemia cells derived from patients with and without NOTCH1 mutations and expression. Finally, we’ll use the ICN1-HA induced up-regulated gene signature to compare against publically available GEO microarray data to find similar signatures in other diseases.   12:30 - 1:00 pm                    LUNCH BREAK   1:00 -  4:00 pm                     QluCore Omics Explorer                                               Presenter: Carl-Johan Ivarsson, PhD   This session will include introduction to and exercises on basic features and functionality in Qlucore Omics Explorer. It is intended for new users and does not require that you have previous experience with Qlucore Omics Explorer. After the training you should be able to do the following using Qlucore Omics Explorer:
  • Import data and annotations
  • Present data with different plot types (PCA, heatmap, bar, box...)
  • Identify discriminating variables using basic statistical test
  • Use visualization to enhance analysis and interpret results
  • Explore large data sets - find structure, patterns and subclusters in data
  • Export variable lists and images

Day 2 - Tuesday, May 23, 2017

9:30 am – 12:30 pm             Open-Source Tools for Analysis and Visualization of NGS Data (CRAVAT, MuPIT, and NG-CHM)
                                             Presenter: Michael Ryan, PhD, Johns Hopkins/MD Anderson/ In Silico Solutions
 
Cancer-Related Analysis of VAriants Toolkit, or CRAVAT (www.cravat.us), is a free tool for high-throughput analysis of human sequencing variants developed by the Karchin lab at Johns Hopkins and In Silico Solutions.  CRAVAT accepts very large variant data files containing single nucleotide substitutions as well as indels and returns a wide variety of annotations and scores that help with identification and exploration of important variants.  The workshop will provide some background on CRAVAT and lots of hands-on exercises to learn how to use the tool and interpret the results.
 
Mutation Position Imaging Toolbox, or MuPIT (www.mupit.icm.jhu/MuPIT_Interactive/), is a sister tool to CRAVAT that shows human mutations on 3D protein structures.  MuPIT analysis enables identification of mutational clusters and proximity to functional domains in 3D space that are not obvious from the linear protein sequence. The focus of the workshop will be a series of exercises to learn how to visualize your mutations in MuPIT, how CRAVAT and MuPIT are integrated, and how to manipulate, investigate, and understand the results.
 
Next Generation Clustered Heat Maps, or NG-CHM(http://bioinformatics.mdanderson.org/chm), is a tool developed by MD Anderson and In Silico Solutions to build clustered heat maps for genomic data. It provides interactive heat maps that enable the user to zoom and pan across the heat map, alter its color scheme, generate production quality PDFs, and link out from rows, columns, and individual heat map entries to related statistics, databases and other information.

12:30 - 1:00 pm                    LUNCH BREAK   1:00 -  4:00 pm                     Ingenuity Pathway Analysis (IPA)                                               Presenter: Jennifer Poitras, PhD   In this session, you will get an opportunity to use IPA for maximizing the biological interpretation of gene, transcript & protein expression data using different modules of the tool. There will hands-on exercises from file uploading to interpreting results, visualizing and integrating multi-omics data, understanding and mining the Ingenuity Knowledge Base (IKB), core analysis, causal networks, and many more new functionalities.
Register
Organizer
BTEP
When
Mon, May 22 - Tue, May 23, 2017 -9:30 am - 4:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding of the interplay between spatially related biological elements. These visualization, pathways and enrichment analysis tools are relevant whether one is interested in expression analysis (RNA-Seq), variant detection (Exome-Seq), protein binding (ChIP-Seq), or any other genome scale data. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please also review the software requirements and instructions provided below. Registration is required. Important: Registration will close on Thursday, May 18th at 5:00 pm. Note: On the registration page, you have the option to (a) attend all four sessions, or (b) select the session/s that you would like to attend. Please choose carefully and only if you are committed to attending the session/s. Software requirements and instructions for hands-on training on the respective applications: Preferred browsers are Chrome, Firefox, or Safari for all sessions. Internet Explorer (IE) will not be compatible with most of these analysis tools. Please bring your PIV cards (and card readers) in order to set up VPN or access to NIH Wireless in the Building 10 FAES classroom. For the Ingenuity Pathway Analysis (IPA) session, please make sure you have an active account and the application opens up succesfully on your computer prior to the workshop. Kindy submit a request to NCI IT (https://service.cancer.gov) for accessing this software through the NIH institutional license. Review the instructions provided in the PDF file under Course Material1 below. Additionally, please make sure your computer meets our specifications for running IPA, as described here: http://ingenuity.force.com/ipa/IPATutorials?id=kA2500000008ak0CAA For the MetaCore session, please make sure that you have an active account and that the web-based application opens up correcetly on your computer browser. Kindy submit a request to NCI IT (https://service.cancer.gov) for setting up an account to access this software through the NIH institutional license. If any issues arise, please send an email to Maria Ryan - maria.ryan@Clarivate.com. Additionally, please download the training files under Course Materials2 below. Training accounts for the open-source tools (CRAVAT, MuPIT, and NG-CHM) will be provided at the workshop For QluCore Omics Explorer (QOE) - please click this link: http://www.qlucore.com/evaluation. After registering on their website, please download the appropriate (Windows or Mac) QOE Trial Software onto your computer. You will need administrative privileges on the computer, so please submit a request to IT to complete the installation if necessary. Once installed, access can be activated by using the trial license file (.lic) that is contained within the file provided under Course Materials3 below. That unzipped folder also contains training files required for the workshop. WORKSHOP AGENDA Day 1 - Monday, May 22, 2017 9:30 - 10:00 am                      Introduction to Workshop Concepts and Sessions                                                Presenter: Anand S. Merchant, MD, PhD   10:00 am - 12:30 pm             MetaCore                                               Presenter: Matthew Wampole, PhD   MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. In this session, we’ll be analyzing data published recently in PNAS about NOTCH1 signaling in chronic lymphocytic leukemia (http://www.pnas.org/content/114/14/E2911.abstract). From this publication, we’ll explore what pathways are enriched by up regulated genes by ICN1-HA from RNA-seq and bound to NOTCH1 in CHiP-seq experiments. Use overconnectivity analysis to associate transcription factors with the regulation of these upregulated genes.  Compare RNA-seq expression data chronic lymphocytic leukemia cells derived from patients with and without NOTCH1 mutations and expression. Finally, we’ll use the ICN1-HA induced up-regulated gene signature to compare against publically available GEO microarray data to find similar signatures in other diseases.   12:30 - 1:00 pm                    LUNCH BREAK   1:00 -  4:00 pm                     QluCore Omics Explorer                                               Presenter: Carl-Johan Ivarsson, PhD   This session will include introduction to and exercises on basic features and functionality in Qlucore Omics Explorer. It is intended for new users and does not require that you have previous experience with Qlucore Omics Explorer. After the training you should be able to do the following using Qlucore Omics Explorer: Import data and annotations Present data with different plot types (PCA, heatmap, bar, box...) Identify discriminating variables using basic statistical test Use visualization to enhance analysis and interpret results Explore large data sets - find structure, patterns and subclusters in data Export variable lists and images Day 2 - Tuesday, May 23, 2017 9:30 am – 12:30 pm             Open-Source Tools for Analysis and Visualization of NGS Data (CRAVAT, MuPIT, and NG-CHM)                                              Presenter: Michael Ryan, PhD, Johns Hopkins/MD Anderson/ In Silico Solutions   Cancer-Related Analysis of VAriants Toolkit, or CRAVAT (www.cravat.us), is a free tool for high-throughput analysis of human sequencing variants developed by the Karchin lab at Johns Hopkins and In Silico Solutions.  CRAVAT accepts very large variant data files containing single nucleotide substitutions as well as indels and returns a wide variety of annotations and scores that help with identification and exploration of important variants.  The workshop will provide some background on CRAVAT and lots of hands-on exercises to learn how to use the tool and interpret the results.   Mutation Position Imaging Toolbox, or MuPIT (www.mupit.icm.jhu/MuPIT_Interactive/), is a sister tool to CRAVAT that shows human mutations on 3D protein structures.  MuPIT analysis enables identification of mutational clusters and proximity to functional domains in 3D space that are not obvious from the linear protein sequence. The focus of the workshop will be a series of exercises to learn how to visualize your mutations in MuPIT, how CRAVAT and MuPIT are integrated, and how to manipulate, investigate, and understand the results.   Next Generation Clustered Heat Maps, or NG-CHM(http://bioinformatics.mdanderson.org/chm), is a tool developed by MD Anderson and In Silico Solutions to build clustered heat maps for genomic data. It provides interactive heat maps that enable the user to zoom and pan across the heat map, alter its color scheme, generate production quality PDFs, and link out from rows, columns, and individual heat map entries to related statistics, databases and other information. 12:30 - 1:00 pm                    LUNCH BREAK   1:00 -  4:00 pm                     Ingenuity Pathway Analysis (IPA)                                               Presenter: Jennifer Poitras, PhD   In this session, you will get an opportunity to use IPA for maximizing the biological interpretation of gene, transcript & protein expression data using different modules of the tool. There will hands-on exercises from file uploading to interpreting results, visualizing and integrating multi-omics data, understanding and mining the Ingenuity Knowledge Base (IKB), core analysis, causal networks, and many more new functionalities. 2017-05-22 09:30:00 Bldg 10 FAES room 4 (B1C205) In-Person BTEP 0 Making Sense of the Data: Visualization, Pathways, and Enrichment Analysis (2-day)
758
Description
Harvesting the Wealth of TCGA Data

The Cancer Genome Atlas (TCGA) is a large-scale study that has catalogued genomic data accumulated from more than 20 different types of cancer including mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation. Being publicly distributed, it has become a major resource for cancer researchers for target discovery, biological interpretation and assessment of the clinical impact of genes of interest. This workshop will familiarize the audience ...Read More

Harvesting the Wealth of TCGA Data

The Cancer Genome Atlas (TCGA) is a large-scale study that has catalogued genomic data accumulated from more than 20 different types of cancer including mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation. Being publicly distributed, it has become a major resource for cancer researchers for target discovery, biological interpretation and assessment of the clinical impact of genes of interest. This workshop will familiarize the audience with the types of data available and analytical tools that enable end-users to easily and effectively mine TCGA data. It will provide training on two applications: (a) cBioPortal for Cancer Genomics, an open-source tool, and (b) TCGA Premier through BioDiscovery Nexus, an NCI-licensed commercial tool.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer, please indicate such in the Comment section on the registration form. 

Date: Monday, June 19, 2017 Time: 9:30 am - 4:00 pm Location: Building 10, FAES Classroom B1C205 (#4) WORKSHOP AGENDA 9:30 am - 12: 30 pm             TCGA Data on cBioPortal: Interactions and Interrogations                                                Presenter: Anand S. Merchant, MD, PhD   The cBio Cancer Genomics Portal (http://www.cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. It significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects. Additionally, it empowers researchers to translate these rich data sets into biologic insights and clinical applications. Required reading ahead of workshop: Two publications (see Course Material1 and 2 below).   Computer requirements for cBioPortal:
  • A personal computer or computing device with an Internet browser (Tested browsers: Internet Explorer 11.0 and above, Firefox 3.0 and above, Safari and Google Chrome) with Javascript enabled. A Java Runtime Environment is needed for launching the Integrative Genomics Viewer (IGV). A Vector graphic editor is necessary for visualizing and editing the SVG file of OncoPrints downloaded from the cBioPortal. Examples of software supporting SVG are Adobe Illustrator (http://www.adobe.com/products/illustrator.html) and Inkscape (http://inkscape.org/).
12:30 - 1:00 pm     LUNCH BREAK 1:00 - 4:00 pm      BioDiscovery’s TCGA Premier and Nexus Copy Number: Integrated analysis of TCGA data using Nexus DB
                              Presenter: Andrea O Hara, PhD
 
NCI’s site license includes unlimited access to TCGA Premier, a database of re-processed, curated and reviewed TCGA samples. Nexus Copy Number is a platform independent copy number analysis software that includes co-visualization of sequence variants and gene expression data at both the individual and population wide levels. With an easy to use visual interface, Nexus Copy Number allows for quick review and detailed analysis of population-wide copy number alterations across the entire genome. In this workshop, you will learn how to use Nexus Copy Number software to mine TCGA copy number data. The Cancer Genome Atlas (TCGA) contains various types of genomic data from a wide variety of cancers, including several rare tumor types. The training session will focus on access of the TCGA data within the software and a detailed evaluation of one TCGA data set to identify statistically significant changes within the sample population.
 
Learning Objectives:
*   Access and integration of CNV, sequence variant and RNA-Seq expression TCGA data directly from Nexus.
*   Visualization and statistical approaches for discovery.
*   Sample stratification by clinical annotation factors or biomarkers.
*   Finding CNVs predictive of survival or other outcome data.
*   Generate publication-ready figures and charts during analysis.
Register
Organizer
BTEP
When
Mon, Jun 19, 2017 - 9:30 am - 4:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
Harvesting the Wealth of TCGA Data The Cancer Genome Atlas (TCGA) is a large-scale study that has catalogued genomic data accumulated from more than 20 different types of cancer including mutations, copy number variation, mRNA and miRNA gene expression, and DNA methylation. Being publicly distributed, it has become a major resource for cancer researchers for target discovery, biological interpretation and assessment of the clinical impact of genes of interest. This workshop will familiarize the audience with the types of data available and analytical tools that enable end-users to easily and effectively mine TCGA data. It will provide training on two applications: (a) cBioPortal for Cancer Genomics, an open-source tool, and (b) TCGA Premier through BioDiscovery Nexus, an NCI-licensed commercial tool. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer, please indicate such in the Comment section on the registration form.  Date: Monday, June 19, 2017 Time: 9:30 am - 4:00 pm Location: Building 10, FAES Classroom B1C205 (#4) WORKSHOP AGENDA 9:30 am - 12: 30 pm             TCGA Data on cBioPortal: Interactions and Interrogations                                                Presenter: Anand S. Merchant, MD, PhD   The cBio Cancer Genomics Portal (http://www.cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. It significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects. Additionally, it empowers researchers to translate these rich data sets into biologic insights and clinical applications. Required reading ahead of workshop: Two publications (see Course Material1 and 2 below).   Computer requirements for cBioPortal: A personal computer or computing device with an Internet browser (Tested browsers: Internet Explorer 11.0 and above, Firefox 3.0 and above, Safari and Google Chrome) with Javascript enabled. A Java Runtime Environment is needed for launching the Integrative Genomics Viewer (IGV). A Vector graphic editor is necessary for visualizing and editing the SVG file of OncoPrints downloaded from the cBioPortal. Examples of software supporting SVG are Adobe Illustrator (http://www.adobe.com/products/illustrator.html) and Inkscape (http://inkscape.org/). 12:30 - 1:00 pm     LUNCH BREAK 1:00 - 4:00 pm      BioDiscovery’s TCGA Premier and Nexus Copy Number: Integrated analysis of TCGA data using Nexus DB                              Presenter: Andrea O Hara, PhD   NCI’s site license includes unlimited access to TCGA Premier, a database of re-processed, curated and reviewed TCGA samples. Nexus Copy Number is a platform independent copy number analysis software that includes co-visualization of sequence variants and gene expression data at both the individual and population wide levels. With an easy to use visual interface, Nexus Copy Number allows for quick review and detailed analysis of population-wide copy number alterations across the entire genome. In this workshop, you will learn how to use Nexus Copy Number software to mine TCGA copy number data. The Cancer Genome Atlas (TCGA) contains various types of genomic data from a wide variety of cancers, including several rare tumor types. The training session will focus on access of the TCGA data within the software and a detailed evaluation of one TCGA data set to identify statistically significant changes within the sample population.  Learning Objectives: *   Access and integration of CNV, sequence variant and RNA-Seq expression TCGA data directly from Nexus. *   Visualization and statistical approaches for discovery. *   Sample stratification by clinical annotation factors or biomarkers. *   Finding CNVs predictive of survival or other outcome data. *   Generate publication-ready figures and charts during analysis. 2017-06-19 09:30:00 Bldg 10 FAES room 4 (B1C205) In-Person BTEP 0 The Art and Science of Data Mining (1-day)
751
Description
NGS Series Open Forum: Meet the Bioinformatics Experts You are invited to a Q & A session with a panel of bioinformatics analysts, most of whom were presenters at the BTEP NGS Workshop Series. The goal of this forum is to assist individuals who have encountered problems/issues attempting to analyze NGS data after having participated in one of the workshops earlier in the year.  We will also attempt to answer questions about ...Read More
NGS Series Open Forum: Meet the Bioinformatics Experts You are invited to a Q & A session with a panel of bioinformatics analysts, most of whom were presenters at the BTEP NGS Workshop Series. The goal of this forum is to assist individuals who have encountered problems/issues attempting to analyze NGS data after having participated in one of the workshops earlier in the year.  We will also attempt to answer questions about bioinformatics software packages or tools licensed by CCR/NCI. Each attendee is requested to send in questions ahead of the event (this will increase the chances of having a suitable answer for your specific problem). Questions related to the same topic and/or pertaining to a similar theme will be summarized and/or generalized so as to benefit all attendees. If a question is not answered during the session, it will be forwarded to the most appropriate person/group/vendor to provide relevant followup. CCR Researchers who had registered for any of the sessions between January through June 2017 will be given preference for attending this event. Representative examples of the types of questions that one could ask are mentioned below:
  • What are the most important metrics to rank/filter variants?
  • Which file formats work best for viewing NGS data in different genome browsers?
  • How would one assess the quality of RNA-Seq (or other NGS data) from starting material to final results?
Note: The panel will make every effort to answer all questions, and may even have slides/ visuals to aid in explaining the solution. Given the time frame and intent of this event, there will be no scope for a one-on-one consultation, demo of pipelines/ software applications, or review of data.
Register
Organizer
BTEP
When
Tue, Jun 27, 2017 - 2:30 pm - 4:30 pm
Where
In-Person
NGS Series Open Forum: Meet the Bioinformatics Experts You are invited to a Q & A session with a panel of bioinformatics analysts, most of whom were presenters at the BTEP NGS Workshop Series. The goal of this forum is to assist individuals who have encountered problems/issues attempting to analyze NGS data after having participated in one of the workshops earlier in the year.  We will also attempt to answer questions about bioinformatics software packages or tools licensed by CCR/NCI. Each attendee is requested to send in questions ahead of the event (this will increase the chances of having a suitable answer for your specific problem). Questions related to the same topic and/or pertaining to a similar theme will be summarized and/or generalized so as to benefit all attendees. If a question is not answered during the session, it will be forwarded to the most appropriate person/group/vendor to provide relevant followup. CCR Researchers who had registered for any of the sessions between January through June 2017 will be given preference for attending this event. Representative examples of the types of questions that one could ask are mentioned below: What are the most important metrics to rank/filter variants? Which file formats work best for viewing NGS data in different genome browsers? How would one assess the quality of RNA-Seq (or other NGS data) from starting material to final results? Note: The panel will make every effort to answer all questions, and may even have slides/ visuals to aid in explaining the solution. Given the time frame and intent of this event, there will be no scope for a one-on-one consultation, demo of pipelines/ software applications, or review of data. 2017-06-27 14:30:00 In-Person Panel of Bioinformatics Analysts (CCR CBIIT FNLCR) BTEP 0 NGS Series Open Forum
830
Description
UCSC Xena (http://xena.ucsc.edu) is a web-based, visual exploration tool for all modes of multi-omic data and associated annotations. Xena has several seminal cancer datasets pre-loaded and ready for visualization including TCGA, ICGC, GTEx and more. Our public datasets include somatic SNPs, INDELs, large structural variants, CNV, gene-, transcript-, exon- protein-, miRNA-expression, DNA methylation, phenotypes, and clinical data. Xena dynamically generates KM plots as ...Read More
UCSC Xena (http://xena.ucsc.edu) is a web-based, visual exploration tool for all modes of multi-omic data and associated annotations. Xena has several seminal cancer datasets pre-loaded and ready for visualization including TCGA, ICGC, GTEx and more. Our public datasets include somatic SNPs, INDELs, large structural variants, CNV, gene-, transcript-, exon- protein-, miRNA-expression, DNA methylation, phenotypes, and clinical data. Xena dynamically generates KM plots as well as visualizes data in a spreadsheet-like view, box plot, bar graph or scatter plot. Using our data hubs and browser, you can easily view both your annotations on top of our datasets, like TCGA, as well as your own genomics data. We fully support human cells, samples, cell lines, organoids, xenografts, etc, and also have basic support for mouse and other species. Session 1: 10 am - 12 pm
  • Data overview: TCGA, ICGC, GTEx and more
  • Navigating our visualizations
  • Running a KM analysis
12 - 1 pm LUNCH BREAK Session 2: 1 - 3 pm
  • In-depth coverage of Xena's features
  • Comprehensive filtering of samples
  • Viewing your own data
Register
Organizer
BTEP
When
Wed, Sep 27, 2017 - 10:00 am - 3:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
UCSC Xena (http://xena.ucsc.edu) is a web-based, visual exploration tool for all modes of multi-omic data and associated annotations. Xena has several seminal cancer datasets pre-loaded and ready for visualization including TCGA, ICGC, GTEx and more. Our public datasets include somatic SNPs, INDELs, large structural variants, CNV, gene-, transcript-, exon- protein-, miRNA-expression, DNA methylation, phenotypes, and clinical data. Xena dynamically generates KM plots as well as visualizes data in a spreadsheet-like view, box plot, bar graph or scatter plot. Using our data hubs and browser, you can easily view both your annotations on top of our datasets, like TCGA, as well as your own genomics data. We fully support human cells, samples, cell lines, organoids, xenografts, etc, and also have basic support for mouse and other species. Session 1: 10 am - 12 pm Data overview: TCGA, ICGC, GTEx and more Navigating our visualizations Running a KM analysis 12 - 1 pm LUNCH BREAK Session 2: 1 - 3 pm In-depth coverage of Xena's features Comprehensive filtering of samples Viewing your own data 2017-09-27 10:00:00 Bldg 10 FAES room 4 (B1C205) In-Person BTEP 0 UCSC Xena: A Tool to Interactively View Cancer Data
831
BTEP encourages researchers with high-throughput data from NGS or mutiple sources to attend this hands-on workshop on Qlucore Omics Explorer (QOE), which handles various types of data with the platform-independent data import Wizard module. Examples of data include Gene expression (array and NGS), proteomics, metabolomics, methylation data, flow cytometry, qPCR etc. For those who may be users of Qlucore, we are also giving an opportunity to set up a 1-on-1 consultation (see agenda below for more details) with the presenter to discuss your own data and/or other questions. With the new version that is going to be introduced at the workshop, Qlucore also includes a NGS module that has these additional features: Built-in variant caller Synchronized analysis of data in expression and genomic spaces User friendly project configuration of all included files (BAM, VCF, GTF, BED…) Integrated Genome browser with dynamic filtering of the content showed in the browser More information and materials pertinent to the workshop will be shared with those who register closer to the date of the workshop. WORKSHOP AGENDA for Wednesday, October 25, 2017 Morning Session 9:30 am– 12:30 pm: Presentation of Qlucore with Hands-on training This session will include introduction to and exercises on basic features and functionality in Qlucore Omics Explorer. It is intended for new users and does not require that you have previous experience with Qlucore Omics Explorer. After the training, you should be able to do the following using QOE: Import data and annotations Present data with different plot types (PCA, heatmap, bar, box...) Identify discriminating variables using basic statistical test (t- test, anova, regression analysis) Use visualization to enhance analysis and interpret results Analyze public data (GEO data sets) Explore large data sets - find structure, patterns and subclusters in data (using PCA, variance filtering, heatmaps,…) Export variable lists and images Afternoon Session, 1:00 - 4:00 pm Four 30-min 1-on-1 sessions; if interested, please indicate inside Comments Box during Registration Participants who sign up for these 30-min sessions are expected to bring their own data and/or questions If slots are available on the day of the workshop, walk-ins will be allowed on a first-come, first-serve basis. Available slots: 1-1:30 pm 1:45-2:15 pm 2:30-3:15 pm 3:30-4:00 pm NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. 2017-10-25 09:30:00 Bldg 10 FAES room 4 (B1C205) In-Person BTEP 0 Navigating Qlucore Omics Explorer
833
Description
MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. In this workshop, the attendees will be analyzing data about NOTCH1 signaling in chronic lymphocytic leukemia: Read More
MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. In this workshop, the attendees will be analyzing data about NOTCH1 signaling in chronic lymphocytic leukemia: http://www.pnas.org/content/114/14/E2911.abstract (PNAS publication). You’ll explore pathways enriched by ICN1-HA up-regulated genes from RNA-seq data, and bound to NOTCH1 in ChIP-seq experiments. The morning session will cover different basic modules available in MetaCore to extract meaningful information. The afternoon session will focus on advanced modules, and also introduce Key Pathway Advisor (KPA), which is a recent web application developed for easy biological pathway analysis of OMICs data. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. AGENDA 9:30am – 12:30pm
  • MetaCore Basics Training
    • MetaCore Overview
    • EZ searching for interactions and pathways involved with NOTCH1
    • Uploading data into MetaCore.
    • Pathway analysis of ICN1 RNA-seq data bound to NOTCH1 in ChIP-seq experiments.
    • Compare pathways enriched by leading edge genes from peripheral blood of chronic lymphocytic leukemia patients.
1:30pm – 4:00pm
  • MetaCore Advanced Training
    • Use overconnectivity analysis to associate transcription factors regulating the ICN1 RNA-seq dataset.
    • Find other publicly available GEO microarray datasets to find similar signatures in other diseases.
    • Identifying causal networks and synergistic pathways from ICN1 RNA-seq data bound to NOTCH1 in ChIP-seq experiments using Key Pathway Advisor.
Register
Organizer
BTEP
When
Mon, Nov 13, 2017 - 9:30 am - 4:00 pm
Where
NIH Bldg 10 FAES Room 4 (B1C205)
MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem. In this workshop, the attendees will be analyzing data about NOTCH1 signaling in chronic lymphocytic leukemia: http://www.pnas.org/content/114/14/E2911.abstract (PNAS publication). You’ll explore pathways enriched by ICN1-HA up-regulated genes from RNA-seq data, and bound to NOTCH1 in ChIP-seq experiments. The morning session will cover different basic modules available in MetaCore to extract meaningful information. The afternoon session will focus on advanced modules, and also introduce Key Pathway Advisor (KPA), which is a recent web application developed for easy biological pathway analysis of OMICs data. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. AGENDA 9:30am – 12:30pm MetaCore Basics Training MetaCore Overview EZ searching for interactions and pathways involved with NOTCH1 Uploading data into MetaCore. Pathway analysis of ICN1 RNA-seq data bound to NOTCH1 in ChIP-seq experiments. Compare pathways enriched by leading edge genes from peripheral blood of chronic lymphocytic leukemia patients. 1:30pm – 4:00pm MetaCore Advanced Training Use overconnectivity analysis to associate transcription factors regulating the ICN1 RNA-seq dataset. Find other publicly available GEO microarray datasets to find similar signatures in other diseases. Identifying causal networks and synergistic pathways from ICN1 RNA-seq data bound to NOTCH1 in ChIP-seq experiments using Key Pathway Advisor. 2017-11-13 09:30:00 NIH Bldg 10 FAES Room 4 (B1C205) In-Person BTEP 0 Pathway Analysis with MetaCore
832
Description
The CCR Office of Science and Technology Resources (OSTR), along with the Bioinformatics Training and Education Program (BTEP), are pleased to organize this event for the CCR scientific community. The primary goal of single cell sequencing is to investigate individual cells using optimized next generation sequencing (NGS) technologies, with the goal of resolving cellular differences to better understand the function of an individual cell in the context of its microenvironment. The event will provide an ...Read More
The CCR Office of Science and Technology Resources (OSTR), along with the Bioinformatics Training and Education Program (BTEP), are pleased to organize this event for the CCR scientific community. The primary goal of single cell sequencing is to investigate individual cells using optimized next generation sequencing (NGS) technologies, with the goal of resolving cellular differences to better understand the function of an individual cell in the context of its microenvironment. The event will provide an overview of this emerging field, and speakers will talk about technology platforms, the best practices for sample preparation, and bioinformatics approaches for analysis of sc-RNAseq data. Date:            Tuesday, November 14, 2017 Time:           Session I B37, Rm 4041/4107 (9:30-11:00 am)

Session II B37, Rm 6041/6107 (2:00-5:00pm)

Scroll further down for WebEx information AGENDA Session I (B37, Rm 4041/4107) 9:30 am – 11:00 am        Introduction to Single Cell Genomics

                                     Speaker: Michael Kelly, Ph.D., NIDCD

  • Key Concepts in Single Cell Genomics
  • Current Challenges & Limitations
  • Experimental Design Considerations
  • Platform Selection Considerations
  • “Established” and Emerging Applications
  Session II (B37, Rm 6041/6107) 2:15 pm – 2:45 pm         Overview of Single Cell Technologies at CCR Cores

     Speaker: Xaolin Wu, Ph.D., CCR Genomics Technology Lab

  • BD Rhapsody Single Cell Analysis for RNA and Proteins
  • Chromium Single Cell 3' Solutiom=n
  • DEP Array Technology Rare Cell Isolation
  • Fluidigm C1 for Single Cell Genomics
  2:45 pm – 3:45 pm       Single Cell RNA-Seq at CCR-SF: Best practices using 10X Genomics

   Speaker: Monika Mehta, Ph.D., CCR-SF (ATRF)

  • Current production systems
  • New methods being tested
  • Optimal experimental design
  • Sample requirements, prep and QC
  • Highlights from range of projects at SF
  3:45 pm – 4:45 pm          Overview of single cell RNA-Seq analysis

      Speaker: Vishal Koparde, Ph.D., CCBR

  • How is single cell data different from bulk RNASeq?
  • Options for filtering, normalization and visualization
  • Current popular analysis tools
In-person attendance is encouraged. For convenience, the talks will be on WebEx (link below): https://cbiit.webex.com/cbiit/j.php?MTID=m5a62ff8ecc39e2226aadf027a32d4938 Meeting number/Access Code: 858 626 079 Audio connection: 1-650-479-3207 Call-in toll number (US/Canada)  
Register
Organizer
BTEP
When
Tue, Nov 14, 2017 - 9:30 am - 5:00 pm
Where
NIH B37, Rm 6041 and 4041
The CCR Office of Science and Technology Resources (OSTR), along with the Bioinformatics Training and Education Program (BTEP), are pleased to organize this event for the CCR scientific community. The primary goal of single cell sequencing is to investigate individual cells using optimized next generation sequencing (NGS) technologies, with the goal of resolving cellular differences to better understand the function of an individual cell in the context of its microenvironment. The event will provide an overview of this emerging field, and speakers will talk about technology platforms, the best practices for sample preparation, and bioinformatics approaches for analysis of sc-RNAseq data. Date:            Tuesday, November 14, 2017 Time:           Session I B37, Rm 4041/4107 (9:30-11:00 am) Session II B37, Rm 6041/6107 (2:00-5:00pm) Scroll further down for WebEx information AGENDA Session I (B37, Rm 4041/4107) 9:30 am – 11:00 am        Introduction to Single Cell Genomics                                      Speaker: Michael Kelly, Ph.D., NIDCD Key Concepts in Single Cell Genomics Current Challenges & Limitations Experimental Design Considerations Platform Selection Considerations “Established” and Emerging Applications   Session II (B37, Rm 6041/6107) 2:15 pm – 2:45 pm         Overview of Single Cell Technologies at CCR Cores      Speaker: Xaolin Wu, Ph.D., CCR Genomics Technology Lab BD Rhapsody Single Cell Analysis for RNA and Proteins Chromium Single Cell 3' Solutiom=n DEP Array Technology Rare Cell Isolation Fluidigm C1 for Single Cell Genomics   2:45 pm – 3:45 pm       Single Cell RNA-Seq at CCR-SF: Best practices using 10X Genomics    Speaker: Monika Mehta, Ph.D., CCR-SF (ATRF) Current production systems New methods being tested Optimal experimental design Sample requirements, prep and QC Highlights from range of projects at SF   3:45 pm – 4:45 pm          Overview of single cell RNA-Seq analysis       Speaker: Vishal Koparde, Ph.D., CCBR How is single cell data different from bulk RNASeq? Options for filtering, normalization and visualization Current popular analysis tools In-person attendance is encouraged. For convenience, the talks will be on WebEx (link below): https://cbiit.webex.com/cbiit/j.php?MTID=m5a62ff8ecc39e2226aadf027a32d4938 Meeting number/Access Code: 858 626 079 Audio connection: 1-650-479-3207 Call-in toll number (US/Canada)   2017-11-14 09:30:00 NIH B37, Rm 6041 and 4041 In-Person Vishal Koparde (CCBR) BTEP 0 A Primer on Single Cell Genomics at CCR
834
Description
PLEASE NOTE: This workshop is a BYOC (Bring Your Own LapTop Computer) class, and requires installation of Partek Genomics Suite on your laptop ahead of the workshop. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer, please indicate such in the Comment section ...Read More
PLEASE NOTE: This workshop is a BYOC (Bring Your Own LapTop Computer) class, and requires installation of Partek Genomics Suite on your laptop ahead of the workshop. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer, please indicate such in the Comment section on the registration form. WORKSHOP AGENDA 9:30 am – 12:30 pm         Gene Expression Analysis with Partek Genomics Suite This training session will include a guided analysis of an Affymetrix gene expression data set to showcase and familiarize users with the Gene Expression analysis workflow covering the topics listed below.
  • Importing data – Affymetrix CEL files
  • Exploratory data analysis – Principal Component Analysis (PCA)
  • Detecting differential expression (ANOVA) – two factor analysis
  • Gene list creation (Venn diagram creation and list overlap)
  • Visualization (PCA, histogram, box plot, dot plot, volcano plot, heatmap etc.)
  • Biological interpretation – through use of Gene Ontology and KEGG
  Additional advanced topics will include:
  • Integration with other data – combining gene and miRNA expression data
  • Batch effect removal
  • Survival analysis
  12:30 – 1:30 pm LUNCH BREAK   1:30 – 3:00 pm           Advanced Modules This session will provide a hands on training for analyzing Illumina methylation microarrays, familiarizing users to the topics listed below.
  • Importing data – Illumina .idat files
  • Data normalization – converting data to M-values
  • Exploratory data analysis – Principal Component Analysis (PCA)
  • Detecting differential methylation (ANOVA)
  • Annotating markers by gene section
  • Visualization (PCA, histogram, box plot, dot plot, volcano plot, heatmap etc.)
This session will also provide the opportunity to learn how to import and process data matrices in text file format (from RNA-seq or protein data, as an example) for the purpose of generating visualizations.   3:00 – 4:00 pm         Bring Your Own Data/Independent Analysis of GEO Data
  •   Bring your own data for analysis help.
-OR-
  •  Attendees will be presented with the task of obtaining a data set from the NCBI Gene Expression Omnibus (GEO) and running an independent analysis of the data to attempt to replicate the findings of the publication.  They will be given a list of analysis goals and will have the opportunity to ask for help from the instructor as they work through this analysis.
The two PDF files below (Course Material 1 and 2) document the workflows that will be followed, and also contains links to the example datasets that attendees will use during the workshop. Kindly download the data if you plan to attend.  
Register
Organizer
BTEP
When
Wed, Dec 13, 2017 - 9:30 am - 4:00 pm
Where
NIH Bldg 10 FAES Room 4 (B1C205)
PLEASE NOTE: This workshop is a BYOC (Bring Your Own LapTop Computer) class, and requires installation of Partek Genomics Suite on your laptop ahead of the workshop. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer, please indicate such in the Comment section on the registration form. WORKSHOP AGENDA 9:30 am – 12:30 pm         Gene Expression Analysis with Partek Genomics Suite This training session will include a guided analysis of an Affymetrix gene expression data set to showcase and familiarize users with the Gene Expression analysis workflow covering the topics listed below. Importing data – Affymetrix CEL files Exploratory data analysis – Principal Component Analysis (PCA) Detecting differential expression (ANOVA) – two factor analysis Gene list creation (Venn diagram creation and list overlap) Visualization (PCA, histogram, box plot, dot plot, volcano plot, heatmap etc.) Biological interpretation – through use of Gene Ontology and KEGG   Additional advanced topics will include: Integration with other data – combining gene and miRNA expression data Batch effect removal Survival analysis   12:30 – 1:30 pm LUNCH BREAK   1:30 – 3:00 pm           Advanced Modules This session will provide a hands on training for analyzing Illumina methylation microarrays, familiarizing users to the topics listed below. Importing data – Illumina .idat files Data normalization – converting data to M-values Exploratory data analysis – Principal Component Analysis (PCA) Detecting differential methylation (ANOVA) Annotating markers by gene section Visualization (PCA, histogram, box plot, dot plot, volcano plot, heatmap etc.) This session will also provide the opportunity to learn how to import and process data matrices in text file format (from RNA-seq or protein data, as an example) for the purpose of generating visualizations.   3:00 – 4:00 pm         Bring Your Own Data/Independent Analysis of GEO Data   Bring your own data for analysis help. -OR-  Attendees will be presented with the task of obtaining a data set from the NCBI Gene Expression Omnibus (GEO) and running an independent analysis of the data to attempt to replicate the findings of the publication.  They will be given a list of analysis goals and will have the opportunity to ask for help from the instructor as they work through this analysis. The two PDF files below (Course Material 1 and 2) document the workflows that will be followed, and also contains links to the example datasets that attendees will use during the workshop. Kindly download the data if you plan to attend.   2017-12-13 09:30:00 NIH Bldg 10 FAES Room 4 (B1C205) In-Person BTEP 0 Analysis with Partek Genomics Suite: mRNA, miRNA, Methylation and More
835
Description
Join us for a seminar/webinar session where Partek Specialist will show you how to analyze your Single Cell RNA-Seq data in an intuitive, interactive and powerful way using Partek Flow. The solution simplifies Single Cell RNA-Seq analysis, even for novices! During this session, the Partek scientist will demonstrate how to analyze a Single Cell RNA-Seq data set with multiple biological replicates and detect genes that are differentially expressed between cell populations across sample groups. <...Read More
Join us for a seminar/webinar session where Partek Specialist will show you how to analyze your Single Cell RNA-Seq data in an intuitive, interactive and powerful way using Partek Flow. The solution simplifies Single Cell RNA-Seq analysis, even for novices! During this session, the Partek scientist will demonstrate how to analyze a Single Cell RNA-Seq data set with multiple biological replicates and detect genes that are differentially expressed between cell populations across sample groups. Date: April 25, 1:00–2:30 p.m. Location: Frederick ATRF Building, Conference Room D3001 & WEBEX (see below) Agenda:
  • Partek Single Cell RNA-Seq Data Analysis Solution Overview
  • Live Demo
  • Cell filtering using interactive QC charts
  • Identifying cell populations by t-SNE plot
  • Pooling cells of the same type across multiple samples
  • Detecting differential expressed genes
  • Visualizing transcription profiles by heat map
  • Biological interpretation
  • Q&A
This Seminar will be video cast via WebEx with the following details: Meeting number (access code): 730 734 594 Meeting password: aPiqcM@4 When it's time, join the meeting Join by phone 1-650-479-3207 Call-in toll number (US/Canada)
Details
Organizer
BTEP
When
Wed, Apr 25, 2018 - 1:00 pm - 2:30 pm
Where
Conference Room D3001
Join us for a seminar/webinar session where Partek Specialist will show you how to analyze your Single Cell RNA-Seq data in an intuitive, interactive and powerful way using Partek Flow. The solution simplifies Single Cell RNA-Seq analysis, even for novices! During this session, the Partek scientist will demonstrate how to analyze a Single Cell RNA-Seq data set with multiple biological replicates and detect genes that are differentially expressed between cell populations across sample groups. Date: April 25, 1:00–2:30 p.m. Location: Frederick ATRF Building, Conference Room D3001 & WEBEX (see below) Agenda: Partek Single Cell RNA-Seq Data Analysis Solution Overview Live Demo Cell filtering using interactive QC charts Identifying cell populations by t-SNE plot Pooling cells of the same type across multiple samples Detecting differential expressed genes Visualizing transcription profiles by heat map Biological interpretation Q&A This Seminar will be video cast via WebEx with the following details: Meeting number (access code): 730 734 594 Meeting password: aPiqcM@4 When it's time, join the meeting Join by phone 1-650-479-3207 Call-in toll number (US/Canada) 2018-04-25 13:00:00 Conference Room D3001 In-Person BTEP 0 Single Cell RNA-Seq (WebEx Seminar)
836
Description
Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of publicly available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding of ...Read More
Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of publicly available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding of the interplay between spatially related biological elements. These visualization tools are relevant whether one is interested in expression analysis (RNA-Seq), variant detection (Exome-Seq), protein binding (ChIP-Seq), or any other genome scale data. This seminar will provide an overview of the available tools, and highlight useful features of some of the more popular genome browsers. Highlighted topics will include:
  • An overview of the tools available - their strength and weaknesses and where to find them
  • An overview of the different classes of browsers
  • An discussion of the relevant file types accepted by most browsers
  • Details on how to navigate the UCSC Genome Browser
  • How to integrate your own or publically available data into browsers
  • How to capture and share specific views of data
  • How to get more detailed views of your data with tools like IGB and IGV and how to integrate them into other tools
  • and more....
Register
Organizer
BTEP
When
Wed, May 09, 2018 - 10:00 am - 12:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
Driven by the decreasing costs and increasing performance of next-generation sequencing (NGS) technologies, the amount of publicly available genomic data has grown exponentially in recent years.  While most of the analyses of these data is done computationally, there are many instances where visual inspection of the original and/or processed data is invaluable. Such direct visualization can provide: “sanity checks” on computational results; novel biological insights; aid in communicating results; and a better understanding of the interplay between spatially related biological elements. These visualization tools are relevant whether one is interested in expression analysis (RNA-Seq), variant detection (Exome-Seq), protein binding (ChIP-Seq), or any other genome scale data. This seminar will provide an overview of the available tools, and highlight useful features of some of the more popular genome browsers. Highlighted topics will include: An overview of the tools available - their strength and weaknesses and where to find them An overview of the different classes of browsers An discussion of the relevant file types accepted by most browsers Details on how to navigate the UCSC Genome Browser How to integrate your own or publically available data into browsers How to capture and share specific views of data How to get more detailed views of your data with tools like IGB and IGV and how to integrate them into other tools and more.... 2018-05-09 10:00:00 Bldg 10 FAES room 4 (B1C205) In-Person Peter FitzGerald (GAU) BTEP 0 Genome Browsers - Tools for Visualizing Genomic Scale Data
837
Description
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticians can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated ...Read More
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticians can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated tools and pipelines, the DNAnexus platform also allows the quick and efficient development of new analysis tools and the  porting of existing pipelines. DNAnexus is built upon the foundational security and features of AWS and AZURE. Key Features of DNAnexus:
  • Centralized online environment for genomic data analysis, storage  and collaboration
  • Web-based interface for ease of use
  • Command line interface for power-users
  • Large library of standard bioinformatic tools and pipelines
  • Access to proprietary hardware (DRAGEN, Sentieon)
  • Scalable infrastructure that can instantly meet any computational and data storage demand
  • Data compliance in accordance with: HIPAA, CLIA, GCP, 21 CFR parts 11, 58, and 493, European Data Privacy laws and regulations
Workshop Agenda:
  • DNAnexus - Company Overview
  • Platform demo (non-interactive)
  • Varient Calling (interactive)
  • RNA-Seq Analysis  (interactive)
  • NCI experiences developing/porting an analytical pipeline to the DNAnexus platform
Workshop will be conducted live on the DNAnexus Platform: https://platform.dnanexus.com Registered users will be provided a temporary account for the interactive parts of the presentation. While attending in person is highly recommended this Workshop will be available via Webex for those not on the NIH campus.. DNAnexus Meeting number (access code): 730 139 731 Meeting password: cD9dm4d@ Join the meeting Join by phone 1-650-479-3207 Call-in toll number (US/Canada)
Register
Organizer
BTEP
When
Tue, Jun 26, 2018 - 2:30 pm - 5:00 pm
Where
NIH Bethesda B37 Rm 4041/4107
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticians can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated tools and pipelines, the DNAnexus platform also allows the quick and efficient development of new analysis tools and the  porting of existing pipelines. DNAnexus is built upon the foundational security and features of AWS and AZURE. Key Features of DNAnexus: Centralized online environment for genomic data analysis, storage  and collaboration Web-based interface for ease of use Command line interface for power-users Large library of standard bioinformatic tools and pipelines Access to proprietary hardware (DRAGEN, Sentieon) Scalable infrastructure that can instantly meet any computational and data storage demand Data compliance in accordance with: HIPAA, CLIA, GCP, 21 CFR parts 11, 58, and 493, European Data Privacy laws and regulations Workshop Agenda: DNAnexus - Company Overview Platform demo (non-interactive) Varient Calling (interactive) RNA-Seq Analysis  (interactive) NCI experiences developing/porting an analytical pipeline to the DNAnexus platform Workshop will be conducted live on the DNAnexus Platform: https://platform.dnanexus.com Registered users will be provided a temporary account for the interactive parts of the presentation. While attending in person is highly recommended this Workshop will be available via Webex for those not on the NIH campus.. DNAnexus Meeting number (access code): 730 139 731 Meeting password: cD9dm4d@ Join the meeting Join by phone 1-650-479-3207 Call-in toll number (US/Canada) 2018-06-26 14:30:00 NIH Bethesda B37 Rm 4041/4107 In-Person Peter FitzGerald (GAU),Darren Ames (DNAnexus),John Didion (DNA nexus) BTEP 0 Analysis of Next-Generation Sequencing Data using the DNAnexus Cloud Platform
839
Description
TOPIC: Single Cell RNA-Seq Data Analysis in Partek Flow Partek (partek.com) Flow software provides a point-and-click interface for analysis of next -gen sequencing data. Users can customize analysis pathways for sequence alignment, differential expression, QA/QC, variant calling and annotation, clustering, peak calling, statistical analysis and quantification. These pathways can be re-used and shared, resulting  in publication-ready data visualizations. During this session, attendees will learn how to identify cell populations and detect ...Read More
TOPIC: Single Cell RNA-Seq Data Analysis in Partek Flow Partek (partek.com) Flow software provides a point-and-click interface for analysis of next -gen sequencing data. Users can customize analysis pathways for sequence alignment, differential expression, QA/QC, variant calling and annotation, clustering, peak calling, statistical analysis and quantification. These pathways can be re-used and shared, resulting  in publication-ready data visualizations. During this session, attendees will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow. Attendees will work with a set of test data for this tutorial. Please bring a laptop running Google chrome. - Import count matrix text file - Filter cells using interactive QA/QC charts - Filter low expressed genes - Normalize raw count - Visualize cell populations using the interactive 3D t-SNE plot - Overlay gene expression and pathway signatures on the 3D t-SNE plot - Select and classify cells on the 3D t-SNE plot - Detect differentially expressed genes between sub populations - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualize cell-level results using heat maps, volcano plots, and violin plots - Demonstrate how to import fastq files and upstream analysis pipeline on 10X prep kit To attend this meeting via Webex, please click here: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=e8fc591c272dbb8074dc7e3f24b1f861f
Register
Organizer
BTEP
When
Wed, Sep 19, 2018 - 8:45 am - 11:00 am
Where
Building 37 Room 4041/4107, NIH
TOPIC: Single Cell RNA-Seq Data Analysis in Partek Flow Partek (partek.com) Flow software provides a point-and-click interface for analysis of next -gen sequencing data. Users can customize analysis pathways for sequence alignment, differential expression, QA/QC, variant calling and annotation, clustering, peak calling, statistical analysis and quantification. These pathways can be re-used and shared, resulting  in publication-ready data visualizations. During this session, attendees will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow. Attendees will work with a set of test data for this tutorial. Please bring a laptop running Google chrome. - Import count matrix text file - Filter cells using interactive QA/QC charts - Filter low expressed genes - Normalize raw count - Visualize cell populations using the interactive 3D t-SNE plot - Overlay gene expression and pathway signatures on the 3D t-SNE plot - Select and classify cells on the 3D t-SNE plot - Detect differentially expressed genes between sub populations - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualize cell-level results using heat maps, volcano plots, and violin plots - Demonstrate how to import fastq files and upstream analysis pipeline on 10X prep kit To attend this meeting via Webex, please click here: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=e8fc591c272dbb8074dc7e3f24b1f861f 2018-09-19 08:45:00 Building 37 Room 4041/4107,NIH In-Person Xiaowen Wang (Partek) BTEP 0 BTEP: Single Cell RNA-Seq Analysis with Partek Flow
840
Description
Participants should bring a laptop running Google Chrome. Additionally, each participant should have an account on the Partek server, and have uploaded their data onto the Partek server prior to the start of this class. For Partek server accounts, please contact Jenny Zhang (jzhang@partek.com) For assistance moving your data onto the Partek server, contact Partek IT or (BTEP) at ncibtep@nih.gov Be sure to upload the count ...Read More
Participants should bring a laptop running Google Chrome. Additionally, each participant should have an account on the Partek server, and have uploaded their data onto the Partek server prior to the start of this class. For Partek server accounts, please contact Jenny Zhang (jzhang@partek.com) For assistance moving your data onto the Partek server, contact Partek IT or (BTEP) at ncibtep@nih.gov Be sure to upload the count matrix file in .txt format If the data was generated from 10X Genomics, please upload the filtered .h5 format files
Register
Organizer
BTEP
When
Wed, Sep 19, 2018 - 1:30 pm - 5:00 pm
Where
NIH, Bldg 37, Rm 6041
Participants should bring a laptop running Google Chrome. Additionally, each participant should have an account on the Partek server, and have uploaded their data onto the Partek server prior to the start of this class. For Partek server accounts, please contact Jenny Zhang (jzhang@partek.com) For assistance moving your data onto the Partek server, contact Partek IT or (BTEP) at ncibtep@nih.gov Be sure to upload the count matrix file in .txt format If the data was generated from 10X Genomics, please upload the filtered .h5 format files 2018-09-19 13:30:00 NIH,Bldg 37,Rm 6041 In-Person Xiaowen Wang (Partek),Amy Stonelake (BTEP) BTEP 0 BTEP: Work with your own data - Single Cell RNA-Seq Analysis with Partek Flow
838
Description
Sept. 27, 2018: BTEP: Practical Bioinformatics Skills at the Command Line Please bring your own laptop to the class or indicate in the comments if you need to borrow one of our loaner laptops. Confused about the command line? Working with data on your laptop that needs to be on the NIH Biowulf cluster for further analysis? Want to transform your BCL files into FASTQ, or turn your BLAST results into an Excel ...Read More
Sept. 27, 2018: BTEP: Practical Bioinformatics Skills at the Command Line Please bring your own laptop to the class or indicate in the comments if you need to borrow one of our loaner laptops. Confused about the command line? Working with data on your laptop that needs to be on the NIH Biowulf cluster for further analysis? Want to transform your BCL files into FASTQ, or turn your BLAST results into an Excel Spreadsheet? Help is available. 1. This will be a hands-on workshop with step-by-step instructions provided. 2. Learn basic unix commands for creating files, moving around the directory tree, running programs on the NIH Biowulf cluster. 3. Use tools like Globus Connect, scp (WinSCP) to move files from your laptop to Biowulf and back. 4. Understand more about various file formats, how they are related, and learn how to transform from one file type to another. 5. Read from and write to files on Biowulf. 6. Compare the web version and command-line versions of NCBI BLAST tools.
Register
Organizer
BTEP
When
Thu, Sep 27, 2018 - 10:00 am - 4:00 pm
Where
NIH Bldg 10 FAES Room 4 (B1C205)
Sept. 27, 2018: BTEP: Practical Bioinformatics Skills at the Command Line Please bring your own laptop to the class or indicate in the comments if you need to borrow one of our loaner laptops. Confused about the command line? Working with data on your laptop that needs to be on the NIH Biowulf cluster for further analysis? Want to transform your BCL files into FASTQ, or turn your BLAST results into an Excel Spreadsheet? Help is available. 1. This will be a hands-on workshop with step-by-step instructions provided. 2. Learn basic unix commands for creating files, moving around the directory tree, running programs on the NIH Biowulf cluster. 3. Use tools like Globus Connect, scp (WinSCP) to move files from your laptop to Biowulf and back. 4. Understand more about various file formats, how they are related, and learn how to transform from one file type to another. 5. Read from and write to files on Biowulf. 6. Compare the web version and command-line versions of NCBI BLAST tools. 2018-09-27 10:00:00 NIH Bldg 10 FAES Room 4 (B1C205) In-Person Peter FitzGerald (GAU),Amy Stonelake (BTEP) BTEP 0 BTEP: Practical Bioinformatics Skills at the Command Line
841
Description
This event will be held in the Scientific Library, Bldg. 549, Ft. Detrick, Frederick. Please bring your own laptop. There will also be a limited number of classroom computers (PC) available.  The Bioinformatics Training and Education Program (BTEP) is excited to present a workshop entitled,“Practical Bioinformatics Skills at the Command Line”.  Confused about the command line? Working with data on your laptop that needs to be on the NIH ...Read More
This event will be held in the Scientific Library, Bldg. 549, Ft. Detrick, Frederick. Please bring your own laptop. There will also be a limited number of classroom computers (PC) available.  The Bioinformatics Training and Education Program (BTEP) is excited to present a workshop entitled,“Practical Bioinformatics Skills at the Command Line”.  Confused about the command line? Working with data on your laptop that needs to be on the NIH Biowulf cluster for further analysis? Want to transform your BCL files into FASTQ, or turn your BLAST results into an Excel Spreadsheet? Help is available.
  1. This will be a hands-on workshop with step-by-step instructions provided. 2. Learn basic unix commands for creating files, moving around the directory tree, running programs on the NIH Biowulf cluster. 3. Use tools like Globus Connect, scp (WinSCP) to move files from your laptop to Biowulf and back. 4. Understand more about various file formats, how they are related, and learn how to transform from one file type to another. 5. Read from and write to files on Biowulf. 6. Compare the web version and command-line versions of NCBI BLAST tools.
Register
Organizer
BTEP
When
Fri, Sep 28, 2018 - 10:00 am - 4:00 pm
Where
NCI-F Bldg549 Scientific Library Training Room
This event will be held in the Scientific Library, Bldg. 549, Ft. Detrick, Frederick. Please bring your own laptop. There will also be a limited number of classroom computers (PC) available.  The Bioinformatics Training and Education Program (BTEP) is excited to present a workshop entitled,“Practical Bioinformatics Skills at the Command Line”.  Confused about the command line? Working with data on your laptop that needs to be on the NIH Biowulf cluster for further analysis? Want to transform your BCL files into FASTQ, or turn your BLAST results into an Excel Spreadsheet? Help is available. This will be a hands-on workshop with step-by-step instructions provided. 2. Learn basic unix commands for creating files, moving around the directory tree, running programs on the NIH Biowulf cluster. 3. Use tools like Globus Connect, scp (WinSCP) to move files from your laptop to Biowulf and back. 4. Understand more about various file formats, how they are related, and learn how to transform from one file type to another. 5. Read from and write to files on Biowulf. 6. Compare the web version and command-line versions of NCBI BLAST tools. 2018-09-28 10:00:00 NCI-F Bldg549 Scientific Library Training Room In-Person Peter FitzGerald (GAU),Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Practical Bioinformatics Skills at the Command Line
842
Description
  1. Experimental Design Considerations in Variant Analysis
    1. Germline vs Somatic
    2. WGS vs WES
    3. Sample sizes and statistical power
  2. WGS/WES Pipelines at NIH
    1. Pipeline performance
    2. Using Pipeliner for internal and external data
  3. Variant QC, Annotation and Downstream Analysis
    1. <...Read More
  1. Experimental Design Considerations in Variant Analysis
    1. Germline vs Somatic
    2. WGS vs WES
    3. Sample sizes and statistical power
  2. WGS/WES Pipelines at NIH
    1. Pipeline performance
    2. Using Pipeliner for internal and external data
  3. Variant QC, Annotation and Downstream Analysis
    1. Variant QC and data correction
    2. Variant annotation and analysis tools
  4. Structural variation and multi-omic integration
Presenter: Justin Lack, Ph.D, Bioinformatics Manager/ Lead, NIAID Collaborative Bioinformatics Core (NCBR)
Register
Organizer
BTEP
When
Fri, Oct 12, 2018 - 9:00 am - 11:00 am
Where
Bldg 37, Rm 2041/2107
Experimental Design Considerations in Variant Analysis Germline vs Somatic WGS vs WES Sample sizes and statistical power WGS/WES Pipelines at NIH Pipeline performance Using Pipeliner for internal and external data Variant QC, Annotation and Downstream Analysis Variant QC and data correction Variant annotation and analysis tools Structural variation and multi-omic integration Presenter: Justin Lack, Ph.D, Bioinformatics Manager/ Lead, NIAID Collaborative Bioinformatics Core (NCBR) 2018-10-12 09:00:00 Bldg 37,Rm 2041/2107 In-Person BTEP 0 BTEP: Variant Analysis in WGS and WES Introduction
843
Description
QIAGEN’s Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based on published biological evidence and your knowledge of disease biology. IVA allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This seminar will focus on: -Uploading Data -Running Analyses -Utilizing Variant ...Read More
QIAGEN’s Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based on published biological evidence and your knowledge of disease biology. IVA allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This seminar will focus on: -Uploading Data -Running Analyses -Utilizing Variant Filtering -Exploring Results and Exporting Data
Register
Organizer
BTEP
When
Fri, Oct 12, 2018 - 1:00 pm - 3:00 pm
Where
Building 37 Room 4041/4107, Bldg 37
QIAGEN’s Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based on published biological evidence and your knowledge of disease biology. IVA allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This seminar will focus on: -Uploading Data -Running Analyses -Utilizing Variant Filtering -Exploring Results and Exporting Data 2018-10-12 13:00:00 Building 37 Room 4041/4107,Bldg 37 In-Person BTEP 0 BTEP: Variant Analysis in WGS and WES with Qiagen IVA (Ingenuity Variant Analysis)
845
Description
10x Genomics: Whole Exome and Whole Genome Analysis
  • How 10x Genomics linked reads works
  • SNVs, copy number variations, structural variants and phasing of the variants from linked read data
  • Walk through of 10x Genomics WGS results
  • Integration of linked reads data with other platforms/technologies
All Structural Variants:
  • CCR Sequencing Facility analysis pipelines of structural variants from Illumina, linked reads, long reads and ...Read More
10x Genomics: Whole Exome and Whole Genome Analysis
  • How 10x Genomics linked reads works
  • SNVs, copy number variations, structural variants and phasing of the variants from linked read data
  • Walk through of 10x Genomics WGS results
  • Integration of linked reads data with other platforms/technologies
All Structural Variants:
  • CCR Sequencing Facility analysis pipelines of structural variants from Illumina, linked reads, long reads and optical mapping
  • Walk through of SV/CNV pipelines, how they work and results from the SV pipelines
Register
Organizer
BTEP
When
Fri, Oct 26, 2018 - 9:00 am - 11:00 am
Where
Rm 6041, NIH Bldg 37, Rm 6107
10x Genomics: Whole Exome and Whole Genome Analysis How 10x Genomics linked reads works SNVs, copy number variations, structural variants and phasing of the variants from linked read data Walk through of 10x Genomics WGS results Integration of linked reads data with other platforms/technologies All Structural Variants: CCR Sequencing Facility analysis pipelines of structural variants from Illumina, linked reads, long reads and optical mapping Walk through of SV/CNV pipelines, how they work and results from the SV pipelines 2018-10-26 09:00:00 Rm 6041,NIH Bldg 37,Rm 6107 In-Person BTEP 0 BTEP: Variant Analysis: SNVs, CNVs and Structural Variants in WGS and WES
844
Description
Variant Analysis using Genomatix GeneGrid ** please bring laptop with Flash installed for the hands-on portion of this demo Susan M. Dombrowski, PhD and Peter Grant, Genomatix, Inc. Genomatix GeneGrid is a variant annotation and analysis tool that integrates both public and proprietary data sources for examining the biological effects of single nucleotide polymorphisms (SNPs) and/or insertions-deletions (indels) in heritable or non-heritable (epigenetic) human disease. Using GeneGrid, one can quickly perform trio analysis, ...Read More
Variant Analysis using Genomatix GeneGrid ** please bring laptop with Flash installed for the hands-on portion of this demo Susan M. Dombrowski, PhD and Peter Grant, Genomatix, Inc. Genomatix GeneGrid is a variant annotation and analysis tool that integrates both public and proprietary data sources for examining the biological effects of single nucleotide polymorphisms (SNPs) and/or insertions-deletions (indels) in heritable or non-heritable (epigenetic) human disease. Using GeneGrid, one can quickly perform trio analysis, case-control studies, identify somatic SNPs and visualize and dynamically interact with these results in the context of other integrated Genomatix data content including: genomic annotation, biological pathways, and the supporting biomedical literature. GeneGrid also includes the ability to generate variant report summaries and provides link-outs to genetic testing providers. For an overview of the GeneGrid technology we invite you to visit: http://www.genomatix.de/solutions/genegrid.html The training course will consist of a GeneGrid overview lecture and instructor-led demonstration of how to import VCF and BAM files into the GeneGrid platform, followed by a hands-on training demonstrating the use of GeneGrid for variant annotation and analysis. At the end of the course, students will have learned how to: *import VCF and BAM files into the GeneGrid platform; *view VCF sample statistics and the associated metadata; *run a sample comparison; *annotate and filter variants; *view annotated variant data in the Genomatix Genome Browser; *use the results management features; *apply the mastery of GeneGrid to their own data
Register
Organizer
BTEP
When
Fri, Oct 26, 2018 - 1:00 pm - 4:00 pm
Where
Bldg 37, Room 6041
Variant Analysis using Genomatix GeneGrid ** please bring laptop with Flash installed for the hands-on portion of this demo Susan M. Dombrowski, PhD and Peter Grant, Genomatix, Inc. Genomatix GeneGrid is a variant annotation and analysis tool that integrates both public and proprietary data sources for examining the biological effects of single nucleotide polymorphisms (SNPs) and/or insertions-deletions (indels) in heritable or non-heritable (epigenetic) human disease. Using GeneGrid, one can quickly perform trio analysis, case-control studies, identify somatic SNPs and visualize and dynamically interact with these results in the context of other integrated Genomatix data content including: genomic annotation, biological pathways, and the supporting biomedical literature. GeneGrid also includes the ability to generate variant report summaries and provides link-outs to genetic testing providers. For an overview of the GeneGrid technology we invite you to visit: http://www.genomatix.de/solutions/genegrid.html The training course will consist of a GeneGrid overview lecture and instructor-led demonstration of how to import VCF and BAM files into the GeneGrid platform, followed by a hands-on training demonstrating the use of GeneGrid for variant annotation and analysis. At the end of the course, students will have learned how to: *import VCF and BAM files into the GeneGrid platform; *view VCF sample statistics and the associated metadata; *run a sample comparison; *annotate and filter variants; *view annotated variant data in the Genomatix Genome Browser; *use the results management features; *apply the mastery of GeneGrid to their own data 2018-10-26 13:00:00 Bldg 37,Room 6041 In-Person BTEP 0 BTEP: Variant Analysis using Genomatix GeneGrid
849
Description
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include:
  1. What is RNASeq ?
  2. What can it be used for ?
  3. Sequencing platforms
  4. Quality Control steps
  5. Experimental Design
  6. Data Analysis Workflows
  7. Identifying differentially expressed genes
  8. Discussion of relevant file ...Read More
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include:
  1. What is RNASeq ?
  2. What can it be used for ?
  3. Sequencing platforms
  4. Quality Control steps
  5. Experimental Design
  6. Data Analysis Workflows
  7. Identifying differentially expressed genes
  8. Discussion of relevant file formats and data conversion tools
  9. Approaches to detect splice variants and fusion genes
  10. NCI specific tools and software
Register
Organizer
BTEP
When
Mon, Nov 05, 2018 - 9:30 am - 11:30 am
Where
Bldg 10: FAES Classroom 3 (B1C207)
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include: What is RNASeq ? What can it be used for ? Sequencing platforms Quality Control steps Experimental Design Data Analysis Workflows Identifying differentially expressed genes Discussion of relevant file formats and data conversion tools Approaches to detect splice variants and fusion genes NCI specific tools and software 2018-11-05 09:30:00 Bldg 10: FAES Classroom 3 (B1C207) In-Person Peter FitzGerald (GAU) BTEP 0 BTEP: Introduction to RNA-Seq technology, Overview and Analyses
850
Description
RNA-seq, expression microarrays, and other omics profiling platforms are powerful tools for discovery, and analytical pipelines often return large numbers of significant genes or other markers.  This presents a challenge when trying to understand how all these results are relevant to the system under investigation.  QIAGEN’s Ingenuity Pathway Analysis (IPA) utilizes an unparalleled curated Knowledge Base derived from literature and public databases to help you understand how genes, miRNAs, proteins, or metabolites function, both ...Read More
RNA-seq, expression microarrays, and other omics profiling platforms are powerful tools for discovery, and analytical pipelines often return large numbers of significant genes or other markers.  This presents a challenge when trying to understand how all these results are relevant to the system under investigation.  QIAGEN’s Ingenuity Pathway Analysis (IPA) utilizes an unparalleled curated Knowledge Base derived from literature and public databases to help you understand how genes, miRNAs, proteins, or metabolites function, both independently and with other molecules in your results, to influence the underlying biology of your study.  IPA provides a powerful platform to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets, and is cited in >16,000 peer-reviewed articles. These training sessions will demonstrate how to utilize IPA's knowledge & discovery tools to relate the most recent literature findings to biological understanding of your data. Agenda 1:00pm - 4:00pm: Introduction to IPA and Getting Started with the Core Analysis This hands-on training (geared towards new users) will define the Qiagen Knowledge Base and how the tools within Ingenuity Pathway Analysis (IPA) draw on this information to provide powerful biological insights.  The session will also introduce Analysis Match, a new feature that allows users to compare their own data to a large spectrum of pre-compiled data from public repositories to determine similarity/dissimilarity at the biological level. This session will provide users training on how to perform the following tasks:
  • Querying the Qiagen Knowledge Base
  • Data Upload & Analysis of gene, transcript, protein & metabolite data
  • Pathway Analysis
  • Regulators and their directional effect on genes, functions and diseases
  • Downstream Effects
  • Analysis Match
Register
Organizer
BTEP
When
Mon, Nov 05, 2018 - 1:00 pm - 4:00 pm
Where
Bldg 10: FAES Classroom 3 (B1C207)
RNA-seq, expression microarrays, and other omics profiling platforms are powerful tools for discovery, and analytical pipelines often return large numbers of significant genes or other markers.  This presents a challenge when trying to understand how all these results are relevant to the system under investigation.  QIAGEN’s Ingenuity Pathway Analysis (IPA) utilizes an unparalleled curated Knowledge Base derived from literature and public databases to help you understand how genes, miRNAs, proteins, or metabolites function, both independently and with other molecules in your results, to influence the underlying biology of your study.  IPA provides a powerful platform to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets, and is cited in >16,000 peer-reviewed articles. These training sessions will demonstrate how to utilize IPA's knowledge & discovery tools to relate the most recent literature findings to biological understanding of your data. Agenda 1:00pm - 4:00pm: Introduction to IPA and Getting Started with the Core Analysis This hands-on training (geared towards new users) will define the Qiagen Knowledge Base and how the tools within Ingenuity Pathway Analysis (IPA) draw on this information to provide powerful biological insights.  The session will also introduce Analysis Match, a new feature that allows users to compare their own data to a large spectrum of pre-compiled data from public repositories to determine similarity/dissimilarity at the biological level. This session will provide users training on how to perform the following tasks: Querying the Qiagen Knowledge Base Data Upload & Analysis of gene, transcript, protein & metabolite data Pathway Analysis Regulators and their directional effect on genes, functions and diseases Downstream Effects Analysis Match 2018-11-05 13:00:00 Bldg 10: FAES Classroom 3 (B1C207) In-Person BTEP 0 BTEP: Introduction to IPA (Ingenuity Pathway Analysis) and the Core Analysis
848
Description
RNA-Seq Data Analysis in Partek Flow Please bring a laptop to this class - or let us know if you need to borrow one. Please printout and bring the handout from the class website. During this hands on training session, students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for RNA-Seq analysis: - Import data from .fastq files - Perform QA/QC (...Read More
RNA-Seq Data Analysis in Partek Flow Please bring a laptop to this class - or let us know if you need to borrow one. Please printout and bring the handout from the class website. During this hands on training session, students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for RNA-Seq analysis: - Import data from .fastq files - Perform QA/QC (Pre-alignment QA/QC, Post-alignment QA/QC) - Trim bases - Align reads to reference genome - Quantify gene/transcript abundance - Normalize gene counts - Detect differentially expressed genes - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualization:
      • Heat maps
      • Volcano plots
      • PCA scatterplot
      • Dot plots
      • Hierarchical clustering
      • Chromosome view
Register
Organizer
BTEP
When
Tue, Nov 06, 2018 - 9:30 am - 12:00 pm
Where
Rm 6041, NIH Bldg 37, Rm 6107
RNA-Seq Data Analysis in Partek Flow Please bring a laptop to this class - or let us know if you need to borrow one. Please printout and bring the handout from the class website. During this hands on training session, students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for RNA-Seq analysis: - Import data from .fastq files - Perform QA/QC (Pre-alignment QA/QC, Post-alignment QA/QC) - Trim bases - Align reads to reference genome - Quantify gene/transcript abundance - Normalize gene counts - Detect differentially expressed genes - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualization: Heat maps Volcano plots PCA scatterplot Dot plots Hierarchical clustering Chromosome view 2018-11-06 09:30:00 Rm 6041,NIH Bldg 37,Rm 6107 In-Person Xiaowen Wang (Partek) BTEP 0 BTEP: RNA-Seq Data Analysis in Partek Flow
846
Description
  1. Experimental Design Considerations in Variant Analysis
    1. Germline vs Somatic
    2. WGS vs WES
    3. Sample sizes and statistical power
  2. WGS/WES Pipelines at NIH
    1. Pipeline performance
    2. Using Pipeliner for internal and external data
  3. Variant QC, Annotation and Downstream Analysis
    1. <...Read More
  1. Experimental Design Considerations in Variant Analysis
    1. Germline vs Somatic
    2. WGS vs WES
    3. Sample sizes and statistical power
  2. WGS/WES Pipelines at NIH
    1. Pipeline performance
    2. Using Pipeliner for internal and external data
  3. Variant QC, Annotation and Downstream Analysis
    1. Variant QC and data correction
    2. Variant annotation and analysis tools
  4. Structural variation and multi-omic integration
Register
Organizer
BTEP
When
Wed, Nov 14, 2018 - 9:00 am - 11:00 am
Where
Ft. Detrick, Bldg. 549, Auditorium
Experimental Design Considerations in Variant Analysis Germline vs Somatic WGS vs WES Sample sizes and statistical power WGS/WES Pipelines at NIH Pipeline performance Using Pipeliner for internal and external data Variant QC, Annotation and Downstream Analysis Variant QC and data correction Variant annotation and analysis tools Structural variation and multi-omic integration 2018-11-14 09:00:00 Ft. Detrick,Bldg. 549,Auditorium In-Person BTEP 0 BTEP, Frederick: Introduction to Variant Analysis in WGS and WES by Dr. Justin Lack
847
Description
THIS EVENT HAS BEEN CANCELLED
10x Genomics: Whole Exome and Whole Genome Analysis
  • How 10x Genomics linked reads works
  • SNVs, copy number variations, structural variants and phasing of the variants from linked read data
  • Walk through of 10x Genomics WGS results
  • Integration of linked reads data with other platforms/technologies
All Structural Variants:
  • CCR Sequencing Facility analysis pipelines of structural variants from ...Read More
THIS EVENT HAS BEEN CANCELLED
10x Genomics: Whole Exome and Whole Genome Analysis
  • How 10x Genomics linked reads works
  • SNVs, copy number variations, structural variants and phasing of the variants from linked read data
  • Walk through of 10x Genomics WGS results
  • Integration of linked reads data with other platforms/technologies
All Structural Variants:
  • CCR Sequencing Facility analysis pipelines of structural variants from Illumina, linked reads, long reads and optical mapping
  • Walk through of SV/CNV pipelines, how they work and results from the SV pipelines
Register
Organizer
BTEP
When
Wed, Dec 05, 2018 - 9:00 am - 11:00 am
Where
Ft. Detrick, Bldg. 549, Auditorium
THIS EVENT HAS BEEN CANCELLED10x Genomics: Whole Exome and Whole Genome Analysis How 10x Genomics linked reads works SNVs, copy number variations, structural variants and phasing of the variants from linked read data Walk through of 10x Genomics WGS results Integration of linked reads data with other platforms/technologies All Structural Variants: CCR Sequencing Facility analysis pipelines of structural variants from Illumina, linked reads, long reads and optical mapping Walk through of SV/CNV pipelines, how they work and results from the SV pipelines 2018-12-05 09:00:00 Ft. Detrick,Bldg. 549,Auditorium In-Person BTEP 0 BTEP, Frederick: Variant Analysis: SNVs, CNVs and Structural Variants in WGS and WES - CANCELLED
222
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/a8032b731dee4058875d9a45b33ef376/playback 2019-02-26 14:30:00 In-Person CBIIT 0 Using BioDiscovery Nexus For Copy Number Analysis
223
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/6c09064170554e54911e2a275cedcd11/playback 2019-03-05 16:00:00 In-Person CBIIT 0 BioDiscovery Nexus Copy Number
226
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/3d246d9ba88a48f188e07a5f60fef3b3/playback 2019-03-12 14:00:00 In-Person 0 BioDiscovery Nexus Expression
227
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/f012b1e97835486fa8f62efa535aaf66/playback 2019-03-26 14:00:00 In-Person 0 Ingenuity Pathway Analysis Basics
229
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/f3ab408e4f8a4cc888d13aeed1a633bc/playback 2019-04-09 14:00:00 In-Person 0 Sequencher Webinar
853
Description
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the first in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. <...Read More
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the first in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. Class Agenda: The main goal of this presentation will be to provide an overview of the DNAnexus platform and to set the stage for the follow-on workshops to be held later in the week. This talk will:
  • Describe the current Pilot program
  • Explain how to get access to and make use of the platform
  • Highlight key resources available within DNAnexus
  • Highlight CCR support resources for getting the most out of this platform
  • Demo select feature of the platform, to illustrate its utility to CCR resarchers.
The first of the follow-up sessions will be aimed at Biologists who wish to make use of DNAnexus for analyzing their own data. The second will be targeted towards bioinformaticists and developers who wish to use this platform to process and share data and/or results, as well as to develop and disseminate new tools. Details and registration for the follow-up talks can be found here: II   DNAnexus - NGS data analysis from a biologist perspective III DNAnexus - A versatile platform for bioinformaticists and developers More about DNAnexus A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticians can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated tools and pipelines, the DNAnexus platform also allows the quick and efficient development of new analysis tools and the  porting of existing pipelines. DNAnexus is built upon the foundational security and features of AWS and AZURE. Key Features of DNAnexus:
  • Centralized online environment for genomic data analysis, storage  and collaboration
  • Web-based interface for ease of use
  • Command line interface for power-users
  • Large library of standard bioinformatic tools and pipelines
  • Scalable infrastructure that can instantly meet any computational and data storage demand
  • Data compliance in accordance with: HIPAA, CLIA, GCP, 21 CFR parts 11, 58, and 493, European Data Privacy laws and regulations
While attending in person is highly recommended this Workshop will be available via Webex for those not on the NIH campus.. DNAnexus Meeting number (access code): 739 435 126 Meeting password:  dWmxnp@9 Join the meeting. Join by phone 1-650-479-3207 Call-in toll number (US/Canada)
Register
Organizer
BTEP
When
Tue, Apr 09, 2019 - 3:00 pm - 5:00 pm
Where
NIH Bethesda B37 Rm 4041/4107
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the first in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. Class Agenda: The main goal of this presentation will be to provide an overview of the DNAnexus platform and to set the stage for the follow-on workshops to be held later in the week. This talk will: Describe the current Pilot program Explain how to get access to and make use of the platform Highlight key resources available within DNAnexus Highlight CCR support resources for getting the most out of this platform Demo select feature of the platform, to illustrate its utility to CCR resarchers. The first of the follow-up sessions will be aimed at Biologists who wish to make use of DNAnexus for analyzing their own data. The second will be targeted towards bioinformaticists and developers who wish to use this platform to process and share data and/or results, as well as to develop and disseminate new tools. Details and registration for the follow-up talks can be found here: II   DNAnexus - NGS data analysis from a biologist perspective III DNAnexus - A versatile platform for bioinformaticists and developers More about DNAnexus A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticians can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated tools and pipelines, the DNAnexus platform also allows the quick and efficient development of new analysis tools and the  porting of existing pipelines. DNAnexus is built upon the foundational security and features of AWS and AZURE. Key Features of DNAnexus: Centralized online environment for genomic data analysis, storage  and collaboration Web-based interface for ease of use Command line interface for power-users Large library of standard bioinformatic tools and pipelines Scalable infrastructure that can instantly meet any computational and data storage demand Data compliance in accordance with: HIPAA, CLIA, GCP, 21 CFR parts 11, 58, and 493, European Data Privacy laws and regulations While attending in person is highly recommended this Workshop will be available via Webex for those not on the NIH campus.. DNAnexus Meeting number (access code): 739 435 126 Meeting password:  dWmxnp@9 Join the meeting. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) 2019-04-09 15:00:00 NIH Bethesda B37 Rm 4041/4107 In-Person Peter FitzGerald (GAU) BTEP 0 Simplified Next-Generation-Sequence (NGS) Data Analysis using DNAnexus
851
Description
BTEP Wednesday, April 10, 2019 9:00 am  |  Eastern Daylight Time (New York, GMT-04:00)  |  2 hrs Meeting number (access code): 738 427 711 Meeting password: zPJpWP$6     10x Genomics: Whole Exome and Whole Genome Analysis
  • How 10x Genomics linked reads works
  • SNVs, copy number variations, structural variants and phasing of the variants from linked read data
  • Walk through of 10x Genomics WGS results
  • Integration of linked reads data with other platforms/technologies
...Read More
BTEP Wednesday, April 10, 2019 9:00 am  |  Eastern Daylight Time (New York, GMT-04:00)  |  2 hrs Meeting number (access code): 738 427 711 Meeting password: zPJpWP$6     10x Genomics: Whole Exome and Whole Genome Analysis
  • How 10x Genomics linked reads works
  • SNVs, copy number variations, structural variants and phasing of the variants from linked read data
  • Walk through of 10x Genomics WGS results
  • Integration of linked reads data with other platforms/technologies
All Structural Variants:
  • CCR Sequencing Facility analysis pipelines of structural variants from Illumina, linked reads, long reads and optical mapping
  • Walk through of SV/CNV pipelines, how they work and results from the SV pipelines
Register
Organizer
BTEP
When
Wed, Apr 10, 2019 - 9:00 am - 11:00 am
Where
Ft. Detrick, Bldg. 549, Auditorium
BTEP Wednesday, April 10, 2019 9:00 am  |  Eastern Daylight Time (New York, GMT-04:00)  |  2 hrs Meeting number (access code): 738 427 711 Meeting password: zPJpWP$6     10x Genomics: Whole Exome and Whole Genome Analysis How 10x Genomics linked reads works SNVs, copy number variations, structural variants and phasing of the variants from linked read data Walk through of 10x Genomics WGS results Integration of linked reads data with other platforms/technologies All Structural Variants: CCR Sequencing Facility analysis pipelines of structural variants from Illumina, linked reads, long reads and optical mapping Walk through of SV/CNV pipelines, how they work and results from the SV pipelines 2019-04-10 09:00:00 Ft. Detrick,Bldg. 549,Auditorium In-Person BTEP 0 BTEP, Frederick: Variant Analysis: SNVs, CNVs and Structural Variants in WGS and WES
852
Description
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the second in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. <...Read More
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the second in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. Class Agenda:

This workshop is aimed at those who are looking for a simple to use resource for the analysis of NGS data (i.e research scientists). In the form of a hands-on workshop this session will include:

  • A walk through the steps involved in setting up and managing an account
  • Details of uploading and downloading data
  • Running example workflows (RNA-Seq, ChIP-Seq, IGV integration, Variant analysis)
  • Interacting with the St. Jude cloud
  • Paths to getting additional assistance
Details and registration for the other talks in the series: I    Simplified Next-Generation-Sequence (NGS) Data Analysis using DNAnexus III DNAnexus – A versatile platform for bioinformaticists and developers Webex: DNAnexus – NGS data analysis from a biologist's perspective Thursday, April 11, 2019 10:00 am  |  Eastern Daylight Time (New York, GMT-04:00)  |  1 hr 30 mins Meeting number (access code): 733 188 237 Meeting password: Wx7NVxi? When it's time, join the meeting. More about DNAnexus A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticists can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated tools and pipelines, the DNAnexus platform also allows the quick and efficient development of new analysis tools and the  porting of existing pipelines. DNAnexus is built upon the foundational security and features of AWS and AZURE. Key Features of DNAnexus:
  • Centralized online environment for genomic data analysis, storage  and collaboration
  • Web-based interface for ease of use
  • Command line interface for power-users
  • Large library of standard bioinformatic tools and pipelines
  • Scalable infrastructure that can instantly meet any computational and data storage demand
  • Data compliance in accordance with: HIPAA, CLIA, GCP, 21 CFR parts 11, 58, and 493, European Data Privacy laws and regulations
Register
Organizer
BTEP
When
Thu, Apr 11, 2019 - 10:00 am - 11:30 am
Where
Building 37 Room 4041/4107
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the second in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. Class Agenda: This workshop is aimed at those who are looking for a simple to use resource for the analysis of NGS data (i.e research scientists). In the form of a hands-on workshop this session will include: A walk through the steps involved in setting up and managing an account Details of uploading and downloading data Running example workflows (RNA-Seq, ChIP-Seq, IGV integration, Variant analysis) Interacting with the St. Jude cloud Paths to getting additional assistance Details and registration for the other talks in the series: I    Simplified Next-Generation-Sequence (NGS) Data Analysis using DNAnexus III DNAnexus – A versatile platform for bioinformaticists and developers Webex: DNAnexus – NGS data analysis from a biologist's perspective Thursday, April 11, 2019 10:00 am  |  Eastern Daylight Time (New York, GMT-04:00)  |  1 hr 30 mins Meeting number (access code): 733 188 237 Meeting password: Wx7NVxi? When it's time, join the meeting. More about DNAnexus A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticists can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated tools and pipelines, the DNAnexus platform also allows the quick and efficient development of new analysis tools and the  porting of existing pipelines. DNAnexus is built upon the foundational security and features of AWS and AZURE. Key Features of DNAnexus: Centralized online environment for genomic data analysis, storage  and collaboration Web-based interface for ease of use Command line interface for power-users Large library of standard bioinformatic tools and pipelines Scalable infrastructure that can instantly meet any computational and data storage demand Data compliance in accordance with: HIPAA, CLIA, GCP, 21 CFR parts 11, 58, and 493, European Data Privacy laws and regulations 2019-04-11 10:00:00 Building 37 Room 4041/4107 In-Person Peter FitzGerald (GAU) BTEP 0 DNAnexus - NGS data analysis from a biologist's perspective
230
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/8a6265d50fb241b292cdcaeb6f20aa1b/playback 2019-04-11 11:00:00 In-Person 0 Qlucore Webinar
854
Description
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the third in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. <...Read More
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the third in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. Class Agenda:

This workshop is aimed at developers, bioinformaticists and computational professionals, who are familiar with the command-line, scripting and batch data processing.  In the form of a hands-on workshop this session will include:

  • A walk through the steps involved in setting up and managing an account
  • Details of uploading and downloading data
  • Details about DNAnexus's dx-toolkit
  • Examples of how to run remote batch processes from the command-line on a local computer
  • The basics of developing and deploying your own App/applet.
Details and registration for the other talks in the series: I  Simplified Next-Generation-Sequence (NGS) Data Analysis using DNAnexus II DNAnexus - NGS data analysis from a biologist perspective More about DNAnexus A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticists can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated tools and pipelines, the DNAnexus platform also allows the quick and efficient development of new analysis tools and the  porting of existing pipelines. DNAnexus is built upon the foundational security and features of AWS and AZURE. Key Features of DNAnexus:
  • Centralized online environment for genomic data analysis, storage  and collaboration
  • Web-based interface for ease of use
  • Command line interface for power-users
  • Large library of standard bioinformatic tools and pipelines
  • Scalable infrastructure that can instantly meet any computational and data storage demand
  • Data compliance in accordance with: HIPAA, CLIA, GCP, 21 CFR parts 11, 58, and 493, European Data Privacy laws and regulations
DNAnexus - Developers Friday, April 12, 2019 10:30 am | Eastern Daylight Time (New York, GMT-04:00) | 1 hr Meeting number (access code): 733 723 913 Meeting password: FribB6E? Join the meeting.
Register
Organizer
BTEP
When
Fri, Apr 12, 2019 - 10:00 am - 11:30 am
Where
Rm 2041/2107, NIH Bldg 37
DNAnexus provides a secure cloud based platform for the analysis, storage and sharing of many different types of genomic data. CCR has recently established a Pilot program to enable CCR researchers to use this powerful tool to process their own NGS data. The platform includes workflows for; RNA-Seq, DNA-Seq, ChIP-Seq etc. This talk is the third in a three-part series, designed to introduce CCR scientists and bioinformaticists to this powerful resource. Class Agenda: This workshop is aimed at developers, bioinformaticists and computational professionals, who are familiar with the command-line, scripting and batch data processing.  In the form of a hands-on workshop this session will include: A walk through the steps involved in setting up and managing an account Details of uploading and downloading data Details about DNAnexus's dx-toolkit Examples of how to run remote batch processes from the command-line on a local computer The basics of developing and deploying your own App/applet. Details and registration for the other talks in the series: I  Simplified Next-Generation-Sequence (NGS) Data Analysis using DNAnexus II DNAnexus - NGS data analysis from a biologist perspective More about DNAnexus A point-and-click web environment provides ready access to powerful tools for the analysis, integration and sharing of complex data. Additionally, experienced researchers and bioinformaticists can interact with the platform through a command-line interface, which allows for quick integration with current system, and the automated processing of large data sets.  Beyond its extensive library of integrated tools and pipelines, the DNAnexus platform also allows the quick and efficient development of new analysis tools and the  porting of existing pipelines. DNAnexus is built upon the foundational security and features of AWS and AZURE. Key Features of DNAnexus: Centralized online environment for genomic data analysis, storage  and collaboration Web-based interface for ease of use Command line interface for power-users Large library of standard bioinformatic tools and pipelines Scalable infrastructure that can instantly meet any computational and data storage demand Data compliance in accordance with: HIPAA, CLIA, GCP, 21 CFR parts 11, 58, and 493, European Data Privacy laws and regulations DNAnexus - Developers Friday, April 12, 2019 10:30 am | Eastern Daylight Time (New York, GMT-04:00) | 1 hr Meeting number (access code): 733 723 913 Meeting password: FribB6E? Join the meeting. 2019-04-12 10:00:00 Rm 2041/2107,NIH Bldg 37 In-Person Peter FitzGerald (GAU) BTEP 0 DNAnexus – A versatile platform for bioinformaticists and developers
855
Description
You do not need to currently have an account on Biowulf to attend this class. Please bring your own computer or let us know if you need to borrow one (ncibtep@nih.gov) Attendees will learn to:
  • Work at the unix command line – explore file structure, create files
  • Move large data files using Globus and other tools
  • Understand environment modules
  • Run scientific software programs in interactive and ...Read More
You do not need to currently have an account on Biowulf to attend this class. Please bring your own computer or let us know if you need to borrow one (ncibtep@nih.gov) Attendees will learn to:
  • Work at the unix command line – explore file structure, create files
  • Move large data files using Globus and other tools
  • Understand environment modules
  • Run scientific software programs in interactive and batch modes
  • Learn about different NGS file formats
  • Use NGS analysis tools
While attending in person is highly recommended, WebEx will be provided for those not able to attend. Practical Bioinformatics: working at the unix command line on Biowulf Hosted by Amy Stonelake Tuesday, Apr 16, 2019 10:00 am | 6 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 734 847 518 Password: pMNZqn?2 https://cbiit.webex.com/cbiit/j.php?MTID=mdcd7510baaed6ea0f22baebc3280fe30 Join by video system Dial 734847518@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 734 847 518
Register
Organizer
BTEP
When
Tue, Apr 16, 2019 - 10:00 am - 3:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
You do not need to currently have an account on Biowulf to attend this class. Please bring your own computer or let us know if you need to borrow one (ncibtep@nih.gov) Attendees will learn to: Work at the unix command line – explore file structure, create files Move large data files using Globus and other tools Understand environment modules Run scientific software programs in interactive and batch modes Learn about different NGS file formats Use NGS analysis tools While attending in person is highly recommended, WebEx will be provided for those not able to attend. Practical Bioinformatics: working at the unix command line on Biowulf Hosted by Amy Stonelake Tuesday, Apr 16, 2019 10:00 am | 6 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 734 847 518 Password: pMNZqn?2 https://cbiit.webex.com/cbiit/j.php?MTID=mdcd7510baaed6ea0f22baebc3280fe30 Join by video system Dial 734847518@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 734 847 518 2019-04-16 10:00:00 Bldg 10 FAES room 4 (B1C205) In-Person Amy Stonelake (BTEP) BTEP 0 Practical Bioinformatics: Working at the unix command line on Biowulf
231 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/68a77da9faa247b691939fdf7dba6899/playback 2019-04-16 14:00:00 In-Person 0 Ingenuity Pathway Analysis Advanced
856
Description
Drop-in session: Bring your own computer or let us know if you need to borrow one (ncibtep@nih.gov).  Get help logging into Biowulf, and working with your own data.
  • Logging into your Biowulf account
  • Requesting additional disk space
  • Unzipping large data files
  • Setting up your globus endpoint
  • Navigating the directory tree
  • Loading environment modules
  • Creating batch scripts and swarm filesRead More
Drop-in session: Bring your own computer or let us know if you need to borrow one (ncibtep@nih.gov).  Get help logging into Biowulf, and working with your own data.
  • Logging into your Biowulf account
  • Requesting additional disk space
  • Unzipping large data files
  • Setting up your globus endpoint
  • Navigating the directory tree
  • Loading environment modules
  • Creating batch scripts and swarm files
Register
Organizer
BTEP
When
Fri, Apr 19, 2019 - 10:00 am - 3:00 pm
Where
Rm 2041/2107, NIH Bldg 37
Drop-in session: Bring your own computer or let us know if you need to borrow one (ncibtep@nih.gov).  Get help logging into Biowulf, and working with your own data. Logging into your Biowulf account Requesting additional disk space Unzipping large data files Setting up your globus endpoint Navigating the directory tree Loading environment modules Creating batch scripts and swarm files 2019-04-19 10:00:00 Rm 2041/2107,NIH Bldg 37 In-Person Amy Stonelake (BTEP) BTEP 0 Practical Bioinformatics: Drop-in session: Working at the unix command line on Biowulf
232
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/7e5c4e0f1dda453d98b5cee6309d35d5/playback 2019-05-07 11:30:00 In-Person 0 Omics Data Analysis in Partek
858
Description
This workshop will teach the basic concepts and practical aspects of data analysis. Topics covered will include: experimental approach, quality control, peak calling, and basic biological interpretation. No computer is required for this class, there is no hands-on work. 9:30-10:15 “ChIP-seq considerations” This section will focus on the importance of experimental design in ChIP-seq. Topics include: variations on ChIP-seq, inputs, replicates, the limitations of ChIP-seq, and the value of follow-up analyses. 10:15-10:30 Break 10:30-12:00 “Analysis ...Read More
This workshop will teach the basic concepts and practical aspects of data analysis. Topics covered will include: experimental approach, quality control, peak calling, and basic biological interpretation. No computer is required for this class, there is no hands-on work. 9:30-10:15 “ChIP-seq considerations” This section will focus on the importance of experimental design in ChIP-seq. Topics include: variations on ChIP-seq, inputs, replicates, the limitations of ChIP-seq, and the value of follow-up analyses. 10:15-10:30 Break 10:30-12:00 “Analysis of ChIP-seq data This section will focus on the analytical pipeline that is implemented by CCBR and NCBR. Time will be spent discussing the tools used, why they were chosen, and how to make sense of their outputs. Example results will be shown. Topics include: quality control and read alignment, deduplication, normalization, gene coverage plotting, peak calling, motif analysis, and more. 12:00-12:30 Questions ************************** If you are unable to attend in person, WebEx will be provided. A recording of the meeting will be made available on the BTEP web site. ChIP-Seq Data Analysis: Probing DNA-Protein Interactions Hosted by Amy Stonelake Friday, May 10, 2019 9:30 am | 3 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 737 294 613 Password: JwrMe6p@ https://cbiit.webex.com/cbiit/j.php?MTID=m4b85110c7a550bd3815d04ae184f6e1b   Join by video system Dial 737294613@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number.   Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 737 294 613
Register
Organizer
BTEP
When
Fri, May 10, 2019 - 9:30 am - 12:30 pm
Where
Rm 2041/2107, NIH Bldg 37
This workshop will teach the basic concepts and practical aspects of data analysis. Topics covered will include: experimental approach, quality control, peak calling, and basic biological interpretation. No computer is required for this class, there is no hands-on work. 9:30-10:15 “ChIP-seq considerations” This section will focus on the importance of experimental design in ChIP-seq. Topics include: variations on ChIP-seq, inputs, replicates, the limitations of ChIP-seq, and the value of follow-up analyses. 10:15-10:30 Break 10:30-12:00 “Analysis of ChIP-seq data This section will focus on the analytical pipeline that is implemented by CCBR and NCBR. Time will be spent discussing the tools used, why they were chosen, and how to make sense of their outputs. Example results will be shown. Topics include: quality control and read alignment, deduplication, normalization, gene coverage plotting, peak calling, motif analysis, and more. 12:00-12:30 Questions ************************** If you are unable to attend in person, WebEx will be provided. A recording of the meeting will be made available on the BTEP web site. ChIP-Seq Data Analysis: Probing DNA-Protein Interactions Hosted by Amy Stonelake Friday, May 10, 2019 9:30 am | 3 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 737 294 613 Password: JwrMe6p@ https://cbiit.webex.com/cbiit/j.php?MTID=m4b85110c7a550bd3815d04ae184f6e1b   Join by video system Dial 737294613@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number.   Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 737 294 613 2019-05-10 09:30:00 Rm 2041/2107,NIH Bldg 37 In-Person Alexei Lobanov (CCBR),Tovah Markowitz (NCBR),Paul Schaughency (NCBR) BTEP 0 ChIP-Seq Data Analysis: Probing DNA-Protein Interactions
860
Description
THIS EVENT HAS BEEN CANCELLED
We will review the three guiding principles of data visualization, basic formats for displaying data (including scatter plots, box plots, etc…), cover some guidelines about when to transform data before plotting, some strategies for displaying high dimensional data (including PCA and t-SNE plots), and review the use of color in data visualization. The presentation will close with a review of all the code used to create the figures shown ...Read More
THIS EVENT HAS BEEN CANCELLED
We will review the three guiding principles of data visualization, basic formats for displaying data (including scatter plots, box plots, etc…), cover some guidelines about when to transform data before plotting, some strategies for displaying high dimensional data (including PCA and t-SNE plots), and review the use of color in data visualization. The presentation will close with a review of all the code used to create the figures shown in the presentation. ***If you are unable to attend in person, this seminar is available by WebEx*** BTEP: RNA-Seq Workshop: Graphical Excellence and Integrity: How to make your data sing! Hosted by Amy Stonelake Tuesday, May 21, 2019 9:30 am | 2 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 737 719 985 Password: sGm9dPM* https://cbiit.webex.com/cbiit/j.php?MTID=m51b4e724ee3c088f0ceb73600aa9ba33 Join by video system Dial 737719985@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 737 719 985      
Register
Organizer
BTEP
When
Tue, May 21, 2019 - 9:30 am - 11:00 am
Where
Bldg 10: FAES Classroom 3 (B1C207)
THIS EVENT HAS BEEN CANCELLEDWe will review the three guiding principles of data visualization, basic formats for displaying data (including scatter plots, box plots, etc…), cover some guidelines about when to transform data before plotting, some strategies for displaying high dimensional data (including PCA and t-SNE plots), and review the use of color in data visualization. The presentation will close with a review of all the code used to create the figures shown in the presentation. ***If you are unable to attend in person, this seminar is available by WebEx*** BTEP: RNA-Seq Workshop: Graphical Excellence and Integrity: How to make your data sing! Hosted by Amy Stonelake Tuesday, May 21, 2019 9:30 am | 2 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 737 719 985 Password: sGm9dPM* https://cbiit.webex.com/cbiit/j.php?MTID=m51b4e724ee3c088f0ceb73600aa9ba33 Join by video system Dial 737719985@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 737 719 985       2019-05-21 09:30:00 Bldg 10: FAES Classroom 3 (B1C207) In-Person BTEP 0 BTEP, RNA-Seq Workshop: Graphical Excellence and Integrity: How to make your data sing! - CANCELLED
863
Description
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include:
  1. What is RNASeq ?
  2. What can it be used for ?
  3. Sequencing platforms
  4. Quality Control steps
  5. Experimental Design
  6. Data Analysis Workflows
  7. Identifying differentially expressed genes
  8. Discussion of relevant file ...Read More
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include:
  1. What is RNASeq ?
  2. What can it be used for ?
  3. Sequencing platforms
  4. Quality Control steps
  5. Experimental Design
  6. Data Analysis Workflows
  7. Identifying differentially expressed genes
  8. Discussion of relevant file formats and data conversion tools
  9. Approaches to detect splice variants and fusion genes
  10. NCI specific tools and software
(This is a repeat of the seminar that was held Nov 5, 2018) If you are unable to attend in person, WebEx and a recording will be provided. BTEP, RNA-Seq Workshop: Intro to RNA-Seq Technology, Overview and Analyses, Part Two Hosted by Amy Stonelake Tuesday, May 21, 2019 1:00 pm | 1 hour | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 734 079 311 Password: 3mEWpkt* https://cbiit.webex.com/cbiit/j.php?MTID=m1731d9a9f087d1f3759471a64f77cd99 Join by video system Dial 734079311@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 734 079 311
Register
Organizer
BTEP
When
Tue, May 21, 2019 - 1:00 pm - 2:00 pm
Where
FAES Room 3 – B1C207
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include: What is RNASeq ? What can it be used for ? Sequencing platforms Quality Control steps Experimental Design Data Analysis Workflows Identifying differentially expressed genes Discussion of relevant file formats and data conversion tools Approaches to detect splice variants and fusion genes NCI specific tools and software (This is a repeat of the seminar that was held Nov 5, 2018) If you are unable to attend in person, WebEx and a recording will be provided. BTEP, RNA-Seq Workshop: Intro to RNA-Seq Technology, Overview and Analyses, Part Two Hosted by Amy Stonelake Tuesday, May 21, 2019 1:00 pm | 1 hour | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 734 079 311 Password: 3mEWpkt* https://cbiit.webex.com/cbiit/j.php?MTID=m1731d9a9f087d1f3759471a64f77cd99 Join by video system Dial 734079311@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 734 079 311 2019-05-21 13:00:00 FAES Room 3 – B1C207 In-Person Peter FitzGerald (GAU) BTEP 0 BTEP, RNA-Seq Workshop: Introduction to RNA-Seq Technology: Overview and Analyses, Part Two
861
Description
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include:
  1. What is RNASeq ?
  2. What can it be used for ?
  3. Sequencing platforms
  4. Quality Control steps
  5. Experimental Design
  6. Data Analysis Workflows
  7. Identifying differentially expressed genes
  8. Discussion of relevant file ...Read More
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include:
  1. What is RNASeq ?
  2. What can it be used for ?
  3. Sequencing platforms
  4. Quality Control steps
  5. Experimental Design
  6. Data Analysis Workflows
  7. Identifying differentially expressed genes
  8. Discussion of relevant file formats and data conversion tools
  9. Approaches to detect splice variants and fusion genes
  10. NCI specific tools and software
Followed by: A Practical Guide to Interpreting RNA-Seq Data Next-generation sequencing (NGS) RNA-seq experiments generate millions of reads per run and problems can arise. Before analyzing the data to draw biological conclusions, it is important to perform quality control checks to minimize the effects of sequencing error, biases in the data, or contamination.  In this talk, we discuss the theory and practice of assessing the quality of RNA-Seq data, as well as analysis strategies for mitigating technical noise using practical examples. If you are unable to attend in person, the workshop will be available via WebEx and recorded. BTEP, RNA-Seq Workshop: Intro to RNA-Seq Technology, Overview and Analyses Hosted by Amy Stonelake Tuesday, May 21, 2019 2:00 pm | 3 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 731 553 624 Password: KbQPp4E@ https://cbiit.webex.com/cbiit/j.php?MTID=m1d788955c1d9aa1ec6482aa68335b255 Join by video system Dial 731553624@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 731 553 624 Link to WebEx recording: Streaming recording link: https://cbiit.webex.com/cbiit/ldr.php?RCID=7d4d2caf8bf8bf0992a12c060113ae9e Download recording link: https://cbiit.webex.com/cbiit/lsr.php?RCID=18ec1c00faa1cf372f186442ca83bc4c  
Register
Organizer
BTEP
When
Tue, May 21, 2019 - 2:00 pm - 5:00 pm
Where
Building 37 Room 4041/4107
This introductory lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. The specific topics covered include: What is RNASeq ? What can it be used for ? Sequencing platforms Quality Control steps Experimental Design Data Analysis Workflows Identifying differentially expressed genes Discussion of relevant file formats and data conversion tools Approaches to detect splice variants and fusion genes NCI specific tools and software Followed by: A Practical Guide to Interpreting RNA-Seq Data Next-generation sequencing (NGS) RNA-seq experiments generate millions of reads per run and problems can arise. Before analyzing the data to draw biological conclusions, it is important to perform quality control checks to minimize the effects of sequencing error, biases in the data, or contamination.  In this talk, we discuss the theory and practice of assessing the quality of RNA-Seq data, as well as analysis strategies for mitigating technical noise using practical examples. If you are unable to attend in person, the workshop will be available via WebEx and recorded. BTEP, RNA-Seq Workshop: Intro to RNA-Seq Technology, Overview and Analyses Hosted by Amy Stonelake Tuesday, May 21, 2019 2:00 pm | 3 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 731 553 624 Password: KbQPp4E@ https://cbiit.webex.com/cbiit/j.php?MTID=m1d788955c1d9aa1ec6482aa68335b255 Join by video system Dial 731553624@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 731 553 624 Link to WebEx recording: Streaming recording link: https://cbiit.webex.com/cbiit/ldr.php?RCID=7d4d2caf8bf8bf0992a12c060113ae9e Download recording link: https://cbiit.webex.com/cbiit/lsr.php?RCID=18ec1c00faa1cf372f186442ca83bc4c   2019-05-21 14:00:00 Building 37 Room 4041/4107 In-Person Peter FitzGerald (GAU), BTEP 0 BTEP, RNA-Seq Workshop: Introduction to RNA-Seq Technology: Overview and Analyses
862
Description
Next-generation sequencing (NGS) RNA-seq experiments generate millions of reads per run and problems can arise. Before analyzing the data to draw biological conclusions, it is important to perform quality control checks to minimize the effects of sequencing error, biases in the data, or contamination.  In this talk, we discuss the theory and practice of assessing the quality of RNA-Seq data, as well as analysis strategies for mitigating technical noise using practical examples. If you are ...Read More
Next-generation sequencing (NGS) RNA-seq experiments generate millions of reads per run and problems can arise. Before analyzing the data to draw biological conclusions, it is important to perform quality control checks to minimize the effects of sequencing error, biases in the data, or contamination.  In this talk, we discuss the theory and practice of assessing the quality of RNA-Seq data, as well as analysis strategies for mitigating technical noise using practical examples. If you are unable to attend in person, WebEx and a recording will be provided. BTEP, RNA-Seq Workshop: A Practical Guide to Interpreting RNA-Seq Data Hosted by Amy Stonelake Tuesday, May 21, 2019 2:00 pm | 1 hour | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 738 718 524 Password: yGPsYJ2? https://cbiit.webex.com/cbiit/j.php?MTID=mc3f0b1c9cb574924ac24bfe3fd343d8f Join by video system Dial 738718524@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 738 718 524  
Register
Organizer
BTEP
When
Tue, May 21, 2019 - 2:00 pm - 4:00 pm
Where
FAES Room 3 – B1C207
Next-generation sequencing (NGS) RNA-seq experiments generate millions of reads per run and problems can arise. Before analyzing the data to draw biological conclusions, it is important to perform quality control checks to minimize the effects of sequencing error, biases in the data, or contamination.  In this talk, we discuss the theory and practice of assessing the quality of RNA-Seq data, as well as analysis strategies for mitigating technical noise using practical examples. If you are unable to attend in person, WebEx and a recording will be provided. BTEP, RNA-Seq Workshop: A Practical Guide to Interpreting RNA-Seq Data Hosted by Amy Stonelake Tuesday, May 21, 2019 2:00 pm | 1 hour | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 738 718 524 Password: yGPsYJ2? https://cbiit.webex.com/cbiit/j.php?MTID=mc3f0b1c9cb574924ac24bfe3fd343d8f Join by video system Dial 738718524@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 738 718 524   2019-05-21 14:00:00 FAES Room 3 – B1C207 In-Person BTEP 0 BTEP, RNA-Seq Workshop: A Practical Guide to Interpreting RNA-Seq Data
859
Description
  • BTEP personnel will be available to help answer any questions you might have from our RNA-Seq lectures earlier this week.
  • Check out RNA-Seq pipelines available  to CCR researchers
  • Also - Get help working at the unix command line
  • stop by anytime between 10 AM and 3 PM and stay as long as you like
     
  • BTEP personnel will be available to help answer any questions you might have from our RNA-Seq lectures earlier this week.
  • Check out RNA-Seq pipelines available  to CCR researchers
  • Also - Get help working at the unix command line
  • stop by anytime between 10 AM and 3 PM and stay as long as you like
     
Register
Organizer
BTEP
When
Fri, May 24, 2019 - 10:00 am - 3:00 pm
Where
Rm 2041/2107, NIH Bldg 37
BTEP personnel will be available to help answer any questions you might have from our RNA-Seq lectures earlier this week. Check out RNA-Seq pipelines available  to CCR researchers Also - Get help working at the unix command line stop by anytime between 10 AM and 3 PM and stay as long as you like       2019-05-24 10:00:00 Rm 2041/2107,NIH Bldg 37 In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, RNA-Seq Week: Hands-on drop-in session on RNA-Seq
233
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/3960c8368ef84e0a8a89d72ac5ff1fa4/playback 2019-05-28 12:00:00 In-Person 0 Lasergene Webinar
234
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/c7d0446cf223475db3d1e9d2a72b9175/playback 2019-06-04 12:00:00 In-Person 0 Metacore Webinar
857
Description
You do not need to currently have an account on Biowulf to attend this class. Computers are provided in the classroom, you may also bring your own laptop. If you bring your own laptop, please make sure the following software is installed [PC - Putty SSH and telnet client (putty.org), WinSCP (https://sourceforge.net/projects/winscp/)], (Mac - no additional software needed). Attendees will learn to:
  • Work at the unix command line – ...Read More
You do not need to currently have an account on Biowulf to attend this class. Computers are provided in the classroom, you may also bring your own laptop. If you bring your own laptop, please make sure the following software is installed [PC - Putty SSH and telnet client (putty.org), WinSCP (https://sourceforge.net/projects/winscp/)], (Mac - no additional software needed). Attendees will learn to:
  • Work at the unix command line – explore file structure, create files
  • Move large data files using Globus and other tools
  • Understand environment modules
  • Run scientific software programs in interactive and batch modes
  • Learn about different NGS file formats
  • Use NGS analysis tools
This is a repeat of the class that was held on the NIH campus April 16, 2019.
Register
Organizer
BTEP
When
Wed, Jun 05, 2019 - 10:00 am - 3:00 pm
Where
Building 549 Scientific Library Frederick MD
You do not need to currently have an account on Biowulf to attend this class. Computers are provided in the classroom, you may also bring your own laptop. If you bring your own laptop, please make sure the following software is installed [PC - Putty SSH and telnet client (putty.org), WinSCP (https://sourceforge.net/projects/winscp/)], (Mac - no additional software needed). Attendees will learn to: Work at the unix command line – explore file structure, create files Move large data files using Globus and other tools Understand environment modules Run scientific software programs in interactive and batch modes Learn about different NGS file formats Use NGS analysis tools This is a repeat of the class that was held on the NIH campus April 16, 2019. 2019-06-05 10:00:00 Building 549 Scientific Library Frederick MD In-Person Amy Stonelake (BTEP) BTEP 0 BTEP FREDERICK: Practical Bioinformatics: working at the unix command line on Biowulf
235
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/6e3c60516aa64b7c8371a7cf2e5328b0/playback 2019-06-11 12:00:00 In-Person 0 DNASTAR Lasergene -Genomics Suite
864
Description
Upcoming hands-on, drop-in BTEP sessions held in Frederick, Ft. Detrick, Bldg 549, Rm 549B Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
Upcoming dates:
  • July 31, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
  • ...Read More
Upcoming hands-on, drop-in BTEP sessions held in Frederick, Ft. Detrick, Bldg 549, Rm 549B Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
Upcoming dates:
  • July 31, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
  • Aug 28, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
  • Sep 25, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
  • Oct 23, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
  • Nov 20, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
  • Dec 18, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
Register
Organizer
BTEP
When
Wed, Jun 12, 2019 - 10:00 am - 12:30 pm
Where
Rm E-1202
Upcoming hands-on, drop-in BTEP sessions held in Frederick, Ft. Detrick, Bldg 549, Rm 549B Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Finding, understanding and moving your data Tools available to CCR personnel for RNA-seq data analysis Upcoming dates: July 31, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Aug 28, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Sep 25, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Oct 23, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Nov 20, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Dec 18, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM 2019-06-12 10:00:00 Rm E-1202 In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Hands-on, drop-in session for working at the Unix command line on Biowulf
865
Description
Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
Upcoming dates:
  • Jul 24, Weds, Bldg 37, Rm 6107, 1 PM – 4 PM
  • Aug 22, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM
  • Sep 19, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PMRead More
Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
Upcoming dates:
  • Jul 24, Weds, Bldg 37, Rm 6107, 1 PM – 4 PM
  • Aug 22, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM
  • Sep 19, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM
  • Oct 17, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM
  • Nov 12, Tues, Bldg 37, Rm6107, 1 PM – 4 PM
  • Dec 10, Tues, Bldg 37, Rm 6107, 1 PM – 4 PM
Register
Organizer
BTEP
When
Fri, Jun 21, 2019 - 10:00 am - 3:00 pm
Where
Rm 2041/2107, NIH Bldg 37
Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Finding, understanding and moving your data Tools available to CCR personnel for RNA-seq data analysis Upcoming dates: Jul 24, Weds, Bldg 37, Rm 6107, 1 PM – 4 PM Aug 22, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Sep 19, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Oct 17, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Nov 12, Tues, Bldg 37, Rm6107, 1 PM – 4 PM Dec 10, Tues, Bldg 37, Rm 6107, 1 PM – 4 PM 2019-06-21 10:00:00 Rm 2041/2107,NIH Bldg 37 In-Person Amy Stonelake (BTEP) BTEP 0 BTEP: NIH, Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
877
Description
Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >16,000 peer-reviewed articles, and the class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research.  The training session will mainly focus on biological interpretation of expression data and comparison with public data ...Read More
Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >16,000 peer-reviewed articles, and the class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research.  The training session will mainly focus on biological interpretation of expression data and comparison with public data sets using Analysis Match, but will also cover multiple ways to query IPA’s Knowledgebase in the absence of data. Attendees should bring their own laptop computers. ****** If unable to attend in person, WebEx will be provided. BTEP: Interpreting Gene Expression Data with Qiagen Ingenuity Pathway Analysis (IPA) Hosted by Amy Stonelake Wednesday, Jul 17, 2019 1:00 pm | 3 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 739 415 196 Password: Jfumyq@9 https://cbiit.webex.com/cbiit/j.php?MTID=m00d87cab804802958dff9d546b39014c Join by video system Dial 739415196@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 739 415 196
Register
Organizer
BTEP
When
Wed, Jul 17, 2019 - 1:00 pm - 4:00 pm
Where
Bldg 37, Room 6041
Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in >16,000 peer-reviewed articles, and the class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research.  The training session will mainly focus on biological interpretation of expression data and comparison with public data sets using Analysis Match, but will also cover multiple ways to query IPA’s Knowledgebase in the absence of data. Attendees should bring their own laptop computers. ****** If unable to attend in person, WebEx will be provided. BTEP: Interpreting Gene Expression Data with Qiagen Ingenuity Pathway Analysis (IPA) Hosted by Amy Stonelake Wednesday, Jul 17, 2019 1:00 pm | 3 hours | (UTC-05:00) Eastern Time (US & Canada) Meeting number: 739 415 196 Password: Jfumyq@9 https://cbiit.webex.com/cbiit/j.php?MTID=m00d87cab804802958dff9d546b39014c Join by video system Dial 739415196@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in toll number (US/Canada) Access code: 739 415 196 2019-07-17 13:00:00 Bldg 37,Room 6041 In-Person BTEP 0 BTEP: Interpreting Gene Expression Data with Qiagen Ingenuity Pathway Analysis (IPA)
236 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/96b9adfc1c8645219a9772a601951732/playback 2019-07-23 16:00:00 In-Person 0 Introduction to Public RNASeq Data Analysis using WebMeV
866
Description
THIS EVENT HAS BEEN CANCELLED
Drop-in anytime during the session. Bring your own computer. We will be covering the following topics: - Unix bootcamp – learn to navigate a Unix file system and move, copy, and edit files - Looking at the quality of your sequence data with FastQC and MultiQC - Trimming sequences with CutAdapt   Upcoming dates: Aug 22, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Sep 19, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Oct 17, Thurs, Bldg 37, Rm 4041/4107, 10 ...Read More
THIS EVENT HAS BEEN CANCELLED
Drop-in anytime during the session. Bring your own computer. We will be covering the following topics: - Unix bootcamp – learn to navigate a Unix file system and move, copy, and edit files - Looking at the quality of your sequence data with FastQC and MultiQC - Trimming sequences with CutAdapt   Upcoming dates: Aug 22, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Sep 19, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Oct 17, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Nov 12, Tues, Bldg 37, Rm6107, 1 PM – 4 PM Dec 10, Tues, Bldg 37, Rm 6107, 1 PM – 4 PM
Register
Organizer
BTEP
When
Wed, Jul 24, 2019 - 12:00 pm - 3:00 pm
Where
NIH Bldg 37, Rm 6107
THIS EVENT HAS BEEN CANCELLEDDrop-in anytime during the session. Bring your own computer. We will be covering the following topics: - Unix bootcamp – learn to navigate a Unix file system and move, copy, and edit files - Looking at the quality of your sequence data with FastQC and MultiQC - Trimming sequences with CutAdapt   Upcoming dates: Aug 22, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Sep 19, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Oct 17, Thurs, Bldg 37, Rm 4041/4107, 10 AM – 3 PM Nov 12, Tues, Bldg 37, Rm6107, 1 PM – 4 PM Dec 10, Tues, Bldg 37, Rm 6107, 1 PM – 4 PM 2019-07-24 12:00:00 NIH Bldg 37,Rm 6107 In-Person Amy Stonelake (BTEP) BTEP 0 BTEP: NIH, Hands-on, drop-in session on Unix/Biowulf and RNA-Seq - CANCELLED
871
Description
Drop-in anytime during the session. Bring your own computer. We will be covering the following topics: - Unix bootcamp – learn to navigate a Unix file system and move, copy, and edit files - Looking at the quality of your sequence data with FastQC and MultiQC - Trimming sequences with CutAdapt Upcoming dates, Frederick: Aug 28, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Sep 25, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Oct 23, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Nov 20, ...Read More
Drop-in anytime during the session. Bring your own computer. We will be covering the following topics: - Unix bootcamp – learn to navigate a Unix file system and move, copy, and edit files - Looking at the quality of your sequence data with FastQC and MultiQC - Trimming sequences with CutAdapt Upcoming dates, Frederick: Aug 28, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Sep 25, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Oct 23, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Nov 20, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Dec 18, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
Register
Organizer
BTEP
When
Wed, Jul 31, 2019 - 9:00 am - 12:00 pm
Where
Ft. Detrick, Bldg. 549, 549B
Drop-in anytime during the session. Bring your own computer. We will be covering the following topics: - Unix bootcamp – learn to navigate a Unix file system and move, copy, and edit files - Looking at the quality of your sequence data with FastQC and MultiQC - Trimming sequences with CutAdapt Upcoming dates, Frederick: Aug 28, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Sep 25, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Oct 23, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Nov 20, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Dec 18, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM 2019-07-31 09:00:00 Ft. Detrick,Bldg. 549,549B In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
237 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/604601950b1a42d3a2a6ea1ca6f00c08/playback 2019-08-13 13:00:00 In-Person 0 Detection of somatic, subclonal and mosaic CNVs from sequencing using CNVator -omics tool
238 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/846e911c453c4aec8350f4c29cc1455d/playback 2019-08-20 14:00:00 In-Person 0 AMARETTO for Network Biology and Medicine
882
Description
Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about: • Working at the Unix command line on Biowulf • Quality control analysis and trimming of RNA Seq data • Alignment and visualization of RNA Seq data
Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about: • Working at the Unix command line on Biowulf • Quality control analysis and trimming of RNA Seq data • Alignment and visualization of RNA Seq data
Register
Organizer
BTEP
When
Thu, Aug 22, 2019 - 1:00 pm - 4:00 pm
Where
Suite 3041 Conference Room
Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about: • Working at the Unix command line on Biowulf • Quality control analysis and trimming of RNA Seq data • Alignment and visualization of RNA Seq data 2019-08-22 13:00:00 Suite 3041 Conference Room In-Person Amy Stonelake (BTEP) BTEP 0 BTEP: NIH, Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
239 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/c1bf64260cba42daa17b673022ac1f45/playback 2019-08-22 14:00:00 In-Person 0 OpenCRAVAT: A customizable annotation and prioritization pipeline for genes and variants
240 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/fa9e7d01848143a993c3ea92f9d911dc/playback 2019-08-27 17:00:00 In-Person 0 A Galaxy-Based Multi-Omic Informatics Hub for Cancer Researchers
872
Description
THIS EVENT HAS BEEN CANCELLED
Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Quality control and trimming of RNA seq data
  • Alignment and visualization of RNA seq data
Upcoming dates, Frederick: Sep 25, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Oct 23, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Nov 20, Weds, Ft. ...Read More
THIS EVENT HAS BEEN CANCELLED
Drop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Quality control and trimming of RNA seq data
  • Alignment and visualization of RNA seq data
Upcoming dates, Frederick: Sep 25, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Oct 23, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Nov 20, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Dec 18, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
Register
Organizer
BTEP
When
Wed, Aug 28, 2019 - 9:00 am - 12:00 pm
Where
Ft. Detrick, Bldg. 549, 549B
THIS EVENT HAS BEEN CANCELLEDDrop-in anytime during the session. Bring your own computer. BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Quality control and trimming of RNA seq data Alignment and visualization of RNA seq data Upcoming dates, Frederick: Sep 25, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Oct 23, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Nov 20, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM Dec 18, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM 2019-08-28 09:00:00 Ft. Detrick,Bldg. 549,549B In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Hands-on, drop-in session on Unix/Biowulf and RNA-Seq - CANCELLED
884
Description
Please bring your computer with Partek Genomics Suite to this hands-on workshop. You'll need to submit a request through “NCI at Your Service” to obtain the access to this software. Partek will demonstrate both Microarray and Methylation Data Analysis in Partek Genomics Suite with Pathway ***Microarray Data Analysis in Partek Genomics Suite with Pathway*** Abstract: The class will start with an overview of Partek Genomics Suite with Pathway and ...Read More
Please bring your computer with Partek Genomics Suite to this hands-on workshop. You'll need to submit a request through “NCI at Your Service” to obtain the access to this software. Partek will demonstrate both Microarray and Methylation Data Analysis in Partek Genomics Suite with Pathway ***Microarray Data Analysis in Partek Genomics Suite with Pathway*** Abstract: The class will start with an overview of Partek Genomics Suite with Pathway and followed by a hands-on tutorial session using a sample Gene Expression dataset.  The hands-on session will help attendees gain expertise on the following topics: data import, QA/QC, exploratory data analysis, differential expression analysis, visualizations, and biological interpretation as well as other basic analysis in Partek Genomics Suite with Pathway.
  • Data Import
  • QA/QC
  • Exploratory Data Analysis
  • Differential Expression Analysis
  • Visualization (PCA, hierarchical clustering, dot plot, chromosome view, volcano plot, etc.)
  • Biological Interpretation
***Microarray Methylation Data Analysis in Partek Genomics Suite*** The class will use the Illumina 450K Methylation array data. The workflow can also be used on HumanMethylation27 (27K) and HumanMethylationEPIC (850K) BeadChips. Attendees will learn how to perform QA/QC, detect differential methylation, find genes overlapping CpG loci and biological interpretation in Partek Genomics Suite with Pathway. - Import data from Illumina methylation array in .idat files -  Methylation array-specific normalization -  Perform QA/AC -  Detection of differentially methylated CpG loci -  Creating list of loci of interest
  • Identifying methylation signatures
  • Find overlapping genes
  • Biological interpretation
  • Visualization
    • PCA
    • Dot plot
    • Hierarchical clustering
    • Pathway
If you can not attend in person, WebEx will be provided. Meeting number (access code): 733 186 710 Meeting password: JdyNKF@3 Wednesday, August 28, 2019 8:30 am  |  (UTC-05:00) Eastern Time (US & Canada)  |  2 hrs 30 mins Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 733186710@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 733186710.cbiit@lync.webex.com Need help? Go to http://help.webex.com  
Register
Organizer
BTEP
When
Wed, Aug 28, 2019 - 9:00 am - 11:00 am
Where
Building 37 Room 4041/4107
Please bring your computer with Partek Genomics Suite to this hands-on workshop. You'll need to submit a request through “NCI at Your Service” to obtain the access to this software. Partek will demonstrate both Microarray and Methylation Data Analysis in Partek Genomics Suite with Pathway ***Microarray Data Analysis in Partek Genomics Suite with Pathway*** Abstract: The class will start with an overview of Partek Genomics Suite with Pathway and followed by a hands-on tutorial session using a sample Gene Expression dataset.  The hands-on session will help attendees gain expertise on the following topics: data import, QA/QC, exploratory data analysis, differential expression analysis, visualizations, and biological interpretation as well as other basic analysis in Partek Genomics Suite with Pathway. Data Import QA/QC Exploratory Data Analysis Differential Expression Analysis Visualization (PCA, hierarchical clustering, dot plot, chromosome view, volcano plot, etc.) Biological Interpretation ***Microarray Methylation Data Analysis in Partek Genomics Suite*** The class will use the Illumina 450K Methylation array data. The workflow can also be used on HumanMethylation27 (27K) and HumanMethylationEPIC (850K) BeadChips. Attendees will learn how to perform QA/QC, detect differential methylation, find genes overlapping CpG loci and biological interpretation in Partek Genomics Suite with Pathway. - Import data from Illumina methylation array in .idat files -  Methylation array-specific normalization -  Perform QA/AC -  Detection of differentially methylated CpG loci -  Creating list of loci of interest Identifying methylation signatures Find overlapping genes Biological interpretation Visualization PCA Dot plot Hierarchical clustering Pathway If you can not attend in person, WebEx will be provided. Meeting number (access code): 733 186 710 Meeting password: JdyNKF@3 Wednesday, August 28, 2019 8:30 am  |  (UTC-05:00) Eastern Time (US & Canada)  |  2 hrs 30 mins Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 733186710@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 733186710.cbiit@lync.webex.com Need help? Go to http://help.webex.com   2019-08-28 09:00:00 Building 37 Room 4041/4107 In-Person Xiaowen Wang (Partek) BTEP 0 BTEP: Microarray and Methylation Data Analysis in Partek Genomics Suite with Pathway
885
Description
Here is the handout used for todays Single Cell RNA Seq analysis class: PartekFlowSingleCellRNA-SeqTrainingHandout_August2019 Attention: This class is limited to 28 people due to the size of the room. There is another Single Cell Rna-Seq class on Oct 23 if you can't make this one. Hands On Training on Single Cell RNA-Seq Data Analysis in Partek ...Read More
Here is the handout used for todays Single Cell RNA Seq analysis class: PartekFlowSingleCellRNA-SeqTrainingHandout_August2019 Attention: This class is limited to 28 people due to the size of the room. There is another Single Cell Rna-Seq class on Oct 23 if you can't make this one. Hands On Training on Single Cell RNA-Seq Data Analysis in Partek Flow Please bring your computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Abstract: Students will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow: - Import data - Filter cells using interactive QA/QC charts - Filter and normalize Single Cell RNA-Seq data - Visualize cell populations using the interactive 3D t-SNE plot - Overlay gene expression and pathway signatures on the 3D t-SNE plot - Select and classify cells on the 3D t-SNE plot - Detect differentially expressed genes - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualize cell-level results using heat maps, volcano plots, and violin plots   If you can't attend in person, WebEx will be provided. Meeting number (access code): 731 584 771 Meeting password: fR3RPGP? Wednesday, August 28, 2019 1:00 pm  |  (UTC-05:00) Eastern Time (US & Canada)  |  3 hrs Start Meeting Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 731584771@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 731584771.cbiit@lync.webex.com Need help? Go to http://help.webex.com
Register
Organizer
BTEP
When
Wed, Aug 28, 2019 - 1:00 pm - 5:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
Here is the handout used for todays Single Cell RNA Seq analysis class: PartekFlowSingleCellRNA-SeqTrainingHandout_August2019 Attention: This class is limited to 28 people due to the size of the room. There is another Single Cell Rna-Seq class on Oct 23 if you can't make this one. Hands On Training on Single Cell RNA-Seq Data Analysis in Partek Flow Please bring your computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Abstract: Students will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow: - Import data - Filter cells using interactive QA/QC charts - Filter and normalize Single Cell RNA-Seq data - Visualize cell populations using the interactive 3D t-SNE plot - Overlay gene expression and pathway signatures on the 3D t-SNE plot - Select and classify cells on the 3D t-SNE plot - Detect differentially expressed genes - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualize cell-level results using heat maps, volcano plots, and violin plots   If you can't attend in person, WebEx will be provided. Meeting number (access code): 731 584 771 Meeting password: fR3RPGP? Wednesday, August 28, 2019 1:00 pm  |  (UTC-05:00) Eastern Time (US & Canada)  |  3 hrs Start Meeting Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 731584771@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 731584771.cbiit@lync.webex.com Need help? Go to http://help.webex.com 2019-08-28 13:00:00 Bldg 10 FAES room 4 (B1C205) In-Person Xiaowen Wang (Partek) BTEP 0 BTEP: Single Cell RNA Seq Analysis with Partek Flow
241 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/361655cd97c049da80df811be91adc04/playback 2019-08-29 14:00:00 In-Person 0 OncoMX: an integrated cancer mutation and expression resource for exploring cancer biomarkers
242 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/aa908acc7fd64f3fac4579bc3b69725d/playback 2019-09-10 12:00:00 In-Person 0 Single Cell Genome Viewer: Computational tools for sparsely sequenced single-cell genomes
243 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/320b6689787943998991eb65d61086ad/playback 2019-09-17 12:00:00 In-Person 0 Introduction to Gene-gene Association Inference based on biomedical Literature
867
Description
Drop-in anytime during the session. Bring your own computer. If you have a PC, please download and install PuTTY (putty.org) and WinSCP (winscp.net). If you have a Mac please download and install Fugu. BTEP personnel will be available to answer questions about: • Working at the Unix command line on Biowulf • Quality control analysis and trimming of RNA Seq data • Alignment and visualization of RNA Seq data  
Drop-in anytime during the session. Bring your own computer. If you have a PC, please download and install PuTTY (putty.org) and WinSCP (winscp.net). If you have a Mac please download and install Fugu. BTEP personnel will be available to answer questions about: • Working at the Unix command line on Biowulf • Quality control analysis and trimming of RNA Seq data • Alignment and visualization of RNA Seq data  
Register
Organizer
BTEP
When
Thu, Sep 19, 2019 - 12:00 pm - 3:00 pm
Where
NIH Bethesda B37 Rm 4041/4107
Drop-in anytime during the session. Bring your own computer. If you have a PC, please download and install PuTTY (putty.org) and WinSCP (winscp.net). If you have a Mac please download and install Fugu. BTEP personnel will be available to answer questions about: • Working at the Unix command line on Biowulf • Quality control analysis and trimming of RNA Seq data • Alignment and visualization of RNA Seq data   2019-09-19 12:00:00 NIH Bethesda B37 Rm 4041/4107 In-Person Amy Stonelake (BTEP) BTEP 0 BTEP: NIH, Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
244 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/1985cbcbb8ad4c07ac58ea7e3a4d7927/playback 2019-09-24 14:30:00 In-Person 0 Revitalize your slides with ScienceSlides
873
Description
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    Read More
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html At the session, BTEP personnel will be available to answer questions about: • Working at the Unix command line on Biowulf • Quality control analysis and trimming of RNA Seq data • Alignment and visualization of RNA Seq data  
Register
Organizer
BTEP
When
Wed, Sep 25, 2019 - 9:00 am - 12:00 pm
Where
Ft. Detrick, Bldg. 549, 549B
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files. For Windows PC, suggested tool is WinScp. 2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html At the session, BTEP personnel will be available to answer questions about: • Working at the Unix command line on Biowulf • Quality control analysis and trimming of RNA Seq data • Alignment and visualization of RNA Seq data   2019-09-25 09:00:00 Ft. Detrick,Bldg. 549,549B In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
879
Description
Qlucore Omics Explorer is an interactive analysis and visualization tool that helps the user to find groups, structures, variable networks and discriminating variables. You can use your own data and analyze public data. Omics Explorer supports 3D PCA plots, t-SNE plots, heat maps with hierarchical clustering, scatter plots, volcano plots, box plots etc. A number of statistical methods are included, like t-test, F-test (ANOVA) and different regressions tests. Plots are dynamically updated as ...Read More
Qlucore Omics Explorer is an interactive analysis and visualization tool that helps the user to find groups, structures, variable networks and discriminating variables. You can use your own data and analyze public data. Omics Explorer supports 3D PCA plots, t-SNE plots, heat maps with hierarchical clustering, scatter plots, volcano plots, box plots etc. A number of statistical methods are included, like t-test, F-test (ANOVA) and different regressions tests. Plots are dynamically updated as filters change and analysis is done, which allows a very interactive exploration to test out new hypothesizes. Omics Explorer has an interface which makes it possible to use statistical functions created in "R" from inside Omics Explorer, so that you can use external statistical methods implemented in “R”. With machine learning algorithms (kNN, SVM and Random Trees) classification of samples in new data sets is simplified. The product has an inbuilt Gene Set Enrichment Analysis (GSEA) workbench for pathway analysis, a direct interface to GEO and an inbuilt Gene Ontology browser. The product has an import wizard to simplify data import. Aligned BAM files (RNA-seq) and microarray files (.cel files) can be directly imported, normalized and log transformed through import pipelines, and GEO soft files can be directly imported. Through Qlucore Templates, based on “Python”, you can create analysis workflows and also customize integrations, like the TCGA mRNA dataset download Template that comes preinstalled. Qlucore NGS module is an optional add-on module to Qlucore Omics Explorer. The main components of the NGS module are an interactive and fast Genome Browser for many samples and many tracks per sample, a genome filter control component, a Project Manager for project set-up and an inbuilt Variant Caller for short indels and variants. Utilizing the existing functionality in Qlucore Omics Explorer for expression data and combining it with the functionality in Qlucore NGS Browser enables significantly increased analysis options. If you are unable to attend in person, WebEx will be available. Amy Stonelake invites you to join this Webex meeting. Meeting number (access code): 739 669 544 Meeting password: Hkfgh3C@ Thursday, September 26, 2019 12:00 pm  |  (UTC-05:00) Eastern Time (US & Canada)  |  3 hrs Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 739669544@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 739669544.cbiit@lync.webex.com  
Register
Organizer
BTEP
When
Thu, Sep 26, 2019 - 12:00 pm - 3:00 pm
Where
NIH, Bldg 37, Rm 2041/2107
Qlucore Omics Explorer is an interactive analysis and visualization tool that helps the user to find groups, structures, variable networks and discriminating variables. You can use your own data and analyze public data. Omics Explorer supports 3D PCA plots, t-SNE plots, heat maps with hierarchical clustering, scatter plots, volcano plots, box plots etc. A number of statistical methods are included, like t-test, F-test (ANOVA) and different regressions tests. Plots are dynamically updated as filters change and analysis is done, which allows a very interactive exploration to test out new hypothesizes. Omics Explorer has an interface which makes it possible to use statistical functions created in "R" from inside Omics Explorer, so that you can use external statistical methods implemented in “R”. With machine learning algorithms (kNN, SVM and Random Trees) classification of samples in new data sets is simplified. The product has an inbuilt Gene Set Enrichment Analysis (GSEA) workbench for pathway analysis, a direct interface to GEO and an inbuilt Gene Ontology browser. The product has an import wizard to simplify data import. Aligned BAM files (RNA-seq) and microarray files (.cel files) can be directly imported, normalized and log transformed through import pipelines, and GEO soft files can be directly imported. Through Qlucore Templates, based on “Python”, you can create analysis workflows and also customize integrations, like the TCGA mRNA dataset download Template that comes preinstalled. Qlucore NGS module is an optional add-on module to Qlucore Omics Explorer. The main components of the NGS module are an interactive and fast Genome Browser for many samples and many tracks per sample, a genome filter control component, a Project Manager for project set-up and an inbuilt Variant Caller for short indels and variants. Utilizing the existing functionality in Qlucore Omics Explorer for expression data and combining it with the functionality in Qlucore NGS Browser enables significantly increased analysis options. If you are unable to attend in person, WebEx will be available. Amy Stonelake invites you to join this Webex meeting. Meeting number (access code): 739 669 544 Meeting password: Hkfgh3C@ Thursday, September 26, 2019 12:00 pm  |  (UTC-05:00) Eastern Time (US & Canada)  |  3 hrs Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 739669544@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 739669544.cbiit@lync.webex.com   2019-09-26 12:00:00 NIH,Bldg 37,Rm 2041/2107 In-Person Yana Stackpole (Qlucore) BTEP 0 BTEP: Qlucore Omics Explorer, Fast and Easy Exploration of NGS Data
888
Description
Overview of single cell technologies, analysis workflows, scRNA-seq preprocessing and QC 10:00 – 10:30 AM Introduction to single cell technologies and applications (Mike Kelly) 10:30 – 11:00 AM Overview of single cell transcriptomic analysis workflow and pipelines (Yongmei Zhao) 11:05 – 12:00 PM scRNA-seq preprocessing and quality control (Vicky Chen, Nathan Wong) 1:00 – 2:00 PM Clustering analysis, dimensionality reduction, marker gene identification, and visualization (Cihan Oguz) 2:00 - 3:00 PM Single cell RNA-seq cell type classification and annotation (Keyur Talsania) If unable to join in person, WebEx will ...Read More
Overview of single cell technologies, analysis workflows, scRNA-seq preprocessing and QC 10:00 – 10:30 AM Introduction to single cell technologies and applications (Mike Kelly) 10:30 – 11:00 AM Overview of single cell transcriptomic analysis workflow and pipelines (Yongmei Zhao) 11:05 – 12:00 PM scRNA-seq preprocessing and quality control (Vicky Chen, Nathan Wong) 1:00 – 2:00 PM Clustering analysis, dimensionality reduction, marker gene identification, and visualization (Cihan Oguz) 2:00 - 3:00 PM Single cell RNA-seq cell type classification and annotation (Keyur Talsania) If unable to join in person, WebEx will be provided. Meeting number (access code): 737 705 057 Meeting password: rPMv3Gf@ Thursday, October 3, 2019 10:00 am  |  (UTC-05:00) Eastern Time (US & Canada)  |  5 hrs Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 737705057@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 737705057.cbiit@lync.webex.com The link to the WebEx recording of this lecture is here: https://cbiit.webex.com/webappng/sites/cbiit/recording/play/c307ef564e4246bba1a7316fed14fea3
Register
Organizer
BTEP
When
Thu, Oct 03, 2019 - 10:00 am - 3:00 pm
Where
Building 37 Room 4041/4107
Overview of single cell technologies, analysis workflows, scRNA-seq preprocessing and QC 10:00 – 10:30 AM Introduction to single cell technologies and applications (Mike Kelly) 10:30 – 11:00 AM Overview of single cell transcriptomic analysis workflow and pipelines (Yongmei Zhao) 11:05 – 12:00 PM scRNA-seq preprocessing and quality control (Vicky Chen, Nathan Wong) 1:00 – 2:00 PM Clustering analysis, dimensionality reduction, marker gene identification, and visualization (Cihan Oguz) 2:00 - 3:00 PM Single cell RNA-seq cell type classification and annotation (Keyur Talsania) If unable to join in person, WebEx will be provided. Meeting number (access code): 737 705 057 Meeting password: rPMv3Gf@ Thursday, October 3, 2019 10:00 am  |  (UTC-05:00) Eastern Time (US & Canada)  |  5 hrs Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 737705057@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 737705057.cbiit@lync.webex.com The link to the WebEx recording of this lecture is here: https://cbiit.webex.com/webappng/sites/cbiit/recording/play/c307ef564e4246bba1a7316fed14fea3 2019-10-03 10:00:00 Building 37 Room 4041/4107 In-Person Yongmei Zhao (CCR-SF IFX Group),Michael Kelly (SCAF),Cihan Oguz (NCBR),Vicky Chen (NCBR),Nathan Wong (CCBR) BTEP 0 BTEP: Single Cell RNA Seq Analysis Workshop, Part 1
245 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/8886c763adae49208aafbca48357d02a/playback 2019-10-08 12:00:00 In-Person 0 Introduction to Gene Set Enrichment Analysis (GSEA) and Molecular Signatures Database
889
Description
Multiple sample analysis, data integration across multiple experiments, and technologies 10:00 – 11:00 AM scRNA-seq batch effect correction, differential analysis,  gene set enrichment and network analysis (Abdalla Abdelmaksoud, Vicky Chen) 11:05 – 12:00 PM Trajectory analysis (Abdalla Abdelmaksoud) 1:00 –   2:30 PM Single cell integrative analysis from multiple technologies (CITE-seq, cell hashing, VDJ analysis, scATAC-seq) (Nathan Wong, Cihan Oguz, and Keyur Talsania) 2:30 - 3:00 PM Panel Discussion (Q & A) If unable to join in person, WebEx will be provided. Meeting number (access ...Read More
Multiple sample analysis, data integration across multiple experiments, and technologies 10:00 – 11:00 AM scRNA-seq batch effect correction, differential analysis,  gene set enrichment and network analysis (Abdalla Abdelmaksoud, Vicky Chen) 11:05 – 12:00 PM Trajectory analysis (Abdalla Abdelmaksoud) 1:00 –   2:30 PM Single cell integrative analysis from multiple technologies (CITE-seq, cell hashing, VDJ analysis, scATAC-seq) (Nathan Wong, Cihan Oguz, and Keyur Talsania) 2:30 - 3:00 PM Panel Discussion (Q & A) If unable to join in person, WebEx will be provided. Meeting number (access code): 732 159 036 Meeting password: 44iZDNP* Thursday, October 10, 2019 10:00 am  |  (UTC-05:00) Eastern Time (US & Canada)  |  5 hrs Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 732159036@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 732159036.cbiit@lync.webex.com The link to the WebEx recording of this event is here: https://cbiit.webex.com/webappng/sites/cbiit/recording/play/7321cf28c97346b8a0b6dc258a0d40a8  
Register
Organizer
BTEP
When
Thu, Oct 10, 2019 - 10:00 am - 3:00 pm
Where
Building 37 Room 4041/4107
Multiple sample analysis, data integration across multiple experiments, and technologies 10:00 – 11:00 AM scRNA-seq batch effect correction, differential analysis,  gene set enrichment and network analysis (Abdalla Abdelmaksoud, Vicky Chen) 11:05 – 12:00 PM Trajectory analysis (Abdalla Abdelmaksoud) 1:00 –   2:30 PM Single cell integrative analysis from multiple technologies (CITE-seq, cell hashing, VDJ analysis, scATAC-seq) (Nathan Wong, Cihan Oguz, and Keyur Talsania) 2:30 - 3:00 PM Panel Discussion (Q & A) If unable to join in person, WebEx will be provided. Meeting number (access code): 732 159 036 Meeting password: 44iZDNP* Thursday, October 10, 2019 10:00 am  |  (UTC-05:00) Eastern Time (US & Canada)  |  5 hrs Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 732159036@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 732159036.cbiit@lync.webex.com The link to the WebEx recording of this event is here: https://cbiit.webex.com/webappng/sites/cbiit/recording/play/7321cf28c97346b8a0b6dc258a0d40a8   2019-10-10 10:00:00 Building 37 Room 4041/4107 In-Person Cihan Oguz (NCBR),Vicky Chen (NCBR),Nathan Wong (CCBR), BTEP 0 BTEP: Single Cell RNA Seq Analysis Workshop, Part II
880
Description
This lecture will briefly review the steps involved in data analysis and how study design, hypothesis, and type of data and their distributions contribute to the choice of statistical tests. Statistical tests are used to determine the presence and strength of a relationship between independent and outcome variables. The basic concepts around the use and interpretation of the following statistical tests will be covered: chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, ...Read More
This lecture will briefly review the steps involved in data analysis and how study design, hypothesis, and type of data and their distributions contribute to the choice of statistical tests. Statistical tests are used to determine the presence and strength of a relationship between independent and outcome variables. The basic concepts around the use and interpretation of the following statistical tests will be covered: chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and non-parametric tests. This class will be taught by Ninet Sinaii, PhD, MPH, NIH Clinical Center's Biostatistics and Clinical Epidemiology Service. This meeting will also be available via WebEx. Meeting number (access code): 736 785 528 Meeting password: 5MppT5b@ Friday, October 11, 2019 9:00 am  |  (UTC-05:00) Eastern Time (US & Canada)  |  3 hrs Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 736785528@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 736785528.cbiit@lync.webex.com The link to the WebEx recording of this lecture is: https://cbiit.webex.com/webappng/sites/cbiit/recording/play/a4e97d58e3b043298767a36a88a753c2
Register
Organizer
BTEP
When
Fri, Oct 11, 2019 - 9:00 am - 12:00 pm
Where
Bldg 37, Rm 2041/2107
This lecture will briefly review the steps involved in data analysis and how study design, hypothesis, and type of data and their distributions contribute to the choice of statistical tests. Statistical tests are used to determine the presence and strength of a relationship between independent and outcome variables. The basic concepts around the use and interpretation of the following statistical tests will be covered: chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and non-parametric tests. This class will be taught by Ninet Sinaii, PhD, MPH, NIH Clinical Center's Biostatistics and Clinical Epidemiology Service. This meeting will also be available via WebEx. Meeting number (access code): 736 785 528 Meeting password: 5MppT5b@ Friday, October 11, 2019 9:00 am  |  (UTC-05:00) Eastern Time (US & Canada)  |  3 hrs Join Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 736785528@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 736785528.cbiit@lync.webex.com The link to the WebEx recording of this lecture is: https://cbiit.webex.com/webappng/sites/cbiit/recording/play/a4e97d58e3b043298767a36a88a753c2 2019-10-11 09:00:00 Bldg 37,Rm 2041/2107 In-Person Ninet Sinaii Ph.D. MPH (Biostatistics and Clinical Epidemiology Branch NIH Clinical Center) BTEP 0 BTEP: Overview of Common Statistical Tests
246 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/a6e14c8eafae40d9936f9d13b3e4cf97/playback 2019-10-15 12:00:00 In-Person 0 Introduction to Integrative Genomics Robust iDentification of cancer subgroups (InGRiD)
868
Description
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    Read More
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
 
Register
Organizer
BTEP
When
Thu, Oct 17, 2019 - 12:00 pm - 3:00 pm
Where
NIH Bethesda B37 Rm 4041/4107
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files. For Windows PC, suggested tool is WinScp. 2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Finding, understanding and moving your data Tools available to CCR personnel for RNA-seq data analysis   2019-10-17 12:00:00 NIH Bethesda B37 Rm 4041/4107 In-Person Amy Stonelake (BTEP) BTEP 0 BTEP: NIH, Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
886
Description
Please note class will start at 10 AM and end at 12 noon. Thank you! Attention: This class is limited to 28 people due to the size of the room. Please add your name to the waiting list if you would like another training scheduled. Hands On Training on Bulk RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to ...Read More
Please note class will start at 10 AM and end at 12 noon. Thank you! Attention: This class is limited to 28 people due to the size of the room. Please add your name to the waiting list if you would like another training scheduled. Hands On Training on Bulk RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Abstract: Students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for RNA-Seq analysis: - Import data from .fastq files - Perform QA/QC (Pre-alignment QA/QC, Post-alignment QA/QC) - Trim bases - Align reads to reference genome - Quantify gene/transcript abundance - Normalize gene counts - Detect differentially expressed genes - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualization:
      • Heat maps
      • Volcano plots
      • PCA scatterplot
      • Dot plots
      • Hierarchical clustering
      • Chromosome view
      • And more
If you are unable to attend in person, WebEx will be provided. Meeting link: https://cbiit.webex.com/cbiit/j.php?MTID=m4ddd63165233af6473f4ce950642aecc Meeting number: 738 100 303 Password: AcCxyy$5 Host key: 544130 More ways to join Join by video system Dial 738100303@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in number (US/Canada) Access code: 738 100 303
Register
Organizer
BTEP
When
Wed, Oct 23, 2019 - 10:00 am - 12:00 pm
Where
Bldg 10 FAES room 4 (B1C205)
Please note class will start at 10 AM and end at 12 noon. Thank you! Attention: This class is limited to 28 people due to the size of the room. Please add your name to the waiting list if you would like another training scheduled. Hands On Training on Bulk RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Abstract: Students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for RNA-Seq analysis: - Import data from .fastq files - Perform QA/QC (Pre-alignment QA/QC, Post-alignment QA/QC) - Trim bases - Align reads to reference genome - Quantify gene/transcript abundance - Normalize gene counts - Detect differentially expressed genes - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualization: Heat maps Volcano plots PCA scatterplot Dot plots Hierarchical clustering Chromosome view And more If you are unable to attend in person, WebEx will be provided. Meeting link: https://cbiit.webex.com/cbiit/j.php?MTID=m4ddd63165233af6473f4ce950642aecc Meeting number: 738 100 303 Password: AcCxyy$5 Host key: 544130 More ways to join Join by video system Dial 738100303@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in number (US/Canada) Access code: 738 100 303 2019-10-23 10:00:00 Bldg 10 FAES room 4 (B1C205) In-Person Xiaowen Wang (Partek) BTEP 0 BTEP: Bulk RNA Seq Analysis with Partek Flow
887
Description
Please note class will start at 1:30 PM and end at 3:30 PM.  Hands On Training on Single Cell RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Abstract: Students will learn how to identify cell populations and detect differentially expressed genes in a simple Single ...Read More
Please note class will start at 1:30 PM and end at 3:30 PM.  Hands On Training on Single Cell RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Abstract: Students will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow: - Import data - Filter cells using interactive QA/QC charts - Filter and normalize Single Cell RNA-Seq data - Visualize cell populations using the interactive 3D t-SNE plot - Overlay gene expression and pathway signatures on the 3D t-SNE plot - Select and classify cells on the 3D t-SNE plot - Detect differentially expressed genes - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualize cell-level results using heat maps, volcano plots, and violin plots If you are unable to attend in person WebEx will be available: Meeting link: https://cbiit.webex.com/cbiit/j.php?MTID=m93cd8514270a1242d230b21a8379fb2f Meeting number: 737 711 601 Password: Cxjjjq@3 Host key: 696480 More ways to join Join by video system Dial 737711601@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in number (US/Canada) Access code: 737 711 601
Register
Organizer
BTEP
When
Wed, Oct 23, 2019 - 1:30 pm - 3:30 pm
Where
Bldg 10 FAES room 4 (B1C205)
Please note class will start at 1:30 PM and end at 3:30 PM.  Hands On Training on Single Cell RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Abstract: Students will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow: - Import data - Filter cells using interactive QA/QC charts - Filter and normalize Single Cell RNA-Seq data - Visualize cell populations using the interactive 3D t-SNE plot - Overlay gene expression and pathway signatures on the 3D t-SNE plot - Select and classify cells on the 3D t-SNE plot - Detect differentially expressed genes - Filter a gene list - Identify enriched KEGG pathway and/or GO terms - Visualize cell-level results using heat maps, volcano plots, and violin plots If you are unable to attend in person WebEx will be available: Meeting link: https://cbiit.webex.com/cbiit/j.php?MTID=m93cd8514270a1242d230b21a8379fb2f Meeting number: 737 711 601 Password: Cxjjjq@3 Host key: 696480 More ways to join Join by video system Dial 737711601@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in number (US/Canada) Access code: 737 711 601 2019-10-23 13:30:00 Bldg 10 FAES room 4 (B1C205) In-Person Xiaowen Wang (Partek) BTEP 0 BTEP: Single Cell RNA Seq Analysis with Partek Flow
878
Description
** Registration is not necessary for members of the public**NIH employees please register**Thank you** Re-assessing the Human Gene Catalog and the Human Genome: How much are we missing?  Steven L. Salzberg, Ph.D. Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science, and Biostatistics Director, Center for Computational Biology,  Johns Hopkins University http://salzberg-lab.org  How many genes do we have? The Human Genome Project was launched ...Read More
** Registration is not necessary for members of the public**NIH employees please register**Thank you** Re-assessing the Human Gene Catalog and the Human Genome: How much are we missing?  Steven L. Salzberg, Ph.D. Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science, and Biostatistics Director, Center for Computational Biology,  Johns Hopkins University http://salzberg-lab.org  How many genes do we have? The Human Genome Project was launched with the promise of revealing all of our genes, the “code” that would help explain our biology. The publication of the human genome in 2001 provided only a very rough answer to this question. For more than a decade following, the number of protein-coding genes steadily shrank, but the introduction of RNA sequencing revealed a vast new world of splice variants and RNA genes. In this talk, I will review where we’ve been and where we are today, and I will describe our use of an unprecedentedly large RNA sequencing resource to create a comprehensive new human gene catalog, containing thousands of novel genes and gene variants. I'll then turn to the genome itself, and discuss how we've found, through the assembly of 910 individuals of African descent, that the human reference genome is missing nearly 300 million bases that are present in some members of the population. This talk describes joint work with Mihaela Pertea, Rachel Sherman, Alaina Shumate, Ales Varabyou, and Geo Pertea. This talk will be video cast live and archived at https://videocast.nih.gov/summary.asp?live=34578
Register
Organizer
BTEP
When
Thu, Oct 24, 2019 - 1:00 pm - 3:00 pm
Where
Lipsett Auditorium
** Registration is not necessary for members of the public**NIH employees please register**Thank you** Re-assessing the Human Gene Catalog and the Human Genome: How much are we missing?  Steven L. Salzberg, Ph.D. Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science, and Biostatistics Director, Center for Computational Biology,  Johns Hopkins University http://salzberg-lab.org  How many genes do we have? The Human Genome Project was launched with the promise of revealing all of our genes, the “code” that would help explain our biology. The publication of the human genome in 2001 provided only a very rough answer to this question. For more than a decade following, the number of protein-coding genes steadily shrank, but the introduction of RNA sequencing revealed a vast new world of splice variants and RNA genes. In this talk, I will review where we’ve been and where we are today, and I will describe our use of an unprecedentedly large RNA sequencing resource to create a comprehensive new human gene catalog, containing thousands of novel genes and gene variants. I'll then turn to the genome itself, and discuss how we've found, through the assembly of 910 individuals of African descent, that the human reference genome is missing nearly 300 million bases that are present in some members of the population. This talk describes joint work with Mihaela Pertea, Rachel Sherman, Alaina Shumate, Ales Varabyou, and Geo Pertea. This talk will be video cast live and archived at https://videocast.nih.gov/summary.asp?live=34578 2019-10-24 13:00:00 Lipsett Auditorium In-Person Steven Salzberg (JHU) BTEP 0 BTEP: Steven Salzberg (JHU), Distinguished Speakers Seminar Series
182
Description
https://videocast.nih.gov/summary.asp?live=34578 NIH Users please connect via VPN to view this video Presenter: Steven Salzberg, Ph.D. Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science and Biostatistics and Director of the Center for Computational Biology, Johns Hopkins University
https://videocast.nih.gov/summary.asp?live=34578 NIH Users please connect via VPN to view this video Presenter: Steven Salzberg, Ph.D. Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science and Biostatistics and Director of the Center for Computational Biology, Johns Hopkins University
Details
When
Thu, Oct 24, 2019 - 1:00 pm - 2:00 pm
Where
Online
https://videocast.nih.gov/summary.asp?live=34578 NIH Users please connect via VPN to view this video Presenter: Steven Salzberg, Ph.D. Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science and Biostatistics and Director of the Center for Computational Biology, Johns Hopkins University 2019-10-24 13:00:00 Online 0 Re-assessing the Human Gene Catalog and Human Genome: How Much Are we Missing?
247 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/755c6514ffb7499f9ca8db00a108a3a2/playback 2019-10-29 12:00:00 In-Person 0 Introduction to the Cancer Proteome Atlas
874
Description
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    Read More
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
 
Register
Organizer
BTEP
When
Wed, Oct 30, 2019 - 9:00 am - 12:00 pm
Where
Ft. Detrick, Bldg. 549, 549B
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files. For Windows PC, suggested tool is WinScp. 2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Finding, understanding and moving your data Tools available to CCR personnel for RNA-seq data analysis   2019-10-30 09:00:00 Ft. Detrick,Bldg. 549,549B In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Hands-on, drop-in session on Unix/Biowulf and RNA-seq
891
Description
This is a hands-on demo, please bring your laptop or let us know if you need to borrow one.  The field of genomics has matured to a stage where organizations are sequencing DNA at population scale. However, taking raw DNAseq data and transforming it into a format suitable for analysis has become the new bottleneck to genomic discovery. Typically, teams are gluing together a series of bioinformatics tools with custom scripts and processing data on ...Read More
This is a hands-on demo, please bring your laptop or let us know if you need to borrow one.  The field of genomics has matured to a stage where organizations are sequencing DNA at population scale. However, taking raw DNAseq data and transforming it into a format suitable for analysis has become the new bottleneck to genomic discovery. Typically, teams are gluing together a series of bioinformatics tools with custom scripts and processing data on single node machines, one sample at a time. Bioinformatics scientists are spending more time building and maintaining pipelines than modeling data. To ease the burden of analyzing population scale genomic data, a number of open-source bioinformatics tools have moved to use Apache Spark™, such as the GATK4, Hail, and ADAM, but mastering these tools is no easy task. In this workshop, we’ll walkthrough how the Databricks Unified Analytics Platform for Genomics simplifies the end-to-end process of turning raw sequencing data into actionable insights at scale. Introduced by the original creators of Apache Spark, this platform makes it simple to deploy Spark-based bioinformatics tools on cloud computing, and rapidly accelerates common genomic analyses. Join this half day technical workshop to learn how to
  • Call variants, both in a single sample and across multiple samples, using our accelerated GATK4 pipelines
  • Use Spark SQL to characterize the association of variants in a population with phenotypes
  • Use machine learning to model genome-wide disease risk across multiple variants associated with a phenotype of interest
Key technologies employed: GATK4/Variant calling, Genotype-phenotype association tests, population scale risk-modeling via ML, ML model training/deployment AGENDA AT A GLANCE 11:00-11:45    Introduction and Opening Remarks 12:30-1:30       Workshop #1: Accelerating Variant Calls with Apache Spark 1:30-2:30         Workshop #2: Characterizing Genetic Variants with Spark SQL 2:30-3:30         Workshop #3: Disease Risk Scoring with Machine Learning   If you are unable to attend in person, WebEx will be provided: Event address for attendees: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=eeb4e34e8558861862b5a716bb88c6c73 WebEx recording available at: https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/92d05e03bf1a4882b92ec73eab7a85a5    
Register
Organizer
BTEP
When
Mon, Nov 04, 2019 - 11:00 am - 3:30 pm
Where
Bldg 60, Rathskeller
This is a hands-on demo, please bring your laptop or let us know if you need to borrow one.  The field of genomics has matured to a stage where organizations are sequencing DNA at population scale. However, taking raw DNAseq data and transforming it into a format suitable for analysis has become the new bottleneck to genomic discovery. Typically, teams are gluing together a series of bioinformatics tools with custom scripts and processing data on single node machines, one sample at a time. Bioinformatics scientists are spending more time building and maintaining pipelines than modeling data. To ease the burden of analyzing population scale genomic data, a number of open-source bioinformatics tools have moved to use Apache Spark™, such as the GATK4, Hail, and ADAM, but mastering these tools is no easy task. In this workshop, we’ll walkthrough how the Databricks Unified Analytics Platform for Genomics simplifies the end-to-end process of turning raw sequencing data into actionable insights at scale. Introduced by the original creators of Apache Spark, this platform makes it simple to deploy Spark-based bioinformatics tools on cloud computing, and rapidly accelerates common genomic analyses. Join this half day technical workshop to learn how to Call variants, both in a single sample and across multiple samples, using our accelerated GATK4 pipelines Use Spark SQL to characterize the association of variants in a population with phenotypes Use machine learning to model genome-wide disease risk across multiple variants associated with a phenotype of interest Key technologies employed: GATK4/Variant calling, Genotype-phenotype association tests, population scale risk-modeling via ML, ML model training/deployment AGENDA AT A GLANCE 11:00-11:45    Introduction and Opening Remarks 12:30-1:30       Workshop #1: Accelerating Variant Calls with Apache Spark 1:30-2:30         Workshop #2: Characterizing Genetic Variants with Spark SQL 2:30-3:30         Workshop #3: Disease Risk Scoring with Machine Learning   If you are unable to attend in person, WebEx will be provided: Event address for attendees: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=eeb4e34e8558861862b5a716bb88c6c73 WebEx recording available at: https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/92d05e03bf1a4882b92ec73eab7a85a5     2019-11-04 11:00:00 Bldg 60,Rathskeller In-Person James Stratton (DataBricks),Frank Nothaft (DataBricks) BTEP 0 BTEP: Data Science Using Apache Spark for Biomedical Applications
869
Description
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    Read More
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
   
Register
Organizer
BTEP
When
Tue, Nov 05, 2019 - 11:00 am - 2:00 pm
Where
Rm 2041/2107, NIH Bldg 37
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files. For Windows PC, suggested tool is WinScp. 2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Finding, understanding and moving your data Tools available to CCR personnel for RNA-seq data analysis     2019-11-05 11:00:00 Rm 2041/2107,NIH Bldg 37 In-Person Amy Stonelake (BTEP) BTEP 0 BTEP: NIH, Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
248
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/d5a83a983b924f3dabe053dce439f754/playback 2019-11-05 12:00:00 In-Person 0 Introduction to UCSC Xena
883
Description
Methods for Characterizing the Activity of Mutational Processes in Cancer The cancer sequencing efforts of the past decade have revealed signatures of the mutational processes shaping cancer genomes. These mutational signatures provide a window into a tumor’s functional state and history, and thus provide new opportunities for identifying the mutations driving an individual’s cancer and for personalized medicine. While researchers have now collected >30 validated mutational signatures, challenges remain for understanding ...Read More
Methods for Characterizing the Activity of Mutational Processes in Cancer The cancer sequencing efforts of the past decade have revealed signatures of the mutational processes shaping cancer genomes. These mutational signatures provide a window into a tumor’s functional state and history, and thus provide new opportunities for identifying the mutations driving an individual’s cancer and for personalized medicine. While researchers have now collected >30 validated mutational signatures, challenges remain for understanding the patterns of mutational signature activity. One such challenge is in characterizing signature etiology: many signatures have unknown etiology, while some similar signatures have different etiologies. In this talk, we will present two probabilistic methods that begin to address these challenges. Inspired by research from natural language processing, the first method, TCSM, models mutational signature activity per tumor conditioned on observed metadata about the patient. We will show that TCSM outperforms standard methods at inferring mutational signature activity and for inferring clinically relevant DNA damage repair deficiencies in breast cancer. Next, we will present SigMa, the first model of mutational signature activity to account for sequence dependencies among clustered mutations. We use these inferred dependencies and associations with other genomic factors to reveal new insights into signature etiology. Finally, we will conclude by presenting ongoing work on ExploSig, a family of tools to enable biologists and data scientists to explore mutational signatures datasets in the browser and in interactive notebooks. If the video fails to work you can download the video as a zip file from the Class Materials 2 link given below [video width="1920" height="1080" mp4="https://btep.ccr.cancer.gov/wp-content/uploads/Max-Leiserson-BTEP.mp4"][/video]
Register
Organizer
BTEP
When
Tue, Nov 19, 2019 - 11:00 am - 12:00 pm
Where
Rm 2041/2107, NIH Bldg 37
Methods for Characterizing the Activity of Mutational Processes in Cancer The cancer sequencing efforts of the past decade have revealed signatures of the mutational processes shaping cancer genomes. These mutational signatures provide a window into a tumor’s functional state and history, and thus provide new opportunities for identifying the mutations driving an individual’s cancer and for personalized medicine. While researchers have now collected >30 validated mutational signatures, challenges remain for understanding the patterns of mutational signature activity. One such challenge is in characterizing signature etiology: many signatures have unknown etiology, while some similar signatures have different etiologies. In this talk, we will present two probabilistic methods that begin to address these challenges. Inspired by research from natural language processing, the first method, TCSM, models mutational signature activity per tumor conditioned on observed metadata about the patient. We will show that TCSM outperforms standard methods at inferring mutational signature activity and for inferring clinically relevant DNA damage repair deficiencies in breast cancer. Next, we will present SigMa, the first model of mutational signature activity to account for sequence dependencies among clustered mutations. We use these inferred dependencies and associations with other genomic factors to reveal new insights into signature etiology. Finally, we will conclude by presenting ongoing work on ExploSig, a family of tools to enable biologists and data scientists to explore mutational signatures datasets in the browser and in interactive notebooks. If the video fails to work you can download the video as a zip file from the Class Materials 2 link given below [video width="1920" height="1080" mp4="https://btep.ccr.cancer.gov/wp-content/uploads/Max-Leiserson-BTEP.mp4"][/video] 2019-11-19 11:00:00 Rm 2041/2107,NIH Bldg 37 In-Person BTEP 0 BTEP: Max Leiserson (UMD), Methods for Characterizing the Activity of Mutational Processes in Cancer
183
Description
https://btep.ccr.cancer.gov/wp-content/uploads/Max-Leiserson-BTEP.mp4?_=1 https://btep.ccr.cancer.gov/classes/max/ Presenter: Max Leiserson, Ph.D. Leiserson Research Group: https://lrgr.io Assistant Professor, Dept. of Computer Science, University of Maryland College Park
https://btep.ccr.cancer.gov/wp-content/uploads/Max-Leiserson-BTEP.mp4?_=1 https://btep.ccr.cancer.gov/classes/max/ Presenter: Max Leiserson, Ph.D. Leiserson Research Group: https://lrgr.io Assistant Professor, Dept. of Computer Science, University of Maryland College Park
Details
When
Tue, Nov 19, 2019 - 11:00 am - 12:00 pm
Where
Online
https://btep.ccr.cancer.gov/wp-content/uploads/Max-Leiserson-BTEP.mp4?_=1 https://btep.ccr.cancer.gov/classes/max/ Presenter: Max Leiserson, Ph.D. Leiserson Research Group: https://lrgr.io Assistant Professor, Dept. of Computer Science, University of Maryland College Park 2019-11-19 11:00:00 Online Online 0 Methods for Characterizing the Activity of Mutational Processes in Cancer
875
Description
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    Read More
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
Upcoming date: Dec 18, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM
Register
Organizer
BTEP
When
Wed, Nov 20, 2019 - 9:00 am - 12:00 pm
Where
Ft. Detrick, Bldg. 549, 549B
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files. For Windows PC, suggested tool is WinScp. 2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Finding, understanding and moving your data Tools available to CCR personnel for RNA-seq data analysis Upcoming date: Dec 18, Weds, Ft. Detrick, 549 B, 9 AM – 12 PM 2019-11-20 09:00:00 Ft. Detrick,Bldg. 549,549B In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
249 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/faa203bfaa1648f19e82b0d3d01aa881/playback 2019-11-20 15:00:00 In-Person 0 An overview of the pivotal Genomatix software functionalities for promoter and gene network analysis
870
Description
Drop-in anytime during the session. Please bring your government-issued computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    Read More
Drop-in anytime during the session. Please bring your government-issued computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html Go to the section "Alternative Binary Files" and download 64 bit putty.exe BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
 
Register
Organizer
BTEP
When
Tue, Dec 03, 2019 - 1:00 pm - 4:00 pm
Where
NIH Bldg 37, Rm 6107
Drop-in anytime during the session. Please bring your government-issued computer. Before coming to class, download a tool for transferring files. For Windows PC, suggested tool is WinScp. 2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html Go to the section "Alternative Binary Files" and download 64 bit putty.exe BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Finding, understanding and moving your data Tools available to CCR personnel for RNA-seq data analysis   2019-12-03 13:00:00 NIH Bldg 37,Rm 6107 In-Person Amy Stonelake (BTEP) BTEP 0 BTEP: NIH, Hands-on, drop-in session on Unix/Biowulf and RNA-Seq
250 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/90dd9528cd6a46e2b528a293779427f1/playback 2019-12-03 14:00:00 In-Person 0 Introduction to cBioPortal for Cancer Genomics
892
Description
Please bring a laptop. If you need to borrow a laptop please contact ncibtep@nih.gov No software installation is needed as this program will run on a  web browser. iCn3D provides a powerful, web-based platform to visualize and analyze proteins complexes in 1D (sequence) and 3D(structure), from single domains through large viruses. This hands-on training is geared towards non-expert users with no to little knowledge in protein structure, or experienced users that ...Read More
Please bring a laptop. If you need to borrow a laptop please contact ncibtep@nih.gov No software installation is needed as this program will run on a  web browser. iCn3D provides a powerful, web-based platform to visualize and analyze proteins complexes in 1D (sequence) and 3D(structure), from single domains through large viruses. This hands-on training is geared towards non-expert users with no to little knowledge in protein structure, or experienced users that do not know iCn3D. We will learn how to use it for 3D visualization, interactive protein structure analysis, visualization of SNPs on 3D structure, and sharing annotations with collaborators. Agenda: 2:30-3:45,  Hitting the road running on protein structure analysis 4:00-4:30,  Play with the protein of your choice Reference/Documentation on iCn3D paper: http://dx.doi.org/10.1093/bioinformatics/btz502 documentation: https://www.ncbi.nlm.nih.gov/Structure/icn3d/docs/icn3d_help.html If you are unable to attend in person, WebEx will be provided: https://cbiit.webex.com/cbiit/j.php?MTID=m375cb4351aefde1b93ae4dc3b9dbbfda
Register
Organizer
BTEP
When
Tue, Dec 10, 2019 - 2:30 pm - 4:30 pm
Where
Building 37 Room 4041/4107
Please bring a laptop. If you need to borrow a laptop please contact ncibtep@nih.gov No software installation is needed as this program will run on a  web browser. iCn3D provides a powerful, web-based platform to visualize and analyze proteins complexes in 1D (sequence) and 3D(structure), from single domains through large viruses. This hands-on training is geared towards non-expert users with no to little knowledge in protein structure, or experienced users that do not know iCn3D. We will learn how to use it for 3D visualization, interactive protein structure analysis, visualization of SNPs on 3D structure, and sharing annotations with collaborators. Agenda: 2:30-3:45,  Hitting the road running on protein structure analysis Analysis of the T-cell receptor complex structure that was recently solved (TCRɑβ/CD3εσ/CD3εδ/CD3ζ, PDB ID 6JXR, https://www.ncbi.nlm.nih.gov/Structure/pdb/6JXR) Look at P53 and P53-DNA interactions (PDB ID 1TUP, https://www.ncbi.nlm.nih.gov/Structure/pdb/1TUP), map SNPs from dbSNP and ClinVar and look at potential SNPs disruptions of key interactions. 4:00-4:30,  Play with the protein of your choice Reference/Documentation on iCn3D paper: http://dx.doi.org/10.1093/bioinformatics/btz502 documentation: https://www.ncbi.nlm.nih.gov/Structure/icn3d/docs/icn3d_help.html If you are unable to attend in person, WebEx will be provided: https://cbiit.webex.com/cbiit/j.php?MTID=m375cb4351aefde1b93ae4dc3b9dbbfda 2019-12-10 14:30:00 Building 37 Room 4041/4107 In-Person Philippe Youkharibache (NCI),Tom Madej (NCBI) BTEP 0 BTEP: Interactive 3D Protein Structure Analysis with iCn3D
893
Description
Hands On Training on Single Cell RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Hands on training on the new data visualization tool in Partek Flow – Data Viewer, using a Single Cell RNA-Seq data set as an example. The newly implemented and released Data Viewer ...Read More
Hands On Training on Single Cell RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Hands on training on the new data visualization tool in Partek Flow – Data Viewer, using a Single Cell RNA-Seq data set as an example. The newly implemented and released Data Viewer in Partek Flow provides more flexible and easy ways to integrate information collected from the data, which helps biologists discover biological meanings. Agenda:
  • Overview on the new Partek Flow Data Viewer
  • Hand On Session on Single Cell RNA-Seq Analysis using the new Data Viewer
    • Import count matrix
    • Single cell QC
    • Filter and Normalization
    • Dimension reduction (PCA, tSNE, UMAP)
    • Identify subpopulation with newly released Data Viewer
    • Differential expression detection
    • Visualizations (scatterplot, violin plot, volcano plot, box plot, histogram etc.)
    • Biological interpretation
  • Q/A
If you are unable to attend in person WebEx will be available: Meeting number (access code): 734 351 875 Meeting password: 6JiNF8B@ Wednesday, December 11, 2019 1:00 pm  |  (UTC-05:00) Eastern Time (US & Canada)  |  3 hrs Join meeting Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 734351875@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 734351875.cbiit@lync.webex.com  
Register
Organizer
BTEP
When
Wed, Dec 11, 2019 - 1:00 pm - 4:00 pm
Where
FAES Room 2 – B1C209
Hands On Training on Single Cell RNA-Seq Data Analysis in Partek Flow Please bring your own computer for this hands-on training. If you need to borrow a computer please email ncibtep@nih.gov Hands on training on the new data visualization tool in Partek Flow – Data Viewer, using a Single Cell RNA-Seq data set as an example. The newly implemented and released Data Viewer in Partek Flow provides more flexible and easy ways to integrate information collected from the data, which helps biologists discover biological meanings. Agenda: Overview on the new Partek Flow Data Viewer Hand On Session on Single Cell RNA-Seq Analysis using the new Data Viewer Import count matrix Single cell QC Filter and Normalization Dimension reduction (PCA, tSNE, UMAP) Identify subpopulation with newly released Data Viewer Differential expression detection Visualizations (scatterplot, violin plot, volcano plot, box plot, histogram etc.) Biological interpretation Q/A If you are unable to attend in person WebEx will be available: Meeting number (access code): 734 351 875 Meeting password: 6JiNF8B@ Wednesday, December 11, 2019 1:00 pm  |  (UTC-05:00) Eastern Time (US & Canada)  |  3 hrs Join meeting Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 734351875@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 734351875.cbiit@lync.webex.com   2019-12-11 13:00:00 FAES Room 2 – B1C209 In-Person Xiaowen Wang (Partek) BTEP 0 BTEP: Single Cell RNA-Seq Analysis with Partek Flow
251
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/3b0378fb09f24894bed50f0311400043/playback 2019-12-13 15:00:00 In-Person 0 Introduction to Geneious Prime
876
Description
THIS EVENT HAS BEEN CANCELLED
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    Read More
THIS EVENT HAS BEEN CANCELLED
Drop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html We will be covering the following topics:
  • Unix bootcamp – learn to navigate a Unix file system and move, copy, and edit files
  • Looking at the quality of your sequence data with FastQC and MultiQC
  • Trimming sequences with CutAdapt
Register
Organizer
BTEP
When
Wed, Dec 18, 2019 - 9:00 am - 12:00 pm
Where
Ft. Detrick, Bldg. 549, 549B
THIS EVENT HAS BEEN CANCELLEDDrop-in anytime during the session. Bring your own computer. Before coming to class, download a tool for transferring files. For Windows PC, suggested tool is WinScp. 2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html We will be covering the following topics: Unix bootcamp – learn to navigate a Unix file system and move, copy, and edit files Looking at the quality of your sequence data with FastQC and MultiQC Trimming sequences with CutAdapt 2019-12-18 09:00:00 Ft. Detrick,Bldg. 549,549B In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Hands-on, drop-in session on Unix/Biowulf and RNA-seq - CANCELLED
894
Description
Drop-in anytime during the session. Bring your government-issued computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    Read More
Drop-in anytime during the session. Bring your government-issued computer. Before coming to class, download a tool for transferring files.
  1. For Windows PC, suggested tool is WinScp.
2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html Go to the section "Alternative Binary Files" and download 64 bit putty.exe BTEP personnel will be available to answer questions about:
  • Working at the Unix command line on Biowulf
  • Finding, understanding and moving your data
  • Tools available to CCR personnel for RNA-seq data analysis
Register
Organizer
BTEP
When
Thu, Dec 19, 2019 - 9:00 am - 12:00 pm
Where
549A
Drop-in anytime during the session. Bring your government-issued computer. Before coming to class, download a tool for transferring files. For Windows PC, suggested tool is WinScp. 2. For Mac, suggested tool is Filezilla. 3. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html Go to the section "Alternative Binary Files" and download 64 bit putty.exe BTEP personnel will be available to answer questions about: Working at the Unix command line on Biowulf Finding, understanding and moving your data Tools available to CCR personnel for RNA-seq data analysis 2019-12-19 09:00:00 549A In-Person Amy Stonelake (BTEP) BTEP 0 BTEP, Frederick: Hands-on drop-in session for Unix/Biowulf and RNA-Seq
42
Description
This course covers the fundamentals of building IT infrastructure on the AWS platform. Students learn how to optimize the AWS Cloud by understanding how AWS services fit into cloud-based solutions. In addition, students explore AWS Cloud best practices and design patterns for architecting optimal IT solutions on AWS, and build a variety of infrastructures in guided, hands-on activities. The course also covers how to create fledgling architectures and build them into robust and adaptive solutions. ...Read More
This course covers the fundamentals of building IT infrastructure on the AWS platform. Students learn how to optimize the AWS Cloud by understanding how AWS services fit into cloud-based solutions. In addition, students explore AWS Cloud best practices and design patterns for architecting optimal IT solutions on AWS, and build a variety of infrastructures in guided, hands-on activities. The course also covers how to create fledgling architectures and build them into robust and adaptive solutions. Course Objectives In this course, you will learn to: Make architectural decisions based on AWS architectural principles and best practices Leverage AWS services to make your infrastructure scalable, reliable, and highly available Leverage AWS Managed Services to enable greater flexibility and resiliency in an infrastructure Make an AWS-based infrastructure more efficient to increase performance and reduce costs Use the Well-Architected Framework to improve architectures with AWS solutions Intended Audience This course is intended for: Solutions architects Solution design engineers
Details
Organizer
NIH Training Library
When
Mon, Jan 06 - Wed, Jan 08, 2020 -9:00 am - 5:00 pm
Where
In-Person
This course covers the fundamentals of building IT infrastructure on the AWS platform. Students learn how to optimize the AWS Cloud by understanding how AWS services fit into cloud-based solutions. In addition, students explore AWS Cloud best practices and design patterns for architecting optimal IT solutions on AWS, and build a variety of infrastructures in guided, hands-on activities. The course also covers how to create fledgling architectures and build them into robust and adaptive solutions. Course Objectives In this course, you will learn to: Make architectural decisions based on AWS architectural principles and best practices Leverage AWS services to make your infrastructure scalable, reliable, and highly available Leverage AWS Managed Services to enable greater flexibility and resiliency in an infrastructure Make an AWS-based infrastructure more efficient to increase performance and reduce costs Use the Well-Architected Framework to improve architectures with AWS solutions Intended Audience This course is intended for: Solutions architects Solution design engineers 2020-01-06 09:00:00 In-Person NIH Training Library 0 Architecting with Amazon Web Services (AWS)
45
Description
The CIViC knowledgebase (www.civicdb.org) is an open access, open source, community-driven resource for Clinical Interpretation of Variants in Cancer. The goal of CIViC is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. The knowledgebase leverages public curation and expert moderation to create a free resource that compiles, annotates, and distributes published cancer variant knowledge to the community. ...Read More
The CIViC knowledgebase (www.civicdb.org) is an open access, open source, community-driven resource for Clinical Interpretation of Variants in Cancer. The goal of CIViC is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. The knowledgebase leverages public curation and expert moderation to create a free resource that compiles, annotates, and distributes published cancer variant knowledge to the community. Identifying and cataloging curated clinical variant knowledge in the peer-reviewed published literature for rapid searching, evaluation, dissemination, and integration into other resources enables clinical cancer variant analysis. The database can be accessed without restrictions through a user-friendly interface, via a flexible public API, or the CIViCpy Software Development Kit. All data is available without login, but curation and other functions such as commenting, flagging, and suggesting changes requires users to create a free CIViC account. The history of all curation and revision in CIViC is viewable on the web interface. Selected, expert editors review and revise submitted content, which is labeled as accepted after complete moderation. All content in CIViC adheres to a structured data model, which incorporates ontologies, standards and guidelines from across the field to promote interoperability and compatibility with other efforts. CIViC currently has a community of over 190 curators and 16,000 clinical and research users around the world. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancer.gov
Details
Organizer
CBIIT
When
Tue, Jan 07, 2020 - 12:30 pm - 1:30 pm
Where
In-Person
The CIViC knowledgebase (www.civicdb.org) is an open access, open source, community-driven resource for Clinical Interpretation of Variants in Cancer. The goal of CIViC is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. The knowledgebase leverages public curation and expert moderation to create a free resource that compiles, annotates, and distributes published cancer variant knowledge to the community. Identifying and cataloging curated clinical variant knowledge in the peer-reviewed published literature for rapid searching, evaluation, dissemination, and integration into other resources enables clinical cancer variant analysis. The database can be accessed without restrictions through a user-friendly interface, via a flexible public API, or the CIViCpy Software Development Kit. All data is available without login, but curation and other functions such as commenting, flagging, and suggesting changes requires users to create a free CIViC account. The history of all curation and revision in CIViC is viewable on the web interface. Selected, expert editors review and revise submitted content, which is labeled as accepted after complete moderation. All content in CIViC adheres to a structured data model, which incorporates ontologies, standards and guidelines from across the field to promote interoperability and compatibility with other efforts. CIViC currently has a community of over 190 curators and 16,000 clinical and research users around the world. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancer.gov 2020-01-07 12:30:00 In-Person CBIIT 0 Clinical Interpretation of Variants in Cancer (CIViC) Knowledgebase
46
Description
Description In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Intended Audience This course is intended for: • ...Read More
Description In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Intended Audience This course is intended for: • Data science practitioners • Machine learning practitioners • Developers and engineers • Systems architects Course Objectives In this course, you will learn how to: • Apply Amazon SageMaker to a specific use case and dataset • Practice all the steps of the typical data science process • Visualize and understand the dataset • Explore how the attributes of the dataset relate to each other • Prepare the dataset for training • Use built-in algorithms • Train models with Amazon SageMaker using built-in algorithms • Explore results and performance of the model, and demonstrate how it can be tuned and executed outside of SageMaker • Run predictions on a batch of data with Amazon SageMaker • Deploy a model to an endpoint in Amazon SageMaker for real-time predictions • Learn how to configure an endpoint for serving predictions at scale • Understand Hyperparameter Optimization (HPO) with Amazon SageMaker to find optimal model parameters • Understand how to perform A/B model testing using Amazon SageMaker • Perform the domain-specific cost of errors analysis to further tune the model threshold in order to maximize model utility expressed in financial terms Prerequisites We recommend that attendees of this course have the following prerequisites: • Experience with Python programming language • Familiarity with NumPy and Pandas Python libraries is a plus • Familiarity with fundamental machine learning algorithms • Familiarity with productionizing machine learning models Delivery Method This course is delivered through [a mix of]: • Hands-on labs
Details
When
Thu, Jan 09, 2020 - 9:00 am - 5:00 pm
Where
In-Person
Description In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Intended Audience This course is intended for: • Data science practitioners • Machine learning practitioners • Developers and engineers • Systems architects Course Objectives In this course, you will learn how to: • Apply Amazon SageMaker to a specific use case and dataset • Practice all the steps of the typical data science process • Visualize and understand the dataset • Explore how the attributes of the dataset relate to each other • Prepare the dataset for training • Use built-in algorithms • Train models with Amazon SageMaker using built-in algorithms • Explore results and performance of the model, and demonstrate how it can be tuned and executed outside of SageMaker • Run predictions on a batch of data with Amazon SageMaker • Deploy a model to an endpoint in Amazon SageMaker for real-time predictions • Learn how to configure an endpoint for serving predictions at scale • Understand Hyperparameter Optimization (HPO) with Amazon SageMaker to find optimal model parameters • Understand how to perform A/B model testing using Amazon SageMaker • Perform the domain-specific cost of errors analysis to further tune the model threshold in order to maximize model utility expressed in financial terms Prerequisites We recommend that attendees of this course have the following prerequisites: • Experience with Python programming language • Familiarity with NumPy and Pandas Python libraries is a plus • Familiarity with fundamental machine learning algorithms • Familiarity with productionizing machine learning models Delivery Method This course is delivered through [a mix of]: • Hands-on labs 2020-01-09 09:00:00 In-Person 0 Practical Data Science with Amazon SageMaker
47
Description
In this introductory course, you will learn about AWS products, services, and common solutions. You will learn the fundamentals of identifying AWS services so that you can make informed decisions about IT solutions based on your business requirements. Course Objectives In this course, you will learn to: Terminology and concepts related to the AWS platform How to navigate the AWS Management Console Key concepts of AWS security measures and AWS Identity and Access Management (IAM) ...Read More
In this introductory course, you will learn about AWS products, services, and common solutions. You will learn the fundamentals of identifying AWS services so that you can make informed decisions about IT solutions based on your business requirements. Course Objectives In this course, you will learn to: Terminology and concepts related to the AWS platform How to navigate the AWS Management Console Key concepts of AWS security measures and AWS Identity and Access Management (IAM) Key AWS Services Included Foundational services: Amazon Elastic Compute Cloud (EC2), Amazon Virtual Private Cloud (VPC), Amazon Simple Storage Service (S3), and Amazon Elastic Block Store (EBS) Database services: Amazon DynamoDB and Amazon Relational Database Service (RDS) Management services: AWS Auto Scaling, Amazon CloudWatch, Elastic Load Balancing (ELB), and AWS Trusted Advisor Intended Audience This course is intended for: Individuals responsible for articulating the technical benefits of AWS services to customers Individuals interested in learning how to get started with AWS
Details
When
Fri, Jan 10, 2020 - 9:00 am - 5:00 pm
Where
In-Person
In this introductory course, you will learn about AWS products, services, and common solutions. You will learn the fundamentals of identifying AWS services so that you can make informed decisions about IT solutions based on your business requirements. Course Objectives In this course, you will learn to: Terminology and concepts related to the AWS platform How to navigate the AWS Management Console Key concepts of AWS security measures and AWS Identity and Access Management (IAM) Key AWS Services Included Foundational services: Amazon Elastic Compute Cloud (EC2), Amazon Virtual Private Cloud (VPC), Amazon Simple Storage Service (S3), and Amazon Elastic Block Store (EBS) Database services: Amazon DynamoDB and Amazon Relational Database Service (RDS) Management services: AWS Auto Scaling, Amazon CloudWatch, Elastic Load Balancing (ELB), and AWS Trusted Advisor Intended Audience This course is intended for: Individuals responsible for articulating the technical benefits of AWS services to customers Individuals interested in learning how to get started with AWS 2020-01-10 09:00:00 In-Person 0 Amazon Web Services Technical Essentials
48
Description
Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.
Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.
Details
Organizer
NIH Training Library
When
Mon, Jan 13, 2020 - 9:30 am - 5:00 pm
Where
In-Person
Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. 2020-01-13 09:30:00 In-Person NIH Training Library 0 Data Carpentries Genomics Workshop
49
Description
Sushant Kumar, Ph.D. Yale University Moving beyond the canonical dichotomy of drivers and passengers in cancer hosted by the Cancer Data Science Laboratory (CDSL) on Monday, January 13th at 1:00 p.m. Dr. Kumar's research focuses on developing integrative computational approaches to investigate the influence of genomic variations on gene regulation and their role in various types of cancer. To view the schedule for all upcoming Earl Stadtman Investigator Program seminars, please visit: Read More
Sushant Kumar, Ph.D. Yale University Moving beyond the canonical dichotomy of drivers and passengers in cancer hosted by the Cancer Data Science Laboratory (CDSL) on Monday, January 13th at 1:00 p.m. Dr. Kumar's research focuses on developing integrative computational approaches to investigate the influence of genomic variations on gene regulation and their role in various types of cancer. To view the schedule for all upcoming Earl Stadtman Investigator Program seminars, please visit: ccrod.cancer.gov/confluence/display/NIHStadt The Earl Stadtman Investigator search is a trans-NIH effort to attract a diverse group of talented early-career scientists pursuing interests across the biomedical research spectrum. Additional information on the program can be found at: irp.nih.gov/careers/trans-nih-scientific-recruitments/stadtman-tenure-track-investigators
Details
When
Mon, Jan 13, 2020 - 1:00 pm - 2:00 pm
Where
In-Person
Sushant Kumar, Ph.D. Yale University Moving beyond the canonical dichotomy of drivers and passengers in cancer hosted by the Cancer Data Science Laboratory (CDSL) on Monday, January 13th at 1:00 p.m. Dr. Kumar's research focuses on developing integrative computational approaches to investigate the influence of genomic variations on gene regulation and their role in various types of cancer. To view the schedule for all upcoming Earl Stadtman Investigator Program seminars, please visit: ccrod.cancer.gov/confluence/display/NIHStadt The Earl Stadtman Investigator search is a trans-NIH effort to attract a diverse group of talented early-career scientists pursuing interests across the biomedical research spectrum. Additional information on the program can be found at: irp.nih.gov/careers/trans-nih-scientific-recruitments/stadtman-tenure-track-investigators 2020-01-13 13:00:00 In-Person 0 Moving beyond the canonical dichotomy of drivers and passengers in cancer
51
Description
Debra S. Marks, PhD Principal Investigator Associate Professor, Harvard Medical School
Debra S. Marks, PhD Principal Investigator Associate Professor, Harvard Medical School
Details
When
Mon, Jan 13, 2020 - 3:00 pm - 4:00 pm
Where
In-Person
Debra S. Marks, PhD Principal Investigator Associate Professor, Harvard Medical School 2020-01-13 15:00:00 In-Person 0 Biological Discovery and Design using Machine Learning
52
Description
Tim Griffin, Ph.D. and Pratik Jagtap, Ph.D., M.Sc In this talk, Dr. Griffin and Dr. Jagtap will present short demonstrations of multi-omic tools and validated workflows and discuss application and access to these resources.
Tim Griffin, Ph.D. and Pratik Jagtap, Ph.D., M.Sc In this talk, Dr. Griffin and Dr. Jagtap will present short demonstrations of multi-omic tools and validated workflows and discuss application and access to these resources.
Details
Organizer
CBIIT
When
Wed, Jan 15, 2020 - 11:00 am - 12:00 pm
Where
In-Person
Tim Griffin, Ph.D. and Pratik Jagtap, Ph.D., M.Sc In this talk, Dr. Griffin and Dr. Jagtap will present short demonstrations of multi-omic tools and validated workflows and discuss application and access to these resources. 2020-01-15 11:00:00 In-Person CBIIT 0 Galaxy-based Multi-omic Informatics Hub for Cancer Researchers
881
Description
Marshaling Public Data for Lean and Powerful Splicing Studies The Sequence Read Archive (SRA) now contains over a million accessions. Such archives are potential gold mines for researchers but they are not organized for everyday use by scientists.  The situation resembles the early days of the World Wide Web, before search engines made the web easy to use.  I will describe our work on making making large public RNA sequencing datasets easy to ...Read More
Marshaling Public Data for Lean and Powerful Splicing Studies The Sequence Read Archive (SRA) now contains over a million accessions. Such archives are potential gold mines for researchers but they are not organized for everyday use by scientists.  The situation resembles the early days of the World Wide Web, before search engines made the web easy to use.  I will describe our work on making making large public RNA sequencing datasets easy to use.  I will describe our multi-layered design, with one layer for scalable and uniform analysis (Rail-RNA), another for forming easy-to-use summarized (recount2), and a third for indexing the summaries and making them queryable (Snaptron). Altogether, the system allows scientists to pose scientific questions over vast gene expression and splicing summaries.  I will describe collaborations where these tools were applied to (a) evaluate hypotheses about prevalence or specificity of splicing patterns, (b) characterize completeness of the gene annotations we use to understand splicing patterns, and (c) reveal patterns in public data that ultimately changed the study design and allowed more targeted hypotheses to be tested with less new data generation. This is joint work with Chris Wilks, Abhinav Nellore, Jonathan Ling, Seth Blackshaw, Luigi Marchionni, Jeff Leek, Kasper Hansen, Andrew Jaffe and others. WebEx recording: https://cbiit.webex.com/cbiit/ldr.php?RCID=da15d3aface8f25f7f478b9a813f6499    
Register
Organizer
BTEP
When
Thu, Jan 16, 2020 - 11:00 am - 12:00 pm
Where
Building 37 Room 4041/4107
Marshaling Public Data for Lean and Powerful Splicing Studies The Sequence Read Archive (SRA) now contains over a million accessions. Such archives are potential gold mines for researchers but they are not organized for everyday use by scientists.  The situation resembles the early days of the World Wide Web, before search engines made the web easy to use.  I will describe our work on making making large public RNA sequencing datasets easy to use.  I will describe our multi-layered design, with one layer for scalable and uniform analysis (Rail-RNA), another for forming easy-to-use summarized (recount2), and a third for indexing the summaries and making them queryable (Snaptron). Altogether, the system allows scientists to pose scientific questions over vast gene expression and splicing summaries.  I will describe collaborations where these tools were applied to (a) evaluate hypotheses about prevalence or specificity of splicing patterns, (b) characterize completeness of the gene annotations we use to understand splicing patterns, and (c) reveal patterns in public data that ultimately changed the study design and allowed more targeted hypotheses to be tested with less new data generation. This is joint work with Chris Wilks, Abhinav Nellore, Jonathan Ling, Seth Blackshaw, Luigi Marchionni, Jeff Leek, Kasper Hansen, Andrew Jaffe and others. WebEx recording: https://cbiit.webex.com/cbiit/ldr.php?RCID=da15d3aface8f25f7f478b9a813f6499     2020-01-16 11:00:00 Building 37 Room 4041/4107 In-Person Ben Langmead (JHU) BTEP 0 BTEP: Ben Langmead (JHU), Distinguished Speakers Seminar Series
53
Description
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training ...Read More
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.
Details
Organizer
NIH Training Library
When
Tue, Jan 21, 2020 - 3:00 pm - 4:30 pm
Where
In-Person
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. 2020-01-21 15:00:00 In-Person NIH Training Library 0 Intro to R Data Types
895
Description
  1. Experimental Design Considerations in Variant Analysis
    1. Germline vs Somatic
    2. WGS vs WES
    3. Sample sizes and statistical power
  2. WGS/WES Pipelines at NIH
    1. Pipeline performance
    2. Using Pipeliner for internal and external data
  3. Variant QC, Annotation and Downstream Analysis
    1. <...Read More
  1. Experimental Design Considerations in Variant Analysis
    1. Germline vs Somatic
    2. WGS vs WES
    3. Sample sizes and statistical power
  2. WGS/WES Pipelines at NIH
    1. Pipeline performance
    2. Using Pipeliner for internal and external data
  3. Variant QC, Annotation and Downstream Analysis
    1. Variant QC and data correction
    2. Variant annotation and analysis tools
  4. Structural variation and multi-omic integration
If you unable to attend in person, WebEx will be provided. Thursday, January 23, 2020 11:00 am  |  Eastern Standard Time (New York, GMT-05:00)  |  1 hr 30 mins Join meeting Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 730733238@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 730733238.cbiit@lync.webex.com
Register
Organizer
BTEP
When
Thu, Jan 23, 2020 - 11:00 am - 12:00 pm
Where
Building 37 Room 4041/4107
Experimental Design Considerations in Variant Analysis Germline vs Somatic WGS vs WES Sample sizes and statistical power WGS/WES Pipelines at NIH Pipeline performance Using Pipeliner for internal and external data Variant QC, Annotation and Downstream Analysis Variant QC and data correction Variant annotation and analysis tools Structural variation and multi-omic integration If you unable to attend in person, WebEx will be provided. Thursday, January 23, 2020 11:00 am  |  Eastern Standard Time (New York, GMT-05:00)  |  1 hr 30 mins Join meeting Join by phone Tap to call in from a mobile device (attendees only) 1-650-479-3207 Call-in toll number (US/Canada) Join from a video system or application Dial 730733238@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join using Microsoft Lync or Microsoft Skype for Business Dial 730733238.cbiit@lync.webex.com 2020-01-23 11:00:00 Building 37 Room 4041/4107 In-Person Justin Lack (NIAID CBR) BTEP 0 BTEP: Variant Analysis in Next Generation Sequencing Data, Whole Genome and Whole Exome
54
Description
This lecture will briefly review the steps involved in data analysis and how study design, hypothesis, and type of data and their distributions contribute to the choice of statistical tests. Statistical tests are used to determine the presence and strength of a relationship between independent and outcome variables. The basic concepts around the use and interpretation of the following statistical tests will be covered: chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, ...Read More
This lecture will briefly review the steps involved in data analysis and how study design, hypothesis, and type of data and their distributions contribute to the choice of statistical tests. Statistical tests are used to determine the presence and strength of a relationship between independent and outcome variables. The basic concepts around the use and interpretation of the following statistical tests will be covered: chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and non-parametric tests.
Details
Organizer
NIH Training Library
When
Thu, Jan 23, 2020 - 1:00 pm - 4:00 pm
Where
Online
This lecture will briefly review the steps involved in data analysis and how study design, hypothesis, and type of data and their distributions contribute to the choice of statistical tests. Statistical tests are used to determine the presence and strength of a relationship between independent and outcome variables. The basic concepts around the use and interpretation of the following statistical tests will be covered: chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and non-parametric tests. 2020-01-23 13:00:00 Online NIH Training Library 0 Overview of Common Statistical Tests
55
Description
Data Wrangling in R is the third class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of manipulating, analyzing and exporting data with the R tidyverse. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services ...Read More
Data Wrangling in R is the third class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of manipulating, analyzing and exporting data with the R tidyverse. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.
Details
Organizer
NIH Training Library
When
Mon, Jan 27, 2020 - 1:00 pm - 2:30 pm
Where
Online
Data Wrangling in R is the third class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of manipulating, analyzing and exporting data with the R tidyverse. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. 2020-01-27 13:00:00 Online NIH Training Library 0 Data Wrangling in R
56
Description
Learn about the GDC's submission process and tools. Get a demonstration of how to upload and review data.
Learn about the GDC's submission process and tools. Get a demonstration of how to upload and review data.
Details
When
Mon, Jan 27, 2020 - 2:00 pm - 3:00 pm
Where
Online
Learn about the GDC's submission process and tools. Get a demonstration of how to upload and review data. 2020-01-27 14:00:00 Online 0 Genomic Data Commons Webinar: Data Submission
57
Description
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/246d5cb98c8d4cd0b2e8f6fb0368978b GenePattern enables researchers at all levels of computational expertise to use hundreds of tools for the analysis of gene expression, sequence variation, proteomics, and more, through an intuitive interface that requires no coding. GenePattern ...Read More
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/246d5cb98c8d4cd0b2e8f6fb0368978b GenePattern enables researchers at all levels of computational expertise to use hundreds of tools for the analysis of gene expression, sequence variation, proteomics, and more, through an intuitive interface that requires no coding. GenePattern makes reproducible research easy: analyses can be rerun at any time with the same inputs; every version of each tool is tracked, so that a result can be reproduced even if the code that produced it changes in the future; and researchers can chain analyses together to encapsulate and share their research as reproducible workflows. A new GenePattern Notebook environment,https://notebook.genepattern.org/#gsc.tab=0 based on the popular Jupyter Notebook system, further allows users to interleave text, graphics, and analyses in unified "research narratives" that can be shared and published. In this webinar, participants will learn how to: • Identify available GenePattern analyses relevant to their scientific objectives • Analyze and visualize gene expression (including RNA-seq) and other genomic data • Ensure that their analyses are reproducible • Create and publish research narratives that serve as a live, executable, shareable representation of a study The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/246d5cb98c8d4cd0b2e8f6fb0368978b For any questions please contact Daoud Meerzaman or Juli Klemm
Details
Organizer
CBIIT
When
Tue, Jan 28, 2020 - 11:00 am - 12:00 pm
Where
In-Person
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/246d5cb98c8d4cd0b2e8f6fb0368978b GenePattern enables researchers at all levels of computational expertise to use hundreds of tools for the analysis of gene expression, sequence variation, proteomics, and more, through an intuitive interface that requires no coding. GenePattern makes reproducible research easy: analyses can be rerun at any time with the same inputs; every version of each tool is tracked, so that a result can be reproduced even if the code that produced it changes in the future; and researchers can chain analyses together to encapsulate and share their research as reproducible workflows. A new GenePattern Notebook environment,https://notebook.genepattern.org/#gsc.tab=0 based on the popular Jupyter Notebook system, further allows users to interleave text, graphics, and analyses in unified "research narratives" that can be shared and published. In this webinar, participants will learn how to: • Identify available GenePattern analyses relevant to their scientific objectives • Analyze and visualize gene expression (including RNA-seq) and other genomic data • Ensure that their analyses are reproducible • Create and publish research narratives that serve as a live, executable, shareable representation of a study The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/246d5cb98c8d4cd0b2e8f6fb0368978b For any questions please contact Daoud Meerzaman or Juli Klemm 2020-01-28 11:00:00 In-Person CBIIT 0 Integrative Genomics Analysis with the GenePattern Notebook Environment
58
Description
NCI Shady Grove, 9609 Medical Center Drive, Room 2W032/034, and via Webinar Rachel Karchin, Ph.D., and Kym Pagel, Ph.D. Dr. Karchin and Dr. Pagel will present OpenCRAVAT, a new open source, scalable decision support system for studying gene variants and gene prioritization.
NCI Shady Grove, 9609 Medical Center Drive, Room 2W032/034, and via Webinar Rachel Karchin, Ph.D., and Kym Pagel, Ph.D. Dr. Karchin and Dr. Pagel will present OpenCRAVAT, a new open source, scalable decision support system for studying gene variants and gene prioritization.
Details
Organizer
CBIIT
When
Wed, Jan 29, 2020 - 11:00 am - 12:00 pm
Where
In-Person
NCI Shady Grove, 9609 Medical Center Drive, Room 2W032/034, and via Webinar Rachel Karchin, Ph.D., and Kym Pagel, Ph.D. Dr. Karchin and Dr. Pagel will present OpenCRAVAT, a new open source, scalable decision support system for studying gene variants and gene prioritization. 2020-01-29 11:00:00 In-Person CBIIT 0 OpenCRAVAT: An Open Source Collaborative Platform for the Annotation of Human Genetic Variation
897
Description
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are ...Read More
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq.  Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset.  Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression.  After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Wed, Jan 29, 2020 - 3:00 pm - 5:00 pm
Where
Frederick, Fort Detrick, Building 549, Conference Room A
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq.  Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset.  Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression.  After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-01-29 15:00:00 Frederick, Fort Detrick, Building 549, Conference Room A In-Person Joshua Meyer (CCBR) BTEP 0 Single-Cell RNA-Seq: Analysis on the Palantir Platform (Jan. 29th, Frederick)
59
Description
Details
Organizer
CBIIT
When
Thu, Jan 30, 2020 - 1:00 am - 5:00 pm
Where
In-Person
2020-01-30 01:00:00 In-Person CBIIT 0 Fluorescence Image Restoration and Denoising, A Biologist's View
60
Description
Details
Organizer
CBIIT
When
Fri, Jan 31, 2020 - 9:30 am - 4:30 pm
Where
In-Person
2020-01-31 09:30:00 In-Person CBIIT 0 Machine Learning Image Analysis: A Practical Hands-on Tutorial Using Aivia
61
Description
Description: Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community
Description: Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community
Details
When
Mon, Feb 03, 2020 - 11:00 am - 12:00 pm
Where
Online
Description: Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community 2020-02-03 11:00:00 Online 0 Data Science Webinar Series: Intro to Big Data & Data Lifecycle
62
Description
Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community
Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community
Details
When
Tue, Feb 04, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community 2020-02-04 11:00:00 Online 0 Data Science Webinar Series: Intro to Big Data & Data Lifecycle
63
Description
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/b8219ffc2b1e4b1daaac7a99c49b4274 Cistrome is an online resource for transcriptional and epigenetic gene regulation.The Cistrome analysis pipeline helps users analyze their ChIP-seq and chromatin accessibility data online, and the Cistrome database allows users to search and browse public ...Read More
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/b8219ffc2b1e4b1daaac7a99c49b4274 Cistrome is an online resource for transcriptional and epigenetic gene regulation.The Cistrome analysis pipeline helps users analyze their ChIP-seq and chromatin accessibility data online, and the Cistrome database allows users to search and browse public data collected and processed. This is an introductory level workshop, although users are expected to have basic knowledge of ChIP-seq, gene regulation, and immunology. Visit the Cistrome website to learn more visit http://cistrome.org/ The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://itcr.cancer.gov/ For any questions please contact Daoud Meerzaman or Juli Klemm.
Details
Organizer
CBIIT
When
Tue, Feb 04, 2020 - 12:30 pm - 1:30 pm
Where
Online
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/b8219ffc2b1e4b1daaac7a99c49b4274 Cistrome is an online resource for transcriptional and epigenetic gene regulation.The Cistrome analysis pipeline helps users analyze their ChIP-seq and chromatin accessibility data online, and the Cistrome database allows users to search and browse public data collected and processed. This is an introductory level workshop, although users are expected to have basic knowledge of ChIP-seq, gene regulation, and immunology. Visit the Cistrome website to learn more visit http://cistrome.org/ The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://itcr.cancer.gov/ For any questions please contact Daoud Meerzaman or Juli Klemm. 2020-02-04 12:30:00 Online In-Person CBIIT 0 Introduction to Cistrome
64
Description
Introduction to Data Visualization in R: ggplot in R is the fourth class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of producing scatter plots, boxplots, and time series plots using ggplot. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered ...Read More
Introduction to Data Visualization in R: ggplot in R is the fourth class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of producing scatter plots, boxplots, and time series plots using ggplot. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. Note for webinar version of this class: Students are encouraged to install R and RStudio before the webinar so that they can follow along with the instructor. Attendees will need to download the class data before the webinar.
Details
Organizer
NIH Training Library
When
Tue, Feb 04, 2020 - 1:00 pm - 2:30 pm
Where
Online
Introduction to Data Visualization in R: ggplot in R is the fourth class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of producing scatter plots, boxplots, and time series plots using ggplot. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. Note for webinar version of this class: Students are encouraged to install R and RStudio before the webinar so that they can follow along with the instructor. Attendees will need to download the class data before the webinar. 2020-02-04 13:00:00 Online NIH Training Library 0 Intro to Data Visualization in R: ggplot
898
Description
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the theory behind RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression, volcano plots, differential expression of genes analysis, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Tue, Feb 04, 2020 - 1:30 pm - 3:30 pm
Where
Bethesda, Building 10, FAES Classroom #6 (B1C208)
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the theory behind RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression, volcano plots, differential expression of genes analysis, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-02-04 13:30:00 Bethesda, Building 10, FAES Classroom #6 (B1C208) In-Person Joshua Meyer (CCBR) BTEP 0 RNA-Seq: Analysis on the Palantir Platform (Feb. 4th, Bethesda)
252
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/b8219ffc2b1e4b1daaac7a99c49b4274/playback 2020-02-04 14:00:00 In-Person 0 Introduction to Cistrome
890
Description
To Function or Not to Function The functions of only a minority of genes in any species is known. And even in those cases the functional annotation is highly incomplete and largely devoid of context. At an even more fundamental level, how can we know whether a gene serves any relevant biological function in a given context? In this informal presentation we will discuss a few vignettes related to the broad questions of context-specific functions ...Read More
To Function or Not to Function The functions of only a minority of genes in any species is known. And even in those cases the functional annotation is highly incomplete and largely devoid of context. At an even more fundamental level, how can we know whether a gene serves any relevant biological function in a given context? In this informal presentation we will discuss a few vignettes related to the broad questions of context-specific functions of genes, in a variety of contexts from bacterial response to drugs, normal tissues, and cancer.   Recording of the talk is here: https://cbiit.webex.com/cbiit/ldr.php?RCID=ec72d1923ffe18a82b63ed348b75be0c  
Register
Organizer
BTEP
When
Thu, Feb 06, 2020 - 11:00 am - 12:00 pm
Where
Building 37 Room 4041/4107
To Function or Not to Function The functions of only a minority of genes in any species is known. And even in those cases the functional annotation is highly incomplete and largely devoid of context. At an even more fundamental level, how can we know whether a gene serves any relevant biological function in a given context? In this informal presentation we will discuss a few vignettes related to the broad questions of context-specific functions of genes, in a variety of contexts from bacterial response to drugs, normal tissues, and cancer.   Recording of the talk is here: https://cbiit.webex.com/cbiit/ldr.php?RCID=ec72d1923ffe18a82b63ed348b75be0c   2020-02-06 11:00:00 Building 37 Room 4041/4107 In-Person Sridhar Hannenhalli (CDSL) BTEP 0 BTEP: Sridhar Hannenhalli (CDSL), Distinguished Speakers Seminar Series
66
Description
Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community.
Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community.
Details
When
Thu, Feb 06, 2020 - 11:00 pm - 10:00 am
Where
Online
Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community. 2020-02-06 23:00:00 Online 0 Data Science Webinar Series: Intro to Big Data & Data Lifecycle
78
Description
Description: Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community.
Description: Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community.
Details
When
Fri, Feb 07, 2020 - 11:00 am - 12:00 pm
Where
Online
Description: Dr. Mark Musen from Stanford University highlights major themes in the management and use of scientific data, gives an overview of the data life cycle, and describes ways in which investigators can ensure that their data will have maximum benefit to the scientific community. 2020-02-07 11:00:00 Online 0 Data Science Webinar Series: Intro to Big Data & Data Lifecycle
79
Description
Description: John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility.
Description: John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility.
Details
When
Mon, Feb 10, 2020 - 11:00 am - 12:00 pm
Where
Online
Description: John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility. 2020-02-10 11:00:00 Online 0 Data Science Webinar Series: Reproducibility
81
Description
The Role of Generalist and Institutional Repositories to Enhance Data Discoverability and Reuse The primary goals of the workshop are to: Learn how generalist repositories see themselves in the larger biomedical data repository landscape. Understand how institutional data repositories are creating suites of solutions for their researchers and how they see generalist repositories fitting into this landscape. Consider desired characteristics of data repositories and how they relate to institutional expectations of data storage and preservation ...Read More
The Role of Generalist and Institutional Repositories to Enhance Data Discoverability and Reuse The primary goals of the workshop are to: Learn how generalist repositories see themselves in the larger biomedical data repository landscape. Understand how institutional data repositories are creating suites of solutions for their researchers and how they see generalist repositories fitting into this landscape. Consider desired characteristics of data repositories and how they relate to institutional expectations of data storage and preservation solutions. Explore adoption of common infrastructure, standards, and federated search solutions to enable greater discoverability of NIH research data across federated data repositories. Address the role of data curators in ensuring that data and metadata are sufficiently well curated to enhance discovery and enable reuse.
Details
When
Tue, Feb 11, 2020 - 9:00 am - 5:00 pm
Where
In-Person
The Role of Generalist and Institutional Repositories to Enhance Data Discoverability and Reuse The primary goals of the workshop are to: Learn how generalist repositories see themselves in the larger biomedical data repository landscape. Understand how institutional data repositories are creating suites of solutions for their researchers and how they see generalist repositories fitting into this landscape. Consider desired characteristics of data repositories and how they relate to institutional expectations of data storage and preservation solutions. Explore adoption of common infrastructure, standards, and federated search solutions to enable greater discoverability of NIH research data across federated data repositories. Address the role of data curators in ensuring that data and metadata are sufficiently well curated to enhance discovery and enable reuse. 2020-02-11 09:00:00 In-Person 0 Establishing a FAIR Biomedical Data Ecosystem
82
Description
John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility. This is a two day event. February 10th and 11th from 11:00AM to 12:00PM.
John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility. This is a two day event. February 10th and 11th from 11:00AM to 12:00PM.
Details
When
Tue, Feb 11, 2020 - 11:00 am - 12:00 pm
Where
Online
John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility. This is a two day event. February 10th and 11th from 11:00AM to 12:00PM. 2020-02-11 11:00:00 Online 0 Data Science Webinar Series: Reproducibility
83
Description
This two-day instructor-led course gives participants a broad study of networking options on Google Cloud Platform. Through presentations, demonstrations, and hands-on labs, learners explore and deploy GCP networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls, interconnection among networks, load balancing, Cloud DNS, and Cloud CDN. The course also covers common network design patterns and automated deployment using Cloud Deployment Manager. Objectives This course teaches participants the following skills: Configure Google VPC ...Read More
This two-day instructor-led course gives participants a broad study of networking options on Google Cloud Platform. Through presentations, demonstrations, and hands-on labs, learners explore and deploy GCP networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls, interconnection among networks, load balancing, Cloud DNS, and Cloud CDN. The course also covers common network design patterns and automated deployment using Cloud Deployment Manager. Objectives This course teaches participants the following skills: Configure Google VPC networks, subnets, and routers Control administrative access to VPC objects Control network access to endpoints in VPCs Interconnect networks among GCP projects Interconnect networks among GCP VPC networks and on-premises or other-cloud networks Choose among GCP load balancer and proxy options and configure them Use Cloud CDN to reduce latency and save money Optimize network spend using Network Tiers Deploy networks declaratively using Cloud Deployment Manager Design networks to meet common customer requirements Configure monitoring and logging to troubleshoot networks problems Audience This course is intended for the following participants: Network Engineers and Network Admins who are either using Google Cloud Platform or planning to do so. Individuals who want to be exposed to software-defined networking solutions in the cloud. Prerequisites To get the most out of this course, participants should have: Completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent experience. Clear understanding of the 7-layer OSI model. Clear understanding of IPv4 addressing. Prior experience with managing IPv4 routes. Course Outline Module 1: Google Cloud VPC Networking Fundamentals Topics Covered: Recall that networks belong to projects Explain the differences among default, auto, and custom networks Create networks and subnets Explain how IPv4 addresses are assigned to Compute Engine instances Publish domain names using Cloud DNS Create Compute Engine instances with IP aliases Create Compute Engine instances with multiple virtual network interfaces Module 2: Controlling Access to VPC Networks Topics Covered: Outline how IAM policies affect VPC networks Control access to network resources using service accounts Control access to Compute Engine instances with tag-based firewall rules Module 3: Sharing Networks Across Projects Topics Covered: Outline the overall workflow for configuring shared VPC Differentiate between the IAM roles that allow network resources to be managed Configure peering between unrelated VPC networks Recall when to use shared VPC and when to use VPC peering Module 4: Load Balancing Topics Covered: Recall the various load balancing services Configure Layer 7 HTTP(S) load balancing Whitelist and blacklist IP traffic with Cloud Armor Cache content with Cloud CDN Configure internal load balancing Determine which GCP load balancer to use when Module 5: Hybrid Connectivity Topics Covered: Recall the GCP interconnect and peering services available to connect your infrastructure to GCP Explain Dedicated Interconnect and Partner Interconnect Describe the workflow for configuring a Dedicated Interconnect Build a connection over a VPN with Cloud Router Determine which GCP interconnect service to use when Explain Direct Peering and Partner Peering Determine which GCP peering service to use when Module 6: Networking Pricing and Billing Topics Covered: Recognize how networking features are charged for Use Network Service Tiers to optimize spend Determine which Network Service Tier to use when Recall that labels can be used to understand networking spend Module 7: Network Design and Deployment Topics Covered: Explain common network design patterns Automate the deployment of networks using Deployment Manager Launch networking solutions using Cloud Marketplace Module 8: Network Monitoring and Troubleshooting Topics Covered: Configure uptime checks, alerting policies, and charts for your network services Use VPC Flow Logs to log and analyze network traffic behavior
Details
When
Wed, Feb 12, 2020 - 9:00 am - 5:00 pm
Where
In-Person
This two-day instructor-led course gives participants a broad study of networking options on Google Cloud Platform. Through presentations, demonstrations, and hands-on labs, learners explore and deploy GCP networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls, interconnection among networks, load balancing, Cloud DNS, and Cloud CDN. The course also covers common network design patterns and automated deployment using Cloud Deployment Manager. Objectives This course teaches participants the following skills: Configure Google VPC networks, subnets, and routers Control administrative access to VPC objects Control network access to endpoints in VPCs Interconnect networks among GCP projects Interconnect networks among GCP VPC networks and on-premises or other-cloud networks Choose among GCP load balancer and proxy options and configure them Use Cloud CDN to reduce latency and save money Optimize network spend using Network Tiers Deploy networks declaratively using Cloud Deployment Manager Design networks to meet common customer requirements Configure monitoring and logging to troubleshoot networks problems Audience This course is intended for the following participants: Network Engineers and Network Admins who are either using Google Cloud Platform or planning to do so. Individuals who want to be exposed to software-defined networking solutions in the cloud. Prerequisites To get the most out of this course, participants should have: Completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent experience. Clear understanding of the 7-layer OSI model. Clear understanding of IPv4 addressing. Prior experience with managing IPv4 routes. Course Outline Module 1: Google Cloud VPC Networking Fundamentals Topics Covered: Recall that networks belong to projects Explain the differences among default, auto, and custom networks Create networks and subnets Explain how IPv4 addresses are assigned to Compute Engine instances Publish domain names using Cloud DNS Create Compute Engine instances with IP aliases Create Compute Engine instances with multiple virtual network interfaces Module 2: Controlling Access to VPC Networks Topics Covered: Outline how IAM policies affect VPC networks Control access to network resources using service accounts Control access to Compute Engine instances with tag-based firewall rules Module 3: Sharing Networks Across Projects Topics Covered: Outline the overall workflow for configuring shared VPC Differentiate between the IAM roles that allow network resources to be managed Configure peering between unrelated VPC networks Recall when to use shared VPC and when to use VPC peering Module 4: Load Balancing Topics Covered: Recall the various load balancing services Configure Layer 7 HTTP(S) load balancing Whitelist and blacklist IP traffic with Cloud Armor Cache content with Cloud CDN Configure internal load balancing Determine which GCP load balancer to use when Module 5: Hybrid Connectivity Topics Covered: Recall the GCP interconnect and peering services available to connect your infrastructure to GCP Explain Dedicated Interconnect and Partner Interconnect Describe the workflow for configuring a Dedicated Interconnect Build a connection over a VPN with Cloud Router Determine which GCP interconnect service to use when Explain Direct Peering and Partner Peering Determine which GCP peering service to use when Module 6: Networking Pricing and Billing Topics Covered: Recognize how networking features are charged for Use Network Service Tiers to optimize spend Determine which Network Service Tier to use when Recall that labels can be used to understand networking spend Module 7: Network Design and Deployment Topics Covered: Explain common network design patterns Automate the deployment of networks using Deployment Manager Launch networking solutions using Cloud Marketplace Module 8: Network Monitoring and Troubleshooting Topics Covered: Configure uptime checks, alerting policies, and charts for your network services Use VPC Flow Logs to log and analyze network traffic behavior 2020-02-12 09:00:00 In-Person 0 Networking in Google Cloud Platform
84
Description
The Role of Generalist and Institutional Repositories to Enhance Data Discoverability and Reuse The primary goals of the workshop are to: Learn how generalist repositories see themselves in the larger biomedical data repository landscape. Understand how institutional data repositories are creating suites of solutions for their researchers and how they see generalist repositories fitting into this landscape. Consider desired characteristics of data repositories and how they relate to institutional expectations of data storage and preservation ...Read More
The Role of Generalist and Institutional Repositories to Enhance Data Discoverability and Reuse The primary goals of the workshop are to: Learn how generalist repositories see themselves in the larger biomedical data repository landscape. Understand how institutional data repositories are creating suites of solutions for their researchers and how they see generalist repositories fitting into this landscape. Consider desired characteristics of data repositories and how they relate to institutional expectations of data storage and preservation solutions. Explore adoption of common infrastructure, standards, and federated search solutions to enable greater discoverability of NIH research data across federated data repositories. Address the role of data curators in ensuring that data and metadata are sufficiently well curated to enhance discovery and enable reuse.
Details
When
Wed, Feb 12, 2020 - 9:00 am - 5:00 pm
Where
In-Person
The Role of Generalist and Institutional Repositories to Enhance Data Discoverability and Reuse The primary goals of the workshop are to: Learn how generalist repositories see themselves in the larger biomedical data repository landscape. Understand how institutional data repositories are creating suites of solutions for their researchers and how they see generalist repositories fitting into this landscape. Consider desired characteristics of data repositories and how they relate to institutional expectations of data storage and preservation solutions. Explore adoption of common infrastructure, standards, and federated search solutions to enable greater discoverability of NIH research data across federated data repositories. Address the role of data curators in ensuring that data and metadata are sufficiently well curated to enhance discovery and enable reuse. 2020-02-12 09:00:00 In-Person 0 Establishing a FAIR Biomedical Data Ecosystem
899
Description
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the theory behind RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression, volcano plots, differential expression of genes analysis, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Wed, Feb 12, 2020 - 3:00 pm - 5:00 pm
Where
Frederick Fort Detrick Building 549 Conference Room A
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the theory behind RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression, volcano plots, differential expression of genes analysis, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-02-12 15:00:00 Frederick Fort Detrick Building 549 Conference Room A In-Person Joshua Meyer (CCBR) BTEP 0 RNA-Seq: Analysis on the Palantir Platform (Feb. 12th, Frederick)
94
Description
John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility.
John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility.
Details
When
Thu, Feb 13, 2020 - 11:00 am - 12:00 pm
Where
Online
John Ioannidis from Stanford University discusses what reproducibility means, separating reproducibility of methods, of results, and of inferences. He also provides an overview for empirical data on reproducibility across different scientific disciplines and discusses different methods and practices that have been proposed to improve reproducibility. 2020-02-13 11:00:00 Online 0 Data Science Webinar Series: Reproducibility
95
Description
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research.
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research.
Details
When
Tue, Feb 18, 2020 - 11:00 am - 12:00 pm
Where
Online
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research. 2020-02-18 11:00:00 Online 0 Data Science Webinar Series: Open Science
900
Description
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are ...Read More
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset. Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Tue, Feb 18, 2020 - 1:00 pm - 3:00 pm
Where
Bethesda Building 10 FAES Classroom #6 (B1C208)
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset. Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-02-18 13:00:00 Bethesda Building 10 FAES Classroom #6 (B1C208) In-Person Joshua Meyer (CCBR) BTEP 0 Single-Cell RNA-Seq: Analysis on the Palantir Platform (Feb 18th, Bethesda)
96
Description
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research.
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research.
Details
When
Wed, Feb 19, 2020 - 11:00 am - 12:00 pm
Where
Online
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research. 2020-02-19 11:00:00 Online 0 Data Science Webinar Series: Open Science
915
Description
Learn how to easily analyze your gene expression data yourself - using Qlucore Omics Explorer NCI/CCR:To get access to Qlucore, put a request into NCI at Your Service under Get Help https://service.cancer.gov/Get Help Qlucore empowers bench scientists to easily visualize and analyze large numerical data sets such as gene expression (array and RNA-seq), DNA methylation, ...Read More
Learn how to easily analyze your gene expression data yourself - using Qlucore Omics Explorer NCI/CCR:To get access to Qlucore, put a request into NCI at Your Service under Get Help https://service.cancer.gov/Get Help Qlucore empowers bench scientists to easily visualize and analyze large numerical data sets such as gene expression (array and RNA-seq), DNA methylation, miRNA, Proteomics, Metabolomics and Flow Cytometry data. No scripting, tables or complex settings, you will have instant visual feedback on complex calculations, interactive plots, and integration with GSEA. Morning session (10AM - noon) – Introduction and live basic hands-on training for new users , bring your government-issued laptops so you can get on the network and follow along, we will provide the license access) Qlucore Omics Explorer is an interactive analysis and visualization tool that helps the user to find groups, structures, variable networks and discriminating variables. You can use your own data and also analyze public data. Omics Explorer supports 3D PCA plots, t-SNE plots, Heat maps with hierarchical clustering, Scatter plots, Volcano plots, Box plots etc. A number of statistical methods are included, like t-test, F-test (ANOVA) and different regressions tests. Plots are dynamically updated as filters change and analysis is done, which allows a very interactive exploration to test out new hypothesizes. Omics Explorer has an interface which makes it possible to use statistical functions created in "R" from inside Omics Explorer, so that you can use external statistical methods implemented in “R” like Limma/voom, Welch, Mann-Whitney, Kruskal-Wallis and also add you own "R" scripts. With machine learning algorithms (kNN, SVM and Random Trees) classification of samples in new data sets is simplified. The product has an inbuilt Gene Set Enrichment Analysis (GSEA) workbench for pathway analysis, a direct interface to GEO and an inbuilt Gene Ontology browser. The product has an import wizard to simplify data import. Aligned BAM files (RNA-seq) and microarray files (.cel files) can be directly imported, normalized and log transformed through import pipelines, and GEO soft files can be directly imported. Trough Qlucore Templates, based on “Python”, you can create scripts of commands that are executed by Qlucore Omics Explorer. You can create standardized analysis templates for standard analysis, and the integration with Python also opens up possibilities for customization. As an example, there is a TCGA mRNA dataset download Template that comes preinstalled. Qlucore NGS module is an optional add-on module to Qlucore Omics Explorer. The main components of the NGS module are an interactive and fast Genome Browser for many samples and many tracks per sample, a genome filter control component, a Project Manager for project set-up and a built in Variant Caller for short indels and variants. The options available for RNA-seq analysis really stand out. Utilizing the existing functionality in Qlucore Omics Explorer for expression data and combining it with the new functionality in Qlucore NGS Browser enables significantly increased analysis options. The Genome Browser content is dynamically updated when filters and filter cut-off are changed using sliders and check-boxes. If you are unable to attend in person, WebEx will be provided: https://cbiit.webex.com/cbiit/j.php?MTID=mfdae6115f8953fb2aeb00ff9c64e7b6f    
Register
Organizer
BTEP
When
Thu, Feb 20, 2020 - 10:00 am - 12:00 pm
Where
Building 37 Room 4041/4107
Learn how to easily analyze your gene expression data yourself - using Qlucore Omics Explorer NCI/CCR:To get access to Qlucore, put a request into NCI at Your Service under Get Help https://service.cancer.gov/Get Help Qlucore empowers bench scientists to easily visualize and analyze large numerical data sets such as gene expression (array and RNA-seq), DNA methylation, miRNA, Proteomics, Metabolomics and Flow Cytometry data. No scripting, tables or complex settings, you will have instant visual feedback on complex calculations, interactive plots, and integration with GSEA. Morning session (10AM - noon) – Introduction and live basic hands-on training for new users , bring your government-issued laptops so you can get on the network and follow along, we will provide the license access) Qlucore Omics Explorer is an interactive analysis and visualization tool that helps the user to find groups, structures, variable networks and discriminating variables. You can use your own data and also analyze public data. Omics Explorer supports 3D PCA plots, t-SNE plots, Heat maps with hierarchical clustering, Scatter plots, Volcano plots, Box plots etc. A number of statistical methods are included, like t-test, F-test (ANOVA) and different regressions tests. Plots are dynamically updated as filters change and analysis is done, which allows a very interactive exploration to test out new hypothesizes. Omics Explorer has an interface which makes it possible to use statistical functions created in "R" from inside Omics Explorer, so that you can use external statistical methods implemented in “R” like Limma/voom, Welch, Mann-Whitney, Kruskal-Wallis and also add you own "R" scripts. With machine learning algorithms (kNN, SVM and Random Trees) classification of samples in new data sets is simplified. The product has an inbuilt Gene Set Enrichment Analysis (GSEA) workbench for pathway analysis, a direct interface to GEO and an inbuilt Gene Ontology browser. The product has an import wizard to simplify data import. Aligned BAM files (RNA-seq) and microarray files (.cel files) can be directly imported, normalized and log transformed through import pipelines, and GEO soft files can be directly imported. Trough Qlucore Templates, based on “Python”, you can create scripts of commands that are executed by Qlucore Omics Explorer. You can create standardized analysis templates for standard analysis, and the integration with Python also opens up possibilities for customization. As an example, there is a TCGA mRNA dataset download Template that comes preinstalled. Qlucore NGS module is an optional add-on module to Qlucore Omics Explorer. The main components of the NGS module are an interactive and fast Genome Browser for many samples and many tracks per sample, a genome filter control component, a Project Manager for project set-up and a built in Variant Caller for short indels and variants. The options available for RNA-seq analysis really stand out. Utilizing the existing functionality in Qlucore Omics Explorer for expression data and combining it with the new functionality in Qlucore NGS Browser enables significantly increased analysis options. The Genome Browser content is dynamically updated when filters and filter cut-off are changed using sliders and check-boxes. If you are unable to attend in person, WebEx will be provided: https://cbiit.webex.com/cbiit/j.php?MTID=mfdae6115f8953fb2aeb00ff9c64e7b6f     2020-02-20 10:00:00 Building 37 Room 4041/4107 In-Person Yana Stackpole (Qlucore) BTEP 0 Qlucore Omics Explorer: Learn how to easily analyze your gene expression data yourself
97
Description
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research.
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research.
Details
When
Thu, Feb 20, 2020 - 11:00 am - 12:00 pm
Where
Online
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research. 2020-02-20 11:00:00 Online 0 Data Science Webinar Series: Open Science
98
Description
Wolfgang Resch/ Biowulf
Wolfgang Resch/ Biowulf
Details
When
Thu, Feb 20, 2020 - 12:00 pm - 1:00 pm
Where
In-Person
Wolfgang Resch/ Biowulf 2020-02-20 12:00:00 In-Person 0 BYOB: Optimizing Python Code
916
Description
Attendance at this hands-on workshop will be limited to 20 people. NCI/CCR: To get access to Qlucore, put a request into NCI at Your Service under Get Help https://service.cancer.gov/Get Help Afternoon session (1-3PM) – hands-on training session using gene expression data, more advanced, and  for anyone interested  to learn how Qlucore can help them with gene expression data, and to get hands-on ...Read More
Attendance at this hands-on workshop will be limited to 20 people. NCI/CCR: To get access to Qlucore, put a request into NCI at Your Service under Get Help https://service.cancer.gov/Get Help Afternoon session (1-3PM) – hands-on training session using gene expression data, more advanced, and  for anyone interested  to learn how Qlucore can help them with gene expression data, and to get hands-on experience with plots commonly visualization and analysis tools (bring your laptops so you can follow along, we will provide the license access) The training includes the following hands-on exercises (bring your laptops - must be a govt-issued laptop to get on the network):
  1. Import data
  2. Getting around user interface
  3. Building and configuring Heatmaps, PCA, Box plot, Volcano plot, Venn diagram
  4. Statistical tests in GUI (two group, multi group, regressions)
  5. Saving results
  6. Exploratory data analysis
  7. Functional analysis using GSEA and integration with other knowledge bases
  8. Clustering
  9. Machine learning
Due to the hands-on aspect of this class, WebEx will not be provided.
Register
Organizer
BTEP
When
Thu, Feb 20, 2020 - 1:00 pm - 3:00 pm
Where
Building 37 Room 4041/4107
Attendance at this hands-on workshop will be limited to 20 people. NCI/CCR: To get access to Qlucore, put a request into NCI at Your Service under Get Help https://service.cancer.gov/Get Help Afternoon session (1-3PM) – hands-on training session using gene expression data, more advanced, and  for anyone interested  to learn how Qlucore can help them with gene expression data, and to get hands-on experience with plots commonly visualization and analysis tools (bring your laptops so you can follow along, we will provide the license access) The training includes the following hands-on exercises (bring your laptops - must be a govt-issued laptop to get on the network): Import data Getting around user interface Building and configuring Heatmaps, PCA, Box plot, Volcano plot, Venn diagram Statistical tests in GUI (two group, multi group, regressions) Saving results Exploratory data analysis Functional analysis using GSEA and integration with other knowledge bases Clustering Machine learning Due to the hands-on aspect of this class, WebEx will not be provided. 2020-02-20 13:00:00 Building 37 Room 4041/4107 In-Person Yana Stackpole (Qlucore) BTEP 0 Qlucore Omics Explorer Hands-on Workshop
99
Description
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research.
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research.
Details
When
Fri, Feb 21, 2020 - 11:00 am - 12:00 pm
Where
Online
Brian Nosek, Executive Director and Co-Founder of Center for Open Science (COS) and Professor of University of Virginia, discusses barriers to open science and methods for improving openness and reproducibility in scientific research. 2020-02-21 11:00:00 Online 0 Data Science Webinar Series: Open Science
100
Description
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics.
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics.
Details
When
Mon, Feb 24, 2020 - 11:00 am - 12:00 pm
Where
Online
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics. 2020-02-24 11:00:00 Online 0 Data Science Webinar Series: Why Data Sharing & Reuse are Hard to Do
101
Description
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics.
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics.
Details
When
Tue, Feb 25, 2020 - 11:00 am - 12:00 pm
Where
Online
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics. 2020-02-25 11:00:00 Online 0 Data Science Webinar Series: Why Data Sharing & Reuse are Hard to Do
102
Description
The life sciences are in the midst of a data revolution. Inexpensive and accurate genome sequencing is a reality, advanced imaging is routine, and clinical data is increasingly stored in electronic form. In principle, these advances have brought us to the threshold of a new era in medicine, one where the data sciences hold the potential to propel our understanding and treatment of disease. In practice, we are stymied by the operational challenges associated with ...Read More
The life sciences are in the midst of a data revolution. Inexpensive and accurate genome sequencing is a reality, advanced imaging is routine, and clinical data is increasingly stored in electronic form. In principle, these advances have brought us to the threshold of a new era in medicine, one where the data sciences hold the potential to propel our understanding and treatment of disease. In practice, we are stymied by the operational challenges associated with storing, sharing, and analyzing genomic and clinical data at scale. In this talk, I will overview Broad's efforts at building a data platform to address these unmet needs by 1) building patient-facing software, 2) performing data engineering, 3) creating machine learning tools, and 4) building a cloud-based researcher environment (Terra). I will also overview flagship applications in precision medicine, infectious disease surveillance, and clinical trial design. Dr. Anthony Philippakis, M.D., Ph.D., Chief Data Officer of the Broad Institute, is presenting on efforts to facilitate storing, sharing, and analyzing genomic and clinical data in a scalable manner. He is the lead PI on the NHGRI-funded AnVIL project, through which our team has been able to participate in a pilot for the NIH-WRNMMC symposium, and have seen firsthand its potential for its use facilitating collaborative research. Dr. Philippakis is a highly engaging speaker given his unique expertise in clinical research as well as in informatics. The presentation will also be available to stream over NIH videocast. For more information, contact Sarah Weber (sarah.weber@nih.gov)
Details
When
Tue, Feb 25, 2020 - 12:00 pm - 1:00 pm
Where
In-Person
The life sciences are in the midst of a data revolution. Inexpensive and accurate genome sequencing is a reality, advanced imaging is routine, and clinical data is increasingly stored in electronic form. In principle, these advances have brought us to the threshold of a new era in medicine, one where the data sciences hold the potential to propel our understanding and treatment of disease. In practice, we are stymied by the operational challenges associated with storing, sharing, and analyzing genomic and clinical data at scale. In this talk, I will overview Broad's efforts at building a data platform to address these unmet needs by 1) building patient-facing software, 2) performing data engineering, 3) creating machine learning tools, and 4) building a cloud-based researcher environment (Terra). I will also overview flagship applications in precision medicine, infectious disease surveillance, and clinical trial design. Dr. Anthony Philippakis, M.D., Ph.D., Chief Data Officer of the Broad Institute, is presenting on efforts to facilitate storing, sharing, and analyzing genomic and clinical data in a scalable manner. He is the lead PI on the NHGRI-funded AnVIL project, through which our team has been able to participate in a pilot for the NIH-WRNMMC symposium, and have seen firsthand its potential for its use facilitating collaborative research. Dr. Philippakis is a highly engaging speaker given his unique expertise in clinical research as well as in informatics. The presentation will also be available to stream over NIH videocast. For more information, contact Sarah Weber (sarah.weber@nih.gov) 2020-02-25 12:00:00 In-Person 0 A Data Platform for Public Health
914
Description
THIS EVENT HAS BEEN CANCELLED
This class is intended for researchers who would like to learn the steps of bulk RNA-Seq data analysis on the NIH Unix cluster Biowulf. You do not need to have a current Biowulf account to attend this class. Beginners and intermediate users will benefit from this class. Please bring your government-issued computer to log onto the NIH network. Before class...please download a tool to move files: For Windows ...Read More
THIS EVENT HAS BEEN CANCELLED
This class is intended for researchers who would like to learn the steps of bulk RNA-Seq data analysis on the NIH Unix cluster Biowulf. You do not need to have a current Biowulf account to attend this class. Beginners and intermediate users will benefit from this class. Please bring your government-issued computer to log onto the NIH network. Before class...please download a tool to move files: For Windows PC, suggested tool is WinScp. For Mac, suggested tool is Filezilla. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html (Go to the section "Alternative Binary Files" and download 64 bit putty.exe)
Register
Organizer
BTEP
When
Tue, Feb 25, 2020 - 1:00 pm - 3:00 pm
Where
NIH Bldg 37, 5th floor vestibule
THIS EVENT HAS BEEN CANCELLEDThis class is intended for researchers who would like to learn the steps of bulk RNA-Seq data analysis on the NIH Unix cluster Biowulf. You do not need to have a current Biowulf account to attend this class. Beginners and intermediate users will benefit from this class. Please bring your government-issued computer to log onto the NIH network. Before class...please download a tool to move files: For Windows PC, suggested tool is WinScp. For Mac, suggested tool is Filezilla. For Windows PC, you will also need to download and install PuTTY from    https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html (Go to the section "Alternative Binary Files" and download 64 bit putty.exe) 2020-02-25 13:00:00 NIH Bldg 37,5th floor vestibule In-Person Amy Stonelake (BTEP) BTEP 0 Hands-on Drop-in Session: Unix/Biowulf and bulk RNA-Seq - CANCELLED
901
Description
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are ...Read More
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset. Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Wed, Feb 26, 2020 - 3:00 pm - 5:00 pm
Where
Frederick Fort Detrick Building 549 Conference Room A
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset. Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-02-26 15:00:00 Frederick Fort Detrick Building 549 Conference Room A In-Person Joshua Meyer (CCBR) BTEP 0 Single-Cell RNA-Seq: Analysis on the Palantir Platform (Feb. 26th, Frederick)
103
Description
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics.
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics.
Details
When
Thu, Feb 27, 2020 - 11:00 am - 12:00 pm
Where
Online
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics. 2020-02-27 11:00:00 Online 0 Data Science Webinar Series: Why Data Sharing & Reuse are Hard to Do
104
Description
In this symposium, distinguished scientists will share recent successes in their research to visualize RNA targets at the single molecule level with single cell resolution and morphological context. Here are some of the exciting topics that will be covered in the presentations: Visualize the cellular heterogeneity of complex organs with the multiplexing and spatial capabilities of the RNAscope™ technology Spatially map scRNA-seq gene profiles at the single cell level in the tissue context Localize genes ...Read More
In this symposium, distinguished scientists will share recent successes in their research to visualize RNA targets at the single molecule level with single cell resolution and morphological context. Here are some of the exciting topics that will be covered in the presentations: Visualize the cellular heterogeneity of complex organs with the multiplexing and spatial capabilities of the RNAscope™ technology Spatially map scRNA-seq gene profiles at the single cell level in the tissue context Localize genes of interest and cell subtype markers Conserve precious samples by interrogating up to 12 targets on a single slide. Connie Zhang, PhD. Account Executive Advanced Cell Diagnostics Presentation Title: Spatial Mapping of Gene Expression at Single Cell Resolution: Applications of the RNAscope Larry Sternberg, Ph.D. Senior Principal Scientist Pathology/Histotechnology Laboratory (PHL), NIH, NCI Presentation Title: Tissue-based Gene Expression Assay Support: Practical Considerations Bring a colleague and join us to learn why the RNAscope technology is being incorporated into scientists’ workflows as a trusted RNA detection method to accelerate their research.
Details
When
Thu, Feb 27, 2020 - 12:30 pm - 1:30 pm
Where
In-Person
In this symposium, distinguished scientists will share recent successes in their research to visualize RNA targets at the single molecule level with single cell resolution and morphological context. Here are some of the exciting topics that will be covered in the presentations: Visualize the cellular heterogeneity of complex organs with the multiplexing and spatial capabilities of the RNAscope™ technology Spatially map scRNA-seq gene profiles at the single cell level in the tissue context Localize genes of interest and cell subtype markers Conserve precious samples by interrogating up to 12 targets on a single slide. Connie Zhang, PhD. Account Executive Advanced Cell Diagnostics Presentation Title: Spatial Mapping of Gene Expression at Single Cell Resolution: Applications of the RNAscope Larry Sternberg, Ph.D. Senior Principal Scientist Pathology/Histotechnology Laboratory (PHL), NIH, NCI Presentation Title: Tissue-based Gene Expression Assay Support: Practical Considerations Bring a colleague and join us to learn why the RNAscope technology is being incorporated into scientists’ workflows as a trusted RNA detection method to accelerate their research. 2020-02-27 12:30:00 In-Person 0 ACD Spatial Genomics Seminar
896
Description
The Annotation, Visualization and Impact Analysis (AVIA) is an application developed to guide, prioritize and summarize genomic variants. AVIA maintains and aggregates dozens of publicly available variant annotation databases and predictions from impact analysis algorithms, allowing users to investigate functional significance of their genetic alterations across samples, genes, and pathways. This demonstration will cover:
  • Variant annotation and impact analysis
  •  Variant representation formats and standards
  • Analyze variants using ...Read More
The Annotation, Visualization and Impact Analysis (AVIA) is an application developed to guide, prioritize and summarize genomic variants. AVIA maintains and aggregates dozens of publicly available variant annotation databases and predictions from impact analysis algorithms, allowing users to investigate functional significance of their genetic alterations across samples, genes, and pathways. This demonstration will cover:
  • Variant annotation and impact analysis
  •  Variant representation formats and standards
  • Analyze variants using AVIA
    • Submit variant lists
    • Understand annotation categories
    • Include or exclude variants based on
      • genes of interest
      • genic features - intronic, exotic, splice
      • public databases - 1000 genomes, TCGA, ClinVar
      • region features - repeat regions, mappability
      • custom annotations
    • Impact analysis
      • levels of impact - pathogenic to benign
      • prediction algorithms on pathogenicity
      • clinical annotations from sources such as ClinVar
    • Variant, gene, protein and pathway analysis
    • View related literature
    • Overview profiles
      • variant landscape using vcf.iobio
      • gene variant profile using gene.iobio
      • frequency of variant occurrence by genes and samples
    • Comparative Analysis
      • overlap with public data sources such as TCGA
      • assess type of damage and compare with cancer mutational profiles using SAMM
      • between sample comparisons
    • Share data with collaborators
    • Reannotate - new genome versions or new samples
  Link to WebEx recording: https://cbiit.webex.com/cbiit/ldr.php?RCID=838671822befaaf2bf504f85a5fb897b            
Register
Organizer
BTEP
When
Thu, Feb 27, 2020 - 1:00 pm - 3:00 pm
Where
Building 37 Room 4041/4107
The Annotation, Visualization and Impact Analysis (AVIA) is an application developed to guide, prioritize and summarize genomic variants. AVIA maintains and aggregates dozens of publicly available variant annotation databases and predictions from impact analysis algorithms, allowing users to investigate functional significance of their genetic alterations across samples, genes, and pathways. This demonstration will cover: Variant annotation and impact analysis  Variant representation formats and standards Analyze variants using AVIA Submit variant lists Understand annotation categories Include or exclude variants based on genes of interest genic features - intronic, exotic, splice public databases - 1000 genomes, TCGA, ClinVar region features - repeat regions, mappability custom annotations Impact analysis levels of impact - pathogenic to benign prediction algorithms on pathogenicity clinical annotations from sources such as ClinVar Variant, gene, protein and pathway analysis View related literature Overview profiles variant landscape using vcf.iobio gene variant profile using gene.iobio frequency of variant occurrence by genes and samples Comparative Analysis overlap with public data sources such as TCGA assess type of damage and compare with cancer mutational profiles using SAMM between sample comparisons Share data with collaborators Reannotate - new genome versions or new samples   Link to WebEx recording: https://cbiit.webex.com/cbiit/ldr.php?RCID=838671822befaaf2bf504f85a5fb897b             2020-02-27 13:00:00 Building 37 Room 4041/4107 In-Person Uma Mudunuri (NCI/OD-F/Advanced Biomedical Computational Science),Hue Reardon (NCI/OD-F/Advanced Biomedical Computational Science), BTEP 0 Variant Annotation, Visualization and Impact Analysis using AVIA
105
Description
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics.
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics.
Details
When
Fri, Feb 28, 2020 - 11:00 am - 12:00 pm
Where
Online
Distinguished Professor Dr. Christine Borgman and Ph. D Candidate Irene Pasquetto of UCLA Informatics Studies compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics. 2020-02-28 11:00:00 Online 0 Data Science Webinar Series: Why Data Sharing & Reuse are Hard to Do
106
Description
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/e688e94d71934c4183480167b05f0f67 The CIViC knowledgebase (www.civicdb.org) is an open access, open source, community-driven resource for Clinical Interpretation of Variants in Cancer. The goal of CIViC is to enable precision medicine by providing an educational forum for dissemination of knowledge and ...Read More
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/e688e94d71934c4183480167b05f0f67 The CIViC knowledgebase (www.civicdb.org) is an open access, open source, community-driven resource for Clinical Interpretation of Variants in Cancer. The goal of CIViC is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. The knowledgebase leverages public curation and expert moderation to create a free resource that compiles, annotates, and distributes published cancer variant knowledge to the community. Identifying and cataloging curated clinical variant knowledge in the peer-reviewed published literature for rapid searching, evaluation, dissemination, and integration into other resources enables clinical cancer variant analysis. The database can be accessed without restrictions through a user-friendly interface, via a flexible public API, or the CIViCpy Software Development Kit. All data is available without login, but curation and other functions such as commenting, flagging, and suggesting changes requires users to create a free CIViC account. The history of all curation and revision in CIViC is viewable on the web interface. Selected, expert editors review and revise submitted content, which is labeled as accepted after complete moderation. All content in CIViC adheres to a structured data model, which incorporates ontologies, standards and guidelines from across the field to promote interoperability and compatibility with other efforts. CIViC currently has a community of over 190 curators and 16,000 clinical and research users around the world. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://itcr.cancergov/ For any questions please contact Daoud Meerzaman or Juli Klemm
Details
Organizer
CBIIT
When
Fri, Feb 28, 2020 - 12:30 pm - 1:30 pm
Where
Online
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/e688e94d71934c4183480167b05f0f67 The CIViC knowledgebase (www.civicdb.org) is an open access, open source, community-driven resource for Clinical Interpretation of Variants in Cancer. The goal of CIViC is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. The knowledgebase leverages public curation and expert moderation to create a free resource that compiles, annotates, and distributes published cancer variant knowledge to the community. Identifying and cataloging curated clinical variant knowledge in the peer-reviewed published literature for rapid searching, evaluation, dissemination, and integration into other resources enables clinical cancer variant analysis. The database can be accessed without restrictions through a user-friendly interface, via a flexible public API, or the CIViCpy Software Development Kit. All data is available without login, but curation and other functions such as commenting, flagging, and suggesting changes requires users to create a free CIViC account. The history of all curation and revision in CIViC is viewable on the web interface. Selected, expert editors review and revise submitted content, which is labeled as accepted after complete moderation. All content in CIViC adheres to a structured data model, which incorporates ontologies, standards and guidelines from across the field to promote interoperability and compatibility with other efforts. CIViC currently has a community of over 190 curators and 16,000 clinical and research users around the world. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://itcr.cancergov/ For any questions please contact Daoud Meerzaman or Juli Klemm 2020-02-28 12:30:00 Online Online CBIIT 0 Clinical Interpretation of Variants in Cancer (CIViC) Knowledgebase
253 https://cbiit.webex.com/recordingservice/sites/cbiit/recording/e688e94d71934c4183480167b05f0f67/playback 2020-02-28 14:00:00 In-Person 0 Clinical Interpretation of Variants in Cancer (CIViC) Knowledgebase
107
Description
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index
Details
When
Mon, Mar 02, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index 2020-03-02 11:00:00 Online 0 Data Science Webinar Series: Finding & Accessing Datasets, Indexing & Identifiers
108
Description
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index
Details
When
Tue, Mar 03, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index 2020-03-03 11:00:00 Online 0 Data Science Webinar Series: Finding & Accessing Datasets, Indexing & Identifiers
902
Description
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the theory behind RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression, volcano plots, differential expression of genes analysis, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Tue, Mar 03, 2020 - 1:00 pm - 3:00 pm
Where
Bethesda, Building 10, FAES Classroom #4 (B1C205)
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the theory behind RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression, volcano plots, differential expression of genes analysis, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-03-03 13:00:00 Bethesda, Building 10, FAES Classroom #4 (B1C205) Online Joshua Meyer (CCBR) BTEP 0 RNA-Seq: Analysis on the Palantir Platform (Mar. 3rd, Bethesda)
109
Description
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index
Details
When
Thu, Mar 05, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index 2020-03-05 11:00:00 Online 0 Data Science Webinar Series: Finding & Accessing Datasets, Indexing & Identifiers
110
Description
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index
Details
When
Fri, Mar 06, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Lucila Ohno-Machado from the University of California San Diego discusses the development of the biomedical data discovery index 2020-03-06 11:00:00 Online 0 Data Science Webinar Series: Finding & Accessing Datasets, Indexing & Identifiers
111
Description
The NIH Office of Data Science Strategy (ODSS) is presenting NIH Figshare for NIH Intramural Researchers: A Generalist Repository for Data Sharing on March 6 at 12:00 p.m. This one-hour webinar will explore how NIH intramural researchers can use the NIH Figshare instance to quickly and easily share research products, including: datasets, code, and multimedia files to make their work more discoverable, reproducible, reusable, and impactful. Register at: bit.ly/NIH-Figshare-webinar Learn more about the NIH ...Read More
The NIH Office of Data Science Strategy (ODSS) is presenting NIH Figshare for NIH Intramural Researchers: A Generalist Repository for Data Sharing on March 6 at 12:00 p.m. This one-hour webinar will explore how NIH intramural researchers can use the NIH Figshare instance to quickly and easily share research products, including: datasets, code, and multimedia files to make their work more discoverable, reproducible, reusable, and impactful. Register at: bit.ly/NIH-Figshare-webinar Learn more about the NIH Figshare instance, a pilot project with the generalist repository Figshare available for all NIH-funded researchers to share research data. For questions related to the NIH Figshare instance, please email ODSS at datascience@nih.gov.
Details
When
Fri, Mar 06, 2020 - 12:00 pm - 1:00 pm
Where
Online
The NIH Office of Data Science Strategy (ODSS) is presenting NIH Figshare for NIH Intramural Researchers: A Generalist Repository for Data Sharing on March 6 at 12:00 p.m. This one-hour webinar will explore how NIH intramural researchers can use the NIH Figshare instance to quickly and easily share research products, including: datasets, code, and multimedia files to make their work more discoverable, reproducible, reusable, and impactful. Register at: bit.ly/NIH-Figshare-webinar Learn more about the NIH Figshare instance, a pilot project with the generalist repository Figshare available for all NIH-funded researchers to share research data. For questions related to the NIH Figshare instance, please email ODSS at datascience@nih.gov. 2020-03-06 12:00:00 Online 0 NIH Figshare for Intramural Investigators
112
Description
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas.
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas.
Details
When
Mon, Mar 09, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas. 2020-03-09 11:00:00 Online 0 Data Science Webinar Series: Data Workflows & Pipelines
113
Description
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas.
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas.
Details
When
Tue, Mar 10, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas. 2020-03-10 11:00:00 Online 0 Data Science Webinar Series: Data Workflows & Pipelines
114
Description
MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. This workshop will review two cases: the first will investigate how infiltrating myeloid derived suppressor cells could be helping tumor cells evade the immune system and the second will review the analysis of RNA-seq, metabolomic, and proteomics data to discover potential disease mechanisms and biomarkers in patients with sepsis. For questions please contact Daoud Meerzaman
MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. This workshop will review two cases: the first will investigate how infiltrating myeloid derived suppressor cells could be helping tumor cells evade the immune system and the second will review the analysis of RNA-seq, metabolomic, and proteomics data to discover potential disease mechanisms and biomarkers in patients with sepsis. For questions please contact Daoud Meerzaman
Details
Organizer
CBIIT
When
Tue, Mar 10, 2020 - 11:00 am - 12:00 pm
Where
Online
MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. This workshop will review two cases: the first will investigate how infiltrating myeloid derived suppressor cells could be helping tumor cells evade the immune system and the second will review the analysis of RNA-seq, metabolomic, and proteomics data to discover potential disease mechanisms and biomarkers in patients with sepsis. For questions please contact Daoud Meerzaman 2020-03-10 11:00:00 Online CBIIT 0 Introduction to MetaCore
1
Description
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use ...Read More
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. This is a hands-on course with 24 laptops available in the library’s classroom. If you prefer to work from your own laptop, feel free to install the IGV browser in advance from here - software.broadinstitute.org/software/igv/download(the UCSV browser is web browser based).
Details
Organizer
NIH Training Library
When
Tue, Mar 10, 2020 - 1:00 pm - 3:30 pm
Where
Bethesda, Bldg 10
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. This is a hands-on course with 24 laptops available in the library’s classroom. If you prefer to work from your own laptop, feel free to install the IGV browser in advance from here - software.broadinstitute.org/software/igv/download(the UCSV browser is web browser based). 2020-03-10 13:00:00 Bethesda, Bldg 10 In-Person NIH Training Library 0 NIH Library: Genome Browsers
115
Description
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use ...Read More
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. This is a hands-on course with 24 laptops available in the library’s classroom. If you prefer to work from your own laptop, feel free to install the IGV browser in advance from here - software.broadinstitute.org/software/igv/download(the UCSV browser is web browser based).
Details
Organizer
NIH Training Library
When
Tue, Mar 10, 2020 - 1:00 pm - 3:30 pm
Where
In-Person
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. This is a hands-on course with 24 laptops available in the library’s classroom. If you prefer to work from your own laptop, feel free to install the IGV browser in advance from here - software.broadinstitute.org/software/igv/download(the UCSV browser is web browser based). 2020-03-10 13:00:00 In-Person NIH Training Library 0 NIH Library: Genome Browsers
116
Description
https://hpc.nih.gov/training/handouts/DL_by_Example4_20200312.pdf https://hpc.nih.gov/apps/biogans.html https://hpc.nih.gov/training/handouts/DL_Assignments4_20200312.pdf This introductory course will teach the basics of deep learning and of different types of deep learning networks through a set of hands-on biological examples implemented in Keras, one example per class. Each class is stand-alone. Class #4 will focus on the Generative Adversarial Networks (GANs) and their application to biological data synthesis. Expected knowledge: Basic Python, Basic Linux/Unix This class is part of a series, but each class is stand-alone.
Details
Organizer
HPC Biowulf
When
Thu, Mar 12, 2020 - 9:30 am - 12:00 pm
Where
Online
https://hpc.nih.gov/training/handouts/DL_by_Example4_20200312.pdf https://hpc.nih.gov/apps/biogans.html https://hpc.nih.gov/training/handouts/DL_Assignments4_20200312.pdf This introductory course will teach the basics of deep learning and of different types of deep learning networks through a set of hands-on biological examples implemented in Keras, one example per class. Each class is stand-alone. Class #4 will focus on the Generative Adversarial Networks (GANs) and their application to biological data synthesis. Expected knowledge: Basic Python, Basic Linux/Unix This class is part of a series, but each class is stand-alone. 2020-03-12 09:30:00 Online In-Person HPC Biowulf 0 Deep Learning by Example on Biowulf
117
Description
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas.
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas.
Details
When
Thu, Mar 12, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas. 2020-03-12 11:00:00 Online 0 Data Science Webinar Series: Data Workflows & Pipelines
118
Description
On Friday, March 13, NIH’s Office of Data Science Strategy will host “Friday Before Pi Day,” a Friday-the-13th-inspired Pi Day celebration highlighting data science at NIH. The day’s events will include two opportunities to participate in friendly, Pi-themed presentation sessions: Scary stories about data and the lessons we can learn from them As part of the festivities, we’ll have a session dedicated to data horror stories: times we’ve gotten file names ...Read More
On Friday, March 13, NIH’s Office of Data Science Strategy will host “Friday Before Pi Day,” a Friday-the-13th-inspired Pi Day celebration highlighting data science at NIH. The day’s events will include two opportunities to participate in friendly, Pi-themed presentation sessions: Scary stories about data and the lessons we can learn from them As part of the festivities, we’ll have a session dedicated to data horror stories: times we’ve gotten file names confused, wrestled with pesky bugs in our code, toiled over data formats, or lost a precious dataset. So, we want to hear from you! Send us your data horror story ideas and the lessons you’ve learned from them. Please limit all pitches to one paragraph. We’ll select 10 and work with you to develop your stories into engaging and entertaining 10-minute talks. Pi-tionary: 3:14-minute illustrated lightning talks This session will give teams or individuals the opportunity to share an illustrated elevator pitch for their lab’s or office’s work. Competitors will describe what they do in lay terms while sketching it out on the whiteboard. Your drawing doesn’t have to be a data visualization; the figure can be a simple pictorial representation of your project or office mission. If you or your team would like to showcase your work at NIH, please submit your title, ICO, and participant names. Contestants will compete against other teams in front of judges from the Pi Day audience, and the winning team will take home a pie! If you are interested in participating in either activity, please send your submissions by COB Thursday, February 13, to maryam.zaringhalam@nih.gov, indicating which session(s) you’d like to take part in. In addition to these presentations, the day’s activities will feature a keynote lecture from data scientist Rebecca Nugent, Ph.D., an award-winning professor of statistics and data science at Carnegie Mellon University (CMU), where she holds the Stephen E. and Joyce Fienberg Professorship in Statistics & Data Science. Dr. Nugent’s keynote will focus on how data science is the “science of the people” and how data can be harnessed by everyone. There will also be Pi-related trivia, and, of course, pie! If you have any questions, please contact datascience@nih.gov
Details
When
Fri, Mar 13, 2020 - 9:00 am - 5:00 pm
Where
In-Person
On Friday, March 13, NIH’s Office of Data Science Strategy will host “Friday Before Pi Day,” a Friday-the-13th-inspired Pi Day celebration highlighting data science at NIH. The day’s events will include two opportunities to participate in friendly, Pi-themed presentation sessions: Scary stories about data and the lessons we can learn from them As part of the festivities, we’ll have a session dedicated to data horror stories: times we’ve gotten file names confused, wrestled with pesky bugs in our code, toiled over data formats, or lost a precious dataset. So, we want to hear from you! Send us your data horror story ideas and the lessons you’ve learned from them. Please limit all pitches to one paragraph. We’ll select 10 and work with you to develop your stories into engaging and entertaining 10-minute talks. Pi-tionary: 3:14-minute illustrated lightning talks This session will give teams or individuals the opportunity to share an illustrated elevator pitch for their lab’s or office’s work. Competitors will describe what they do in lay terms while sketching it out on the whiteboard. Your drawing doesn’t have to be a data visualization; the figure can be a simple pictorial representation of your project or office mission. If you or your team would like to showcase your work at NIH, please submit your title, ICO, and participant names. Contestants will compete against other teams in front of judges from the Pi Day audience, and the winning team will take home a pie! If you are interested in participating in either activity, please send your submissions by COB Thursday, February 13, to maryam.zaringhalam@nih.gov, indicating which session(s) you’d like to take part in. In addition to these presentations, the day’s activities will feature a keynote lecture from data scientist Rebecca Nugent, Ph.D., an award-winning professor of statistics and data science at Carnegie Mellon University (CMU), where she holds the Stephen E. and Joyce Fienberg Professorship in Statistics & Data Science. Dr. Nugent’s keynote will focus on how data science is the “science of the people” and how data can be harnessed by everyone. There will also be Pi-related trivia, and, of course, pie! If you have any questions, please contact datascience@nih.gov 2020-03-13 09:00:00 In-Person 0 NIH Office of Data Science Strategy: Friday Before Pi Day
119
Description
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas.
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas.
Details
When
Fri, Mar 13, 2020 - 11:00 am - 12:00 pm
Where
Online
Dr. Rommie Amaro from University of California, San Diego covers what scientific workflows and pipeline are, how they can be useful or even critical to science and big data management and presents relevant examples across biomedical research areas. 2020-03-13 11:00:00 Online 0 Data Science Webinar Series: Data Workflows & Pipelines
903
Description
THIS EVENT HAS BEEN CANCELLED
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a ...Read More
THIS EVENT HAS BEEN CANCELLED
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset. Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Tue, Mar 17, 2020 - 1:30 pm - 3:30 pm
Where
Bethesda Building 10 FAES Classroom #6 (B1C208)
THIS EVENT HAS BEEN CANCELLEDSingle-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset. Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-03-17 13:30:00 Bethesda Building 10 FAES Classroom #6 (B1C208) Online Joshua Meyer (CCBR) BTEP 0 Single-Cell RNA-Seq: Analysis on the Palantir Platform (Mar. 17th, Bethesda) - CANCELLED
917
Description
THIS EVENT HAS BEEN CANCELLED
Within the last decade, Dr. Albert has served as the lead developer of various well-known bioinformatics related, web-based platforms such as Galaxy and Biostars. During the same time, he has participated in strengthening the applied bioinformatics competencies of life sciences students at  Penn State. Dr. Albert plans to talk about some of the lessons learned from running Biostars,  a website serving millions of users each year, and to discuss ...Read More
THIS EVENT HAS BEEN CANCELLED
Within the last decade, Dr. Albert has served as the lead developer of various well-known bioinformatics related, web-based platforms such as Galaxy and Biostars. During the same time, he has participated in strengthening the applied bioinformatics competencies of life sciences students at  Penn State. Dr. Albert plans to talk about some of the lessons learned from running Biostars,  a website serving millions of users each year, and to discuss the challenges of running a Bioinformatics Consulting Center at Penn State. He will explain how these cumulative experiences, have led to the design and implementation of  a new platform called Bioinformatics Recipes, an online application that aims to work as both an educational aid, and a practical data analysis platform that can serve the needs of various organizations.   If you are unable to attend in person, WebEx will be provided: https://cbiit.webex.com/cbiit/j.php?MTID=m79340586cdcf3d8ee5793903e30429d3  
Register
Organizer
BTEP
When
Thu, Mar 19, 2020 - 11:00 am - 12:00 pm
Where
Bldg 49, Conf Room 1A50 A/B
THIS EVENT HAS BEEN CANCELLEDWithin the last decade, Dr. Albert has served as the lead developer of various well-known bioinformatics related, web-based platforms such as Galaxy and Biostars. During the same time, he has participated in strengthening the applied bioinformatics competencies of life sciences students at  Penn State. Dr. Albert plans to talk about some of the lessons learned from running Biostars,  a website serving millions of users each year, and to discuss the challenges of running a Bioinformatics Consulting Center at Penn State. He will explain how these cumulative experiences, have led to the design and implementation of  a new platform called Bioinformatics Recipes, an online application that aims to work as both an educational aid, and a practical data analysis platform that can serve the needs of various organizations.   If you are unable to attend in person, WebEx will be provided: https://cbiit.webex.com/cbiit/j.php?MTID=m79340586cdcf3d8ee5793903e30429d3   2020-03-19 11:00:00 Bldg 49,Conf Room 1A50 A/B Online Istvan Albert (Penn State) BTEP 0 Istvan Albert: Bioinformatics Recipes: Creating and Sharing Reproducible Data Analysis Workflows - CANCELLED
120
Description
Members of the Advanced Biomedical Computational Science (ABCS) group will discuss their experiences with use of data science in biomedical research.
Members of the Advanced Biomedical Computational Science (ABCS) group will discuss their experiences with use of data science in biomedical research.
Details
When
Tue, Mar 24, 2020 - 12:00 pm - 1:00 pm
Where
Online
Members of the Advanced Biomedical Computational Science (ABCS) group will discuss their experiences with use of data science in biomedical research. 2020-03-24 12:00:00 Online 0 Data Science Webinar Series: Capstone Panel Discussion
254
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/361206f278924d5099645b5e10b5ca8e/playback 2020-03-24 13:00:00 In-Person 0 Metacore
904
Description
THIS EVENT HAS BEEN CANCELLED
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a ...Read More
THIS EVENT HAS BEEN CANCELLED
Single-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset. Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Wed, Mar 25, 2020 - 3:00 pm - 5:00 pm
Where
Frederick, Fort Detrick, Building 549, Cafe Room
THIS EVENT HAS BEEN CANCELLEDSingle-cell RNA sequencing (scRNA-Seq) is a rapidly evolving method in the field of single-cell genomics which has enhanced our ability to study biological processes at the cellular level. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a Seurat-based Single-cell RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the history and theory behind Single-cell RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real single-cell dataset. Topics you will learn about include filtering and QC, PCA and merging of Seurat objects, clustering, cell annotation, coloring of UMAP and TSNE plots by various metadata, and differential expression. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-03-25 15:00:00 Frederick, Fort Detrick, Building 549, Cafe Room Online Joshua Meyer (CCBR) BTEP 0 Single-Cell RNA-Seq: Analysis on the Palantir Platform (Mar. 25th, Frederick) - CANCELLED
918
Description
THIS EVENT HAS BEEN CANCELLED and will be rescheduled. Small Group Hands-on Workshop on using the AVIA Tool for Variant Annotation, Visualization and Impact Analysis https://avia-abcc.ncifcrf.gov Two of the AVIA developers will be on-hand to work one-on-one and small group with CCR researchers. We will work through a set of test data and help researchers load and work with their own data. Please ...Read More
THIS EVENT HAS BEEN CANCELLED and will be rescheduled. Small Group Hands-on Workshop on using the AVIA Tool for Variant Annotation, Visualization and Impact Analysis https://avia-abcc.ncifcrf.gov Two of the AVIA developers will be on-hand to work one-on-one and small group with CCR researchers. We will work through a set of test data and help researchers load and work with their own data. Please bring your government-issued laptop to access the NIH network. If you do not have a laptop please send email to ncibtep@nih.gov to request a loaner laptop.
Register
Organizer
BTEP
When
Thu, Mar 26, 2020 - 1:00 pm - 3:00 pm
Where
Building 37 Room 4041/4107
THIS EVENT HAS BEEN CANCELLED and will be rescheduled. Small Group Hands-on Workshop on using the AVIA Tool for Variant Annotation, Visualization and Impact Analysis https://avia-abcc.ncifcrf.gov Two of the AVIA developers will be on-hand to work one-on-one and small group with CCR researchers. We will work through a set of test data and help researchers load and work with their own data. Please bring your government-issued laptop to access the NIH network. If you do not have a laptop please send email to ncibtep@nih.gov to request a loaner laptop. 2020-03-26 13:00:00 Building 37 Room 4041/4107 Online Hue Reardon (NCI/OD-F/Advanced Biomedical Computational Science), BTEP 0 Small Group Hands-on Workshop: Variant Annotation, Visualization and Impact Analysis: AVIA tool - CANCELLED
905
Description
THIS EVENT HAS BEEN CANCELLED
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are ...Read More
THIS EVENT HAS BEEN CANCELLED
RNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the theory behind RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression, volcano plots, differential expression of genes analysis, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials.
Register
Organizer
BTEP
When
Tue, Mar 31, 2020 - 1:00 pm - 3:00 pm
Where
Bethesda, Building 10, FAES Classroom #1 (B1C211)
THIS EVENT HAS BEEN CANCELLEDRNA sequencing (RNA-Seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes.  The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the Palantir collaboration platform, which is available and free to use for all NCI researchers.  The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This training session will include a brief lecture on the theory behind RNA-seq. Most of the class time will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression, volcano plots, differential expression of genes analysis, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. Trainees will need an NIH laptop capable of connecting to the secure NIH wireless network, as well as their own NIH credentials. 2020-03-31 13:00:00 Bethesda, Building 10, FAES Classroom #1 (B1C211) Online Joshua Meyer (CCBR) BTEP 0 RNA-Seq: Analysis on the Palantir Platform (Mar. 31st, Bethesda) - CANCELLED
121
Description
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research. We encourage attendance in person, but the class is also available online through WebEx.
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research. We encourage attendance in person, but the class is also available online through WebEx.
Details
When
Thu, Apr 02, 2020 - 10:00 am - 11:30 am
Where
Online
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research. We encourage attendance in person, but the class is also available online through WebEx. 2020-04-02 10:00:00 Online 0 Variant Selection in Genomic DNA sequences
122
Description
The emergence of SARS-CoV-2 in China has driven an enormous global effort to contribute and share genomic data in order to inform local authorities and the international community about key aspects of the outbreak. Analyses of these data have played an important role in tracking the epidemiology and evolution of the virus in real-time. Nextstrain (nextstrain.org) is an open science initiative to harness the scientific and public health potential of pathogen genome data, and ...Read More
The emergence of SARS-CoV-2 in China has driven an enormous global effort to contribute and share genomic data in order to inform local authorities and the international community about key aspects of the outbreak. Analyses of these data have played an important role in tracking the epidemiology and evolution of the virus in real-time. Nextstrain (nextstrain.org) is an open science initiative to harness the scientific and public health potential of pathogen genome data, and has previously provided key insight into outbreaks of Ebola and Zika, and longer-term pathogen spread of Influenza and Enterovirus. This initiative provides a continually-updated view of publicly available data alongside powerful analytic and visualization tools for use by the community. Drs. Hodcroft and Hadfield, along with other members of the Nextstrain team, have been maintaining an up-to-date analysis of SARS-CoV-2 at nextstrain.org/ncov since January 20th 2020. This talk will provide an overview of Nextstrain and how it embodies ‘FAIR’ principles (Findable, Accessible, Interoperable, Reusable), as well as outlining what insights Nextstrain has provided about the COVID-19 outbreak via genomic data sharing from around the world. https://nextstrain.org/
Details
When
Fri, Apr 03, 2020 - 12:00 pm - 1:00 pm
Where
Online
The emergence of SARS-CoV-2 in China has driven an enormous global effort to contribute and share genomic data in order to inform local authorities and the international community about key aspects of the outbreak. Analyses of these data have played an important role in tracking the epidemiology and evolution of the virus in real-time. Nextstrain (nextstrain.org) is an open science initiative to harness the scientific and public health potential of pathogen genome data, and has previously provided key insight into outbreaks of Ebola and Zika, and longer-term pathogen spread of Influenza and Enterovirus. This initiative provides a continually-updated view of publicly available data alongside powerful analytic and visualization tools for use by the community. Drs. Hodcroft and Hadfield, along with other members of the Nextstrain team, have been maintaining an up-to-date analysis of SARS-CoV-2 at nextstrain.org/ncov since January 20th 2020. This talk will provide an overview of Nextstrain and how it embodies ‘FAIR’ principles (Findable, Accessible, Interoperable, Reusable), as well as outlining what insights Nextstrain has provided about the COVID-19 outbreak via genomic data sharing from around the world. https://nextstrain.org/ 2020-04-03 12:00:00 Online 0 Tracking epidemics with Nextstrain
123
Description
Accelerating discovery requires fast access to genomic data as well as on-demand computational resources. NCBI, with support from NIH's STRIDES initiative, has moved all of the Sequence Read Archive (SRA) to the Google (GCP) and Amazon (AWS) clouds, providing unparalleled data access to its 14+ petabytes of data. In this webinar, we will go over how to best leverage the cloud to speed up research and discovery. We'll introduce new and existing tools and data including ...Read More
Accelerating discovery requires fast access to genomic data as well as on-demand computational resources. NCBI, with support from NIH's STRIDES initiative, has moved all of the Sequence Read Archive (SRA) to the Google (GCP) and Amazon (AWS) clouds, providing unparalleled data access to its 14+ petabytes of data. In this webinar, we will go over how to best leverage the cloud to speed up research and discovery. We'll introduce new and existing tools and data including BigQuery, SRA Toolkit, and more. You'll hear about real workflows in the cloud, including an example of the work NCBI was able to accomplish in the cloud using SRA data and a case study from an SRA cloud customer. By the end of this webinar, you will know where to look for new cloud products from NCBI, access help information to get you started, and will have expanded your knowledge on how to run your analyses efficiently in the cloud. Please send your questions, comments and feedback to: webinars@ncbi.nlm.nih.gov
Details
When
Wed, Apr 08, 2020 - 12:00 pm - 12:45 pm
Where
Online
Accelerating discovery requires fast access to genomic data as well as on-demand computational resources. NCBI, with support from NIH's STRIDES initiative, has moved all of the Sequence Read Archive (SRA) to the Google (GCP) and Amazon (AWS) clouds, providing unparalleled data access to its 14+ petabytes of data. In this webinar, we will go over how to best leverage the cloud to speed up research and discovery. We'll introduce new and existing tools and data including BigQuery, SRA Toolkit, and more. You'll hear about real workflows in the cloud, including an example of the work NCBI was able to accomplish in the cloud using SRA data and a case study from an SRA cloud customer. By the end of this webinar, you will know where to look for new cloud products from NCBI, access help information to get you started, and will have expanded your knowledge on how to run your analyses efficiently in the cloud. Please send your questions, comments and feedback to: webinars@ncbi.nlm.nih.gov 2020-04-08 12:00:00 Online 0 Accelerate genomics discovery with SRA in the cloud
124
Description
SS/SC Seminar: Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a ...Read More
SS/SC Seminar: Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a foundation to understanding various dimension reduction methods. Principal components are the linear combinations of the original variables with the 1st principal component captures the largest variance, followed by a descending order for the rest of the principal components and subject to the principal components being orthonormal. Multidimensional Scaling (MDS) provides an alternative strategy for dimension reduction. I will explain why MDS is equivalent to PCA. PCA and MDS are linear dimension reduction methods. In 2000, two Science papers described nonlinear dimension reduction methods: ISOMAP and locally-linear embedding (LLE). ISOMAP is similar to MDS except that it uses geodesic distance instead of Euclidean distance. ISOMAP and LLE provided a conceptual framework that led to the development of the current state of the art dimension reduction methods, such as TSNE and UMAP, which have much improved performance and better visual representation. I will discuss the connection between dimension reduction methods and clustering analysis. I will talk about integrated data analysis using Canonical Correlation Analysis and trajectory analysis using reversed graph embedding. Thursday Aril 16, 2020, 12noon – 1pm Maxwell Lee ‘Dimension Reduction Methods: from PCA to TSNE and UMAP – Part I’
Details
When
Thu, Apr 16, 2020 - 12:00 pm - 1:00 pm
Where
Online
SS/SC Seminar: Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a foundation to understanding various dimension reduction methods. Principal components are the linear combinations of the original variables with the 1st principal component captures the largest variance, followed by a descending order for the rest of the principal components and subject to the principal components being orthonormal. Multidimensional Scaling (MDS) provides an alternative strategy for dimension reduction. I will explain why MDS is equivalent to PCA. PCA and MDS are linear dimension reduction methods. In 2000, two Science papers described nonlinear dimension reduction methods: ISOMAP and locally-linear embedding (LLE). ISOMAP is similar to MDS except that it uses geodesic distance instead of Euclidean distance. ISOMAP and LLE provided a conceptual framework that led to the development of the current state of the art dimension reduction methods, such as TSNE and UMAP, which have much improved performance and better visual representation. I will discuss the connection between dimension reduction methods and clustering analysis. I will talk about integrated data analysis using Canonical Correlation Analysis and trajectory analysis using reversed graph embedding. Thursday Aril 16, 2020, 12noon – 1pm Maxwell Lee ‘Dimension Reduction Methods: from PCA to TSNE and UMAP – Part I’ 2020-04-16 12:00:00 Online 0 Dimension Reduction Methods: from PCA to TSNE and UMAP
125
Description
NIAID Building Workflows with Python
NIAID Building Workflows with Python
Details
When
Wed, Apr 22, 2020 - 2:00 pm - 4:00 pm
Where
Online
NIAID Building Workflows with Python 2020-04-22 14:00:00 Online 0 Python Programming for Scientists
906
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 1 of a 2-part course.  Please make sure you take Part 2 after taking Part 1. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.
Register
Organizer
BTEP
When
Wed, Apr 22, 2020 - 3:00 pm - 4:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 1 of a 2-part course.  Please make sure you take Part 2 after taking Part 1. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN. 2020-04-22 15:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP - Part 1: Background & Theory (Apr. 22nd)
126
Description
The Center for Biomedical Informatics and Information Technology (CBIIT) training program invites you to attend a webinar on the software application, DNASTAR Lasergene. DNASTAR offers data analysis tools for molecular biology, protein analysis, and genomics. Attendees will see an overview of the applications included in Lasergene Molecular Biology and Protein package. The webinar will demonstrate the latest version, Lasergene 17, and will include cloning and primer design; auto-annotation; multiple sequence (phylogenetic) alignment; Sanger sequence assembly/alignment; ...Read More
The Center for Biomedical Informatics and Information Technology (CBIIT) training program invites you to attend a webinar on the software application, DNASTAR Lasergene. DNASTAR offers data analysis tools for molecular biology, protein analysis, and genomics. Attendees will see an overview of the applications included in Lasergene Molecular Biology and Protein package. The webinar will demonstrate the latest version, Lasergene 17, and will include cloning and primer design; auto-annotation; multiple sequence (phylogenetic) alignment; Sanger sequence assembly/alignment; and protein analysis, including 3D structure visualization.
Details
Organizer
CBIIT
When
Thu, Apr 23, 2020 - 11:00 am - 12:00 pm
Where
Online
The Center for Biomedical Informatics and Information Technology (CBIIT) training program invites you to attend a webinar on the software application, DNASTAR Lasergene. DNASTAR offers data analysis tools for molecular biology, protein analysis, and genomics. Attendees will see an overview of the applications included in Lasergene Molecular Biology and Protein package. The webinar will demonstrate the latest version, Lasergene 17, and will include cloning and primer design; auto-annotation; multiple sequence (phylogenetic) alignment; Sanger sequence assembly/alignment; and protein analysis, including 3D structure visualization. 2020-04-23 11:00:00 Online CBIIT 0 Introduction to DNASTAR Lasergene
919
Description
Within the last decade, Dr. Albert has served as the lead developer of various well-known bioinformatics related, web-based platforms such as Galaxy and Biostars. During the same time, he has participated in strengthening the applied bioinformatics competencies of life sciences students at  Penn State. Dr. Albert plans to talk about some of the lessons learned from running Biostars,  a website serving millions of users each year, and to discuss the challenges of running a Bioinformatics ...Read More
Within the last decade, Dr. Albert has served as the lead developer of various well-known bioinformatics related, web-based platforms such as Galaxy and Biostars. During the same time, he has participated in strengthening the applied bioinformatics competencies of life sciences students at  Penn State. Dr. Albert plans to talk about some of the lessons learned from running Biostars,  a website serving millions of users each year, and to discuss the challenges of running a Bioinformatics Consulting Center at Penn State. He will explain how these cumulative experiences, have led to the design and implementation of  a new platform called Bioinformatics Recipes, an online application that aims to work as both an educational aid, and a practical data analysis platform that can serve the needs of various organizations. Presentation: https://docs.google.com/presentation/d/1xtZ9bNvVggY7mnKJfh7B1mYb0yOp3K9LD6SmoOQnsVU/edit?usp=sharing WebEx recording: https://cbiit.webex.com/cbiit/ldr.php?RCID=d212f383485e4b0341264e14995dbc7b
Register
Organizer
BTEP
When
Thu, Apr 23, 2020 - 11:00 am - 12:00 pm
Where
Online Webinar
Within the last decade, Dr. Albert has served as the lead developer of various well-known bioinformatics related, web-based platforms such as Galaxy and Biostars. During the same time, he has participated in strengthening the applied bioinformatics competencies of life sciences students at  Penn State. Dr. Albert plans to talk about some of the lessons learned from running Biostars,  a website serving millions of users each year, and to discuss the challenges of running a Bioinformatics Consulting Center at Penn State. He will explain how these cumulative experiences, have led to the design and implementation of  a new platform called Bioinformatics Recipes, an online application that aims to work as both an educational aid, and a practical data analysis platform that can serve the needs of various organizations. Presentation: https://docs.google.com/presentation/d/1xtZ9bNvVggY7mnKJfh7B1mYb0yOp3K9LD6SmoOQnsVU/edit?usp=sharing WebEx recording: https://cbiit.webex.com/cbiit/ldr.php?RCID=d212f383485e4b0341264e14995dbc7b 2020-04-23 11:00:00 Online Webinar Online Istvan Albert (Penn State) BTEP 0 Webinar, Istvan Albert (PSU): Bioinformatics Recipes: Creating and Sharing Reproducible Data Analysis Workflows
127
Description
SSSC Seminar:Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a foundation ...Read More
SSSC Seminar:Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a foundation to understanding various dimension reduction methods. Principal components are the linear combinations of the original variables with the 1st principal component captures the largest variance, followed by a descending order for the rest of the principal components and subject to the principal components being orthonormal. Multidimensional Scaling (MDS) provides an alternative strategy for dimension reduction. I will explain why MDS is equivalent to PCA. PCA and MDS are linear dimension reduction methods. In 2000, two Science papers described nonlinear dimension reduction methods: ISOMAP and locally-linear embedding (LLE). ISOMAP is similar to MDS except that it uses geodesic distance instead of Euclidean distance. ISOMAP and LLE provided a conceptual framework that led to the development of the current state of the art dimension reduction methods, such as TSNE and UMAP, which have much improved performance and better visual representation. I will discuss the connection between dimension reduction methods and clustering analysis. I will talk about integrated data analysis using Canonical Correlation Analysis and trajectory analysis using reversed graph embedding. Thursday, April 23, 2020, 12noon – 1pm Maxwell Lee ‘Dimension Reduction Methods: from PCA to TSNE and UMAP – Part II’
Details
When
Thu, Apr 23, 2020 - 12:00 pm - 1:00 pm
Where
Online
SSSC Seminar:Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a foundation to understanding various dimension reduction methods. Principal components are the linear combinations of the original variables with the 1st principal component captures the largest variance, followed by a descending order for the rest of the principal components and subject to the principal components being orthonormal. Multidimensional Scaling (MDS) provides an alternative strategy for dimension reduction. I will explain why MDS is equivalent to PCA. PCA and MDS are linear dimension reduction methods. In 2000, two Science papers described nonlinear dimension reduction methods: ISOMAP and locally-linear embedding (LLE). ISOMAP is similar to MDS except that it uses geodesic distance instead of Euclidean distance. ISOMAP and LLE provided a conceptual framework that led to the development of the current state of the art dimension reduction methods, such as TSNE and UMAP, which have much improved performance and better visual representation. I will discuss the connection between dimension reduction methods and clustering analysis. I will talk about integrated data analysis using Canonical Correlation Analysis and trajectory analysis using reversed graph embedding. Thursday, April 23, 2020, 12noon – 1pm Maxwell Lee ‘Dimension Reduction Methods: from PCA to TSNE and UMAP – Part II’ 2020-04-23 12:00:00 Online 0 Dimension Reduction Methods: from PCA to TSNE to UMAP Part 2
907
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 2 of a 2-part course.  Please make sure you have taken Part 1 before taking Part 2. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.
Register
Organizer
BTEP
When
Thu, Apr 23, 2020 - 3:00 pm - 4:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 2 of a 2-part course.  Please make sure you have taken Part 1 before taking Part 2. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN. 2020-04-23 15:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP - Part 2: Hands-on Workshop (Apr. 23rd)
128
Description
Introduction to Machine Learning with Python and scikit-learn
Introduction to Machine Learning with Python and scikit-learn
Details
When
Mon, Apr 27, 2020 - 2:00 pm - 3:00 pm
Where
Online
Introduction to Machine Learning with Python and scikit-learn 2020-04-27 14:00:00 Online 0 NIAID Python Programming for Scientists
908
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 1 of a 2-part course.  Please make sure you take Part 2 after taking Part 1. Please note: Trainees will need their own NIH user name and password, and an NIH computer capable of connecting to the secure NIH network using VPN.
Register
Organizer
BTEP
When
Tue, Apr 28, 2020 - 3:00 pm - 4:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 1 of a 2-part course.  Please make sure you take Part 2 after taking Part 1. Please note: Trainees will need their own NIH user name and password, and an NIH computer capable of connecting to the secure NIH network using VPN. 2020-04-28 15:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP - Part 1: Background & Theory (Apr. 28th)
129
Description
Enabling Reproducible Science with Python and Jupiter
Enabling Reproducible Science with Python and Jupiter
Details
When
Wed, Apr 29, 2020 - 2:00 pm - 4:00 pm
Where
Online
Enabling Reproducible Science with Python and Jupiter 2020-04-29 14:00:00 Online 0 NIAID Python Programming for Scientists
130
Description
Presenter: Dr. Christian Aguilera-Sandoval, BD Description: This webinar will provide an overview of FlowJo, an integrated environment for viewing and analyzing flow cytometry data. Attendees will see how FlowJo can be used to uniformly analyze whole experiments encompassing many related samples. Participants also will learn how to use FlowJo’s tools to generate graphs and statistical reports to further drive discovery of biological mechanisms.
Presenter: Dr. Christian Aguilera-Sandoval, BD Description: This webinar will provide an overview of FlowJo, an integrated environment for viewing and analyzing flow cytometry data. Attendees will see how FlowJo can be used to uniformly analyze whole experiments encompassing many related samples. Participants also will learn how to use FlowJo’s tools to generate graphs and statistical reports to further drive discovery of biological mechanisms.
Details
Organizer
CBIIT
When
Thu, Apr 30, 2020 - 12:00 pm - 2:00 pm
Where
Online
Presenter: Dr. Christian Aguilera-Sandoval, BD Description: This webinar will provide an overview of FlowJo, an integrated environment for viewing and analyzing flow cytometry data. Attendees will see how FlowJo can be used to uniformly analyze whole experiments encompassing many related samples. Participants also will learn how to use FlowJo’s tools to generate graphs and statistical reports to further drive discovery of biological mechanisms. 2020-04-30 12:00:00 Online CBIIT 0 FlowJo: An Integrated environment for viewing and analyzing flow cytometry data
131
Description
SSSC Seminar: Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a foundation ...Read More
SSSC Seminar: Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a foundation to understanding various dimension reduction methods. Principal components are the linear combinations of the original variables with the 1st principal component captures the largest variance, followed by a descending order for the rest of the principal components and subject to the principal components being orthonormal. Multidimensional Scaling (MDS) provides an alternative strategy for dimension reduction. I will explain why MDS is equivalent to PCA. PCA and MDS are linear dimension reduction methods. In 2000, two Science papers described nonlinear dimension reduction methods: ISOMAP and locally-linear embedding (LLE). ISOMAP is similar to MDS except that it uses geodesic distance instead of Euclidean distance. ISOMAP and LLE provided a conceptual framework that led to the development of the current state of the art dimension reduction methods, such as TSNE and UMAP, which have much improved performance and better visual representation. I will discuss the connection between dimension reduction methods and clustering analysis. I will talk about integrated data analysis using Canonical Correlation Analysis and trajectory analysis using reversed graph embedding. Thursday, April 30, 2020, 12noon – 1pm Maxwell Lee ‘Dimension Reduction Methods: from PCA to TSNE and UMAP – Part III’
Details
When
Thu, Apr 30, 2020 - 12:00 pm - 1:00 pm
Where
Online
SSSC Seminar: Dimension Reduction Methods: from PCA to TSNE and UMAP In this seminar series, I will review some basic concepts of statistical analyses that are commonly used in everyday research. I will talk about supervised versus unsupervised learning. My talk will be focused on unsupervised methods, in particular, on dimension reduction methods, which are fundamental to most of state-of-the-art technologies used in the single cell data analysis. Principal Component Analysis (PCA) provides a foundation to understanding various dimension reduction methods. Principal components are the linear combinations of the original variables with the 1st principal component captures the largest variance, followed by a descending order for the rest of the principal components and subject to the principal components being orthonormal. Multidimensional Scaling (MDS) provides an alternative strategy for dimension reduction. I will explain why MDS is equivalent to PCA. PCA and MDS are linear dimension reduction methods. In 2000, two Science papers described nonlinear dimension reduction methods: ISOMAP and locally-linear embedding (LLE). ISOMAP is similar to MDS except that it uses geodesic distance instead of Euclidean distance. ISOMAP and LLE provided a conceptual framework that led to the development of the current state of the art dimension reduction methods, such as TSNE and UMAP, which have much improved performance and better visual representation. I will discuss the connection between dimension reduction methods and clustering analysis. I will talk about integrated data analysis using Canonical Correlation Analysis and trajectory analysis using reversed graph embedding. Thursday, April 30, 2020, 12noon – 1pm Maxwell Lee ‘Dimension Reduction Methods: from PCA to TSNE and UMAP – Part III’ 2020-04-30 12:00:00 Online 0 Webinar: Dimension Reduction Methods: from PCA to TSNE to UMAP Part 3
255
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/513032e4d351405ba008422226966d29/playback 2020-04-30 12:00:00 In-Person 0 FlowJo v10 Training
909
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 2 of a 2-part course.  Please make sure you have taken Part 1 before taking Part 2. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.
Register
Organizer
BTEP
When
Thu, Apr 30, 2020 - 3:00 pm - 5:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 2 of a 2-part course.  Please make sure you have taken Part 1 before taking Part 2. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN. 2020-04-30 15:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP - Part 2: Hands-on Workshop (Apr. 30th)
132
Description
BioCyc.org [1] is an extensive web portal containing 17,000 microbial genomes and associated metabolic pathways. BioCyc databases are created through a process that combines computational inferences with imported and curated data from multiple sources. The first step in the creation of BioCyc databases is to run prediction algorithms for metabolic pathways, operons, PFam domains, and orthologs. We next run programs that import data from related databases (such as UniProt) including regulatory network data, protein features, subcellular ...Read More
BioCyc.org [1] is an extensive web portal containing 17,000 microbial genomes and associated metabolic pathways. BioCyc databases are created through a process that combines computational inferences with imported and curated data from multiple sources. The first step in the creation of BioCyc databases is to run prediction algorithms for metabolic pathways, operons, PFam domains, and orthologs. We next run programs that import data from related databases (such as UniProt) including regulatory network data, protein features, subcellular locations, and Gene Ontology assignments. Curated databases next receive intensive review and updating by a Ph.D. biologist that includes reviewing the computationally predicted metabolic pathways, entering new gene functions and metabolic pathways from the experimental literature, and defining protein complexes. The resulting databases are high-quality reference sources for the latest gene and pathway information. Overall the BioCyc databases have been curated from 95,000 publications. The BioCyc website provides extensive bioinformatics tools for searching and analyzing these databases, and leveraging them for analysis of omics datasets. Genome-related tools include a genome browser, sequence searching and alignment, and extraction of sequence regions. Pathway-related tools include pathway diagrams, a tool for navigating zoomable organism-specific metabolic map diagrams, and a tool for searching for metabolic routes that transform a starting metabolite into a product metabolite. Regulation tools depict operons and regulatory sites, as well as showing full organism regulatory networks. Comparative analysis tools enable comparisons of genome organization, of orthologs, and of pathway complements. Omics data analysis tools support enrichment analysis and painting of transcriptomics and metabolomics data onto individual pathway diagrams and onto zoomable metabolic map diagrams. A new Omics Dashboard tool enables interactive exploration of omics datasets through a hierarchy of cellular systems. SmartTables enable users to construct tables of genes, metabolites, or pathways, and to perform analysis such as transforming a set of pathways to all genes within the pathway set.
Details
When
Fri, May 01, 2020 - 12:00 pm - 1:00 pm
Where
Online
BioCyc.org [1] is an extensive web portal containing 17,000 microbial genomes and associated metabolic pathways. BioCyc databases are created through a process that combines computational inferences with imported and curated data from multiple sources. The first step in the creation of BioCyc databases is to run prediction algorithms for metabolic pathways, operons, PFam domains, and orthologs. We next run programs that import data from related databases (such as UniProt) including regulatory network data, protein features, subcellular locations, and Gene Ontology assignments. Curated databases next receive intensive review and updating by a Ph.D. biologist that includes reviewing the computationally predicted metabolic pathways, entering new gene functions and metabolic pathways from the experimental literature, and defining protein complexes. The resulting databases are high-quality reference sources for the latest gene and pathway information. Overall the BioCyc databases have been curated from 95,000 publications. The BioCyc website provides extensive bioinformatics tools for searching and analyzing these databases, and leveraging them for analysis of omics datasets. Genome-related tools include a genome browser, sequence searching and alignment, and extraction of sequence regions. Pathway-related tools include pathway diagrams, a tool for navigating zoomable organism-specific metabolic map diagrams, and a tool for searching for metabolic routes that transform a starting metabolite into a product metabolite. Regulation tools depict operons and regulatory sites, as well as showing full organism regulatory networks. Comparative analysis tools enable comparisons of genome organization, of orthologs, and of pathway complements. Omics data analysis tools support enrichment analysis and painting of transcriptomics and metabolomics data onto individual pathway diagrams and onto zoomable metabolic map diagrams. A new Omics Dashboard tool enables interactive exploration of omics datasets through a hierarchy of cellular systems. SmartTables enable users to construct tables of genes, metabolites, or pathways, and to perform analysis such as transforming a set of pathways to all genes within the pathway set. 2020-05-01 12:00:00 Online 0 The BioCyc Web Portal for Microbial Genomes and Metabolic Pathways
910
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 1 of a 2-part course.  Please make sure you take Part 2 after taking Part 1. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.
Register
Organizer
BTEP
When
Tue, May 05, 2020 - 3:00 pm - 4:30 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 1 of a 2-part course.  Please make sure you take Part 2 after taking Part 1. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN. 2020-05-05 15:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP - Part 1: Background & Theory (May 5th)
133
Description
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research.
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research.
Details
Organizer
NIH Training Library
When
Thu, May 07, 2020 - 10:00 am - 11:30 am
Where
Online
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research. 2020-05-07 10:00:00 Online NIH Training Library 0 Variant Selection in Genomic DNA sequences
911
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 2 of a 2-part course.  Please make sure you have taken Part 1 before taking Part 2. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.
Register
Organizer
BTEP
When
Thu, May 07, 2020 - 3:00 pm - 5:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 2 of a 2-part course.  Please make sure you have taken Part 1 before taking Part 2. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN. 2020-05-07 15:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP - Part 2: Hands-on Workshop (May 7th)
134
Description
DNASTAR offers software solutions for molecular biology, structural biology, and genomics. In this class, attendees will be presented with an overview of the applications within the Lasergene Suite. The instructor will use the latest software version and demonstration data to provide tutorials for various workflows, including: cloning and primer design, Sanger sequence alignment, protein structure analysis, and next generation sequence assembly and analysis. Individual meetings after the training session can be scheduled to provide help ...Read More
DNASTAR offers software solutions for molecular biology, structural biology, and genomics. In this class, attendees will be presented with an overview of the applications within the Lasergene Suite. The instructor will use the latest software version and demonstration data to provide tutorials for various workflows, including: cloning and primer design, Sanger sequence alignment, protein structure analysis, and next generation sequence assembly and analysis. Individual meetings after the training session can be scheduled to provide help with specific projects. To schedule an individual meeting, please email the instructor.
Details
Organizer
NIH Training Library
When
Fri, May 08, 2020 - 9:30 am - 3:30 pm
Where
Online
DNASTAR offers software solutions for molecular biology, structural biology, and genomics. In this class, attendees will be presented with an overview of the applications within the Lasergene Suite. The instructor will use the latest software version and demonstration data to provide tutorials for various workflows, including: cloning and primer design, Sanger sequence alignment, protein structure analysis, and next generation sequence assembly and analysis. Individual meetings after the training session can be scheduled to provide help with specific projects. To schedule an individual meeting, please email the instructor. 2020-05-08 09:30:00 Online NIH Training Library 0 DNASTAR Lasergene Demonstration and Training Workshop
140
Description
This webinar will provide an introduction to RNA-Seq data analysis followed by tutorials of popular RNA-Seq analysis applications. Participants will be prepared to independently run basic RNA-Seq analysis for expression profiling using a “point and click” approach on a public Galaxy platform. Presenter: Dr. Daoud Meerzaman and Dr. Qingrong Chen
This webinar will provide an introduction to RNA-Seq data analysis followed by tutorials of popular RNA-Seq analysis applications. Participants will be prepared to independently run basic RNA-Seq analysis for expression profiling using a “point and click” approach on a public Galaxy platform. Presenter: Dr. Daoud Meerzaman and Dr. Qingrong Chen
Details
Organizer
CBIIT
When
Tue, May 12, 2020 - 10:00 am - 4:00 pm
Where
Online
This webinar will provide an introduction to RNA-Seq data analysis followed by tutorials of popular RNA-Seq analysis applications. Participants will be prepared to independently run basic RNA-Seq analysis for expression profiling using a “point and click” approach on a public Galaxy platform. Presenter: Dr. Daoud Meerzaman and Dr. Qingrong Chen 2020-05-12 10:00:00 Online CBIIT 0 RNA-Seq Data Analysis on the Galaxy Platform
912
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 1 of a 2-part course.  Please make sure you take Part 2 after taking Part 1. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.
Register
Organizer
BTEP
When
Tue, May 12, 2020 - 3:00 pm - 4:30 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 1 of a 2-part course.  Please make sure you take Part 2 after taking Part 1. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN. 2020-05-12 15:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP - Part 1: Background & Theory (May 12th)
141
Description
During this virtual panel discussion, members of the Advanced Biomedical Computational Science (ABCS) group will discuss their experiences with use of data science in biomedical research. The panel discussion will cover: Types of research data that ABCS works with. Challenges faced when working with research data and solutions to overcome these challenges. Benefits and obstacles to data sharing. Advice on best practices when working with biomedical research data. Participants include: Uma S. Mudunuri, ABCS Deputy ...Read More
During this virtual panel discussion, members of the Advanced Biomedical Computational Science (ABCS) group will discuss their experiences with use of data science in biomedical research. The panel discussion will cover: Types of research data that ABCS works with. Challenges faced when working with research data and solutions to overcome these challenges. Benefits and obstacles to data sharing. Advice on best practices when working with biomedical research data. Participants include: Uma S. Mudunuri, ABCS Deputy Director Justin B. Lack, Ph.D., NIAID Bioinformatics Team Lead Yanling Liu, Ph.D., Imaging and Visualization Team Lead Raul Cachau, Ph.D., Senior Principal Scientist Parthav Jailwala, CCR Bioinformatics Team Lead Brian T. Luke, Ph.D., Principal Scientist Email Joelle Mornini at Joelle.Mornini@nih.gov if you have any questions about the Data Science Discussion Panel.
Details
When
Wed, May 13, 2020 - 11:00 am - 12:00 pm
Where
Online
During this virtual panel discussion, members of the Advanced Biomedical Computational Science (ABCS) group will discuss their experiences with use of data science in biomedical research. The panel discussion will cover: Types of research data that ABCS works with. Challenges faced when working with research data and solutions to overcome these challenges. Benefits and obstacles to data sharing. Advice on best practices when working with biomedical research data. Participants include: Uma S. Mudunuri, ABCS Deputy Director Justin B. Lack, Ph.D., NIAID Bioinformatics Team Lead Yanling Liu, Ph.D., Imaging and Visualization Team Lead Raul Cachau, Ph.D., Senior Principal Scientist Parthav Jailwala, CCR Bioinformatics Team Lead Brian T. Luke, Ph.D., Principal Scientist Email Joelle Mornini at Joelle.Mornini@nih.gov if you have any questions about the Data Science Discussion Panel. 2020-05-13 11:00:00 Online 0 ABCS: Data Science Discussion Panel
142
Description
Speaker: Maxwell LeeToday Max will discuss the principals of TSNE and UMAP, the single cell sequencing data analysis work flow and how parameters of software affect the results.
Speaker: Maxwell LeeToday Max will discuss the principals of TSNE and UMAP, the single cell sequencing data analysis work flow and how parameters of software affect the results.
Details
When
Thu, May 14, 2020 - 12:00 pm - 1:00 pm
Where
Online
Speaker: Maxwell LeeToday Max will discuss the principals of TSNE and UMAP, the single cell sequencing data analysis work flow and how parameters of software affect the results. 2020-05-14 12:00:00 Online 0 Principles of TSNE and UMAP
913
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 2 of a 2-part course.  Please make sure you have taken Part 1 before taking Part 2. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.
Register
Organizer
BTEP
When
Thu, May 14, 2020 - 3:00 pm - 5:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of two parts, taught in two separate sessions. The first session will consist of a lecture on the theory behind RNA-seq and many of the methodologies we use in the analysis. The second session will be devoted to a workshop in which trainees will be guided through a basic analysis of a real RNA-seq dataset. Topics you will learn about include filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After training, trainees will be ready to upload their own data to the platform and begin their own analyses. This is Part 2 of a 2-part course.  Please make sure you have taken Part 1 before taking Part 2. Please note: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN. 2020-05-14 15:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP - Part 2: Hands-on Workshop (May 14th)
143
Description
This training will provide an introduction to RNA-seq data analysis followed by tutorials showing the use of popular RNA-seq analysis packages and preparing participants to independently run basic RNA-Seq analysis for expression profiling. The exercise will run on the Galaxy platform using Illumina paired-end RNA-seq data. Participants will have a chance to: run quality control check on sequencing data; align the sequencing reads to a reference genome; generate alignment statistics and check mapping quality; measure ...Read More
This training will provide an introduction to RNA-seq data analysis followed by tutorials showing the use of popular RNA-seq analysis packages and preparing participants to independently run basic RNA-Seq analysis for expression profiling. The exercise will run on the Galaxy platform using Illumina paired-end RNA-seq data. Participants will have a chance to: run quality control check on sequencing data; align the sequencing reads to a reference genome; generate alignment statistics and check mapping quality; measure abundance of transcripts; perform differential expression analysis; and visualize the output of RNA-seq analyses.
Details
Organizer
NIH Training Library
When
Mon, May 18, 2020 - 11:00 am - 2:00 pm
Where
Online
This training will provide an introduction to RNA-seq data analysis followed by tutorials showing the use of popular RNA-seq analysis packages and preparing participants to independently run basic RNA-Seq analysis for expression profiling. The exercise will run on the Galaxy platform using Illumina paired-end RNA-seq data. Participants will have a chance to: run quality control check on sequencing data; align the sequencing reads to a reference genome; generate alignment statistics and check mapping quality; measure abundance of transcripts; perform differential expression analysis; and visualize the output of RNA-seq analyses. 2020-05-18 11:00:00 Online NIH Training Library 0 RNA-Seq Analysis Training on the Galaxy Platform
144
Description
Students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for DNA-Seq and ATAC/ ChIP-Seq data analysis.The class will start with NGS data import, followed by filtering duplicates, SNV detection and annotation in DNA-seq analysis; Peak detection and annotation in ATAC-seq analysis; Visualize data using Chromosome view, Sankey plot, TSS plot and other viewers.Benefits: Acquire working knowledge of tools available to NIH researchers ...Read More
Students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for DNA-Seq and ATAC/ ChIP-Seq data analysis.The class will start with NGS data import, followed by filtering duplicates, SNV detection and annotation in DNA-seq analysis; Peak detection and annotation in ATAC-seq analysis; Visualize data using Chromosome view, Sankey plot, TSS plot and other viewers.Benefits: Acquire working knowledge of tools available to NIH researchers for the start to finish DNA-Seq and ATAC/ChIP-seq data analysis. Who should attend: NIH staff interested in learning DNA-Seq data analysis and ATAC/ChIP-seq data analysis.
Details
Organizer
NIH Training Library
When
Tue, May 19, 2020 - 9:30 am - 12:00 pm
Where
Online
Students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for DNA-Seq and ATAC/ ChIP-Seq data analysis.The class will start with NGS data import, followed by filtering duplicates, SNV detection and annotation in DNA-seq analysis; Peak detection and annotation in ATAC-seq analysis; Visualize data using Chromosome view, Sankey plot, TSS plot and other viewers.Benefits: Acquire working knowledge of tools available to NIH researchers for the start to finish DNA-Seq and ATAC/ChIP-seq data analysis. Who should attend: NIH staff interested in learning DNA-Seq data analysis and ATAC/ChIP-seq data analysis. 2020-05-19 09:30:00 Online NIH Training Library 0 Hands-On DNA-Seq and ATAC-Seq Data Analysis in Partek Flow
145
Description
UCSC Xena ( xena.ucscedu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Xena showcases seminal cancer genomics datasets from TCGA and the Pan-Cancer Atlas, as well as the GDC, PCAWG, and ICGC; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data modality, including SNPs, INDELs, large structural variants, CNV, gene and other types of expression, DNA methylation, ...Read More
UCSC Xena ( xena.ucscedu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Xena showcases seminal cancer genomics datasets from TCGA and the Pan-Cancer Atlas, as well as the GDC, PCAWG, and ICGC; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data modality, including SNPs, INDELs, large structural variants, CNV, gene and other types of expression, DNA methylation, clinical and phenotypic annotations. Browser features include the high performance Visual Spreadsheet, dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, and live bookmarks. Researchers can securely view their own analysis results side-by-side with the data already in Xena, enabling insight and discovery between public and private data. Integration occurs only within the Xena Browser, ensuring pre-publication data remains private. Xena can help you answer questions like: * Is over-expression of geneA associated with lower survival? * Is geneB differentially expressed in tumor vs normal? * Do my subgroups have differential survival? * What is the relationship between expression, mutation, copy number, etc for these genes? This webinar will include a live demonstration of Xena. Please bring a laptop with either Chrome or Firefox installed to follow along. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancergov/
Details
Organizer
CBIIT
When
Tue, May 19, 2020 - 12:30 pm - 1:30 pm
Where
Online
UCSC Xena ( xena.ucscedu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Xena showcases seminal cancer genomics datasets from TCGA and the Pan-Cancer Atlas, as well as the GDC, PCAWG, and ICGC; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data modality, including SNPs, INDELs, large structural variants, CNV, gene and other types of expression, DNA methylation, clinical and phenotypic annotations. Browser features include the high performance Visual Spreadsheet, dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, and live bookmarks. Researchers can securely view their own analysis results side-by-side with the data already in Xena, enabling insight and discovery between public and private data. Integration occurs only within the Xena Browser, ensuring pre-publication data remains private. Xena can help you answer questions like: * Is over-expression of geneA associated with lower survival? * Is geneB differentially expressed in tumor vs normal? * Do my subgroups have differential survival? * What is the relationship between expression, mutation, copy number, etc for these genes? This webinar will include a live demonstration of Xena. Please bring a laptop with either Chrome or Firefox installed to follow along. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancergov/ 2020-05-19 12:30:00 Online CBIIT 0 Introduction to UCSC Xena
146
Description
The class will start with an overview of Partek Genomics Suite with Pathway and followed with the hands-on training on Illumina 450K Methylation array data. The workflow can also be used on HumanMethylation27 (27K) and Human MethylationEPIC (850K) BeadChips. Students will learn how to perform QA/QC, detect differential methylation, find genes overlapping CpG loci and biological interpretation as well as other basic analysis in Partek Genomics Suite with Pathway. For example: import data from ...Read More
The class will start with an overview of Partek Genomics Suite with Pathway and followed with the hands-on training on Illumina 450K Methylation array data. The workflow can also be used on HumanMethylation27 (27K) and Human MethylationEPIC (850K) BeadChips. Students will learn how to perform QA/QC, detect differential methylation, find genes overlapping CpG loci and biological interpretation as well as other basic analysis in Partek Genomics Suite with Pathway. For example: import data from Illumina methylation array in .idat files; methylation array-specific normalization; perform QA/AC; detection of differentially methylated CpG loci; creating list of loci of interest; identifying methylation signatures; find overlapping genes; biological interpretation; visualization (PCA; Dot plot; Hierarchical clustering; Pathway). Benefits: Acquire working knowledge of tools available to NIH researchers for Microarray Methylation data analysis.
Details
Organizer
NIH Training Library
When
Tue, May 19, 2020 - 1:00 pm - 4:00 pm
Where
Online
The class will start with an overview of Partek Genomics Suite with Pathway and followed with the hands-on training on Illumina 450K Methylation array data. The workflow can also be used on HumanMethylation27 (27K) and Human MethylationEPIC (850K) BeadChips. Students will learn how to perform QA/QC, detect differential methylation, find genes overlapping CpG loci and biological interpretation as well as other basic analysis in Partek Genomics Suite with Pathway. For example: import data from Illumina methylation array in .idat files; methylation array-specific normalization; perform QA/AC; detection of differentially methylated CpG loci; creating list of loci of interest; identifying methylation signatures; find overlapping genes; biological interpretation; visualization (PCA; Dot plot; Hierarchical clustering; Pathway). Benefits: Acquire working knowledge of tools available to NIH researchers for Microarray Methylation data analysis. 2020-05-19 13:00:00 Online NIH Training Library 0 Hands-On Microarray Methylation Data Analysis in Partek Genomic Suite
147
Description
Advances in technology have reduced the cost of single cell sequencing, opening the doors to many new areas of study including transcriptome, DNA genomics, epigenomics and microbial systems. This workshop, provided by experts from across NIH, will give an overview of single-cell sequencing, especially in single cell RNA-seq, highlight tips and potential related to single-cell RNA sequencing, and introduce the major methods and tools available for single-cell RNA sequencing and analysis (both commercial and open ...Read More
Advances in technology have reduced the cost of single cell sequencing, opening the doors to many new areas of study including transcriptome, DNA genomics, epigenomics and microbial systems. This workshop, provided by experts from across NIH, will give an overview of single-cell sequencing, especially in single cell RNA-seq, highlight tips and potential related to single-cell RNA sequencing, and introduce the major methods and tools available for single-cell RNA sequencing and analysis (both commercial and open source). Strategies and Methods in scRNA-seq Data Analysis Li Jia, Bioinformatician, NIH Library Single cell sequencing has reopened the definition of a cell’s identity and the ways in which that identity is regulated by the cell’s molecular circuitry. Learn the types of studies that are well suited for single cell sequencing analysis as well as how to design a single cell experiment. The speaker will also walk through the steps of a Single Cell RNA-Sequence (scRNA-seq) processing, common analysis strategies, and state-of-the-art analysis methods using R. Avoiding Common Pitfalls in Single Cell RNA-Seq Experiments Michael Kelly, Senior Scientist, Single Cell Analysis Facility, Frederick National Laboratory As the use of single cell sequencing becomes increasingly common, researchers may have a false sense that the technique is immune to issues that undermine the experiment, only to find limitations at the data analysis stage. The speaker will discuss various examples of potential data issues that can arise such as variability in number of targets datapoints, low gene detection, and technical batch effects. As part of this discussion the speaker will address some strategies for how to avoid them, and what they might look like in the final dataset. The speaker will also discuss some of the approaches used during a typical single cell RNA-Seq analysis workflow to help mitigate effects on your data. The Applications of Current Single Cell Sequencing Brian J. Henson, Senior Specialist, Illumina, Inc. The speaker will provide an overview and demonstration of the current single-cell applications available, including RNA, ATAC, CNV, TCR, Epitope, and spatial gene expression. Several examples from the literature will be highlighted as use cases for the tools. The speaker will conclude with a practical discussion on the utility and capacity of using the single-cell applications on the NovaSeq and the NextSeq 2000 benchtop sequencers. Single Cell Analysis in Partek Flow Xiaowen Wang, Senior Technical Support, Partek, Inc. Demonstration from a Partek scientist who will utilize Single Cell RNA-Seq data within Partek Flow to streamline Multi-omics data analysis. This GUI-based tool helps to overcome common analysis challenges on scRNA-Seq data and has built in data visualization options. Identifying and Interpreting the Human Liver Cellular Landscape using OmicSoft and IPA Eric Seiser, Senior Application Scientist, QIAGEN Bioinformatics The speaker will provide a practical example of how they utilized publicly available scRNA-Seq data in a research study. Specifically, the speaker processed scRNA-Seq human liver data using the OmicSoft single-cell analysis pipeline to identify numerous discrete cell populations. Gene signatures from these resident cells were then analyzed in Ingenuity Pathway Analysis to determine both shared and distinct cell biology in the context of pathways, regulation, and functional characteristics. These results provide insight into hepatic cells as well as the immune microenvironment within the liver.
Details
Organizer
NIH Training Library
When
Wed, May 20, 2020 - 9:30 am - 4:00 pm
Where
Online
Advances in technology have reduced the cost of single cell sequencing, opening the doors to many new areas of study including transcriptome, DNA genomics, epigenomics and microbial systems. This workshop, provided by experts from across NIH, will give an overview of single-cell sequencing, especially in single cell RNA-seq, highlight tips and potential related to single-cell RNA sequencing, and introduce the major methods and tools available for single-cell RNA sequencing and analysis (both commercial and open source). Strategies and Methods in scRNA-seq Data Analysis Li Jia, Bioinformatician, NIH Library Single cell sequencing has reopened the definition of a cell’s identity and the ways in which that identity is regulated by the cell’s molecular circuitry. Learn the types of studies that are well suited for single cell sequencing analysis as well as how to design a single cell experiment. The speaker will also walk through the steps of a Single Cell RNA-Sequence (scRNA-seq) processing, common analysis strategies, and state-of-the-art analysis methods using R. Avoiding Common Pitfalls in Single Cell RNA-Seq Experiments Michael Kelly, Senior Scientist, Single Cell Analysis Facility, Frederick National Laboratory As the use of single cell sequencing becomes increasingly common, researchers may have a false sense that the technique is immune to issues that undermine the experiment, only to find limitations at the data analysis stage. The speaker will discuss various examples of potential data issues that can arise such as variability in number of targets datapoints, low gene detection, and technical batch effects. As part of this discussion the speaker will address some strategies for how to avoid them, and what they might look like in the final dataset. The speaker will also discuss some of the approaches used during a typical single cell RNA-Seq analysis workflow to help mitigate effects on your data. The Applications of Current Single Cell Sequencing Brian J. Henson, Senior Specialist, Illumina, Inc. The speaker will provide an overview and demonstration of the current single-cell applications available, including RNA, ATAC, CNV, TCR, Epitope, and spatial gene expression. Several examples from the literature will be highlighted as use cases for the tools. The speaker will conclude with a practical discussion on the utility and capacity of using the single-cell applications on the NovaSeq and the NextSeq 2000 benchtop sequencers. Single Cell Analysis in Partek Flow Xiaowen Wang, Senior Technical Support, Partek, Inc. Demonstration from a Partek scientist who will utilize Single Cell RNA-Seq data within Partek Flow to streamline Multi-omics data analysis. This GUI-based tool helps to overcome common analysis challenges on scRNA-Seq data and has built in data visualization options. Identifying and Interpreting the Human Liver Cellular Landscape using OmicSoft and IPA Eric Seiser, Senior Application Scientist, QIAGEN Bioinformatics The speaker will provide a practical example of how they utilized publicly available scRNA-Seq data in a research study. Specifically, the speaker processed scRNA-Seq human liver data using the OmicSoft single-cell analysis pipeline to identify numerous discrete cell populations. Gene signatures from these resident cells were then analyzed in Ingenuity Pathway Analysis to determine both shared and distinct cell biology in the context of pathways, regulation, and functional characteristics. These results provide insight into hepatic cells as well as the immune microenvironment within the liver. 2020-05-20 09:30:00 Online NIH Training Library 0 Bioinformatics Workshop: Single Cell RNA-Seq Analysis
148
Description
Students will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow. For example: import data; filter cells using interactive QA/QC charts; filter and normalize Single Cell RNA-Seq data; visualize cell populations using the interactive 3D t-SNE plot; overlay gene expression and pathway signatures on the 3D t-SNE plot; select and classify cells on the 3D t-SNE plot; detect ...Read More
Students will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow. For example: import data; filter cells using interactive QA/QC charts; filter and normalize Single Cell RNA-Seq data; visualize cell populations using the interactive 3D t-SNE plot; overlay gene expression and pathway signatures on the 3D t-SNE plot; select and classify cells on the 3D t-SNE plot; detect differentially expressed genes; filter a gene list; identify enriched KEGG pathway and/or GO terms; visualize cell-level results using heat maps, volcano plots, and violin plots. Benefits: Acquire working knowledge of tools available to NIH researchers for Single Cell RNA-Seq data analysis.
Details
Organizer
NIH Training Library
When
Thu, May 21, 2020 - 9:30 am - 12:00 pm
Where
Online
Students will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow. For example: import data; filter cells using interactive QA/QC charts; filter and normalize Single Cell RNA-Seq data; visualize cell populations using the interactive 3D t-SNE plot; overlay gene expression and pathway signatures on the 3D t-SNE plot; select and classify cells on the 3D t-SNE plot; detect differentially expressed genes; filter a gene list; identify enriched KEGG pathway and/or GO terms; visualize cell-level results using heat maps, volcano plots, and violin plots. Benefits: Acquire working knowledge of tools available to NIH researchers for Single Cell RNA-Seq data analysis. 2020-05-21 09:30:00 Online NIH Training Library 0 Hands-On Single Cell RNA-Seq Data Analysis in Partek Flow
149
Description
NIH BYOB is an informal, community-led talk series that meets once a month for presentations and discussions relating to topics of interest among the NIH bioinformatics community. Rather than focusing on research itself, the meetings are focused more on the practical side of doing bioinformatics work. All are welcome, regardless of experience!
NIH BYOB is an informal, community-led talk series that meets once a month for presentations and discussions relating to topics of interest among the NIH bioinformatics community. Rather than focusing on research itself, the meetings are focused more on the practical side of doing bioinformatics work. All are welcome, regardless of experience!
Details
When
Thu, May 21, 2020 - 12:30 pm - 1:30 pm
Where
Online
NIH BYOB is an informal, community-led talk series that meets once a month for presentations and discussions relating to topics of interest among the NIH bioinformatics community. Rather than focusing on research itself, the meetings are focused more on the practical side of doing bioinformatics work. All are welcome, regardless of experience! 2020-05-21 12:30:00 Online 0 Bring your own Bioinformatics: Making better usef of Biowulf storage
150
Description
Students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for RNA-Seq analysis. For example: import data from .fastq files; perform QA/QC (Pre-alignment QA/QC, Post-alignment QA/QC); trim bases; align reads to reference genome; quantify gene/transcript abundance; normalize gene counts; detect differentially expressed genes; filter a gene list; identify enriched KEGG pathway and/or GO terms; visualization (heat maps; volcano plots; PCA ...Read More
Students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for RNA-Seq analysis. For example: import data from .fastq files; perform QA/QC (Pre-alignment QA/QC, Post-alignment QA/QC); trim bases; align reads to reference genome; quantify gene/transcript abundance; normalize gene counts; detect differentially expressed genes; filter a gene list; identify enriched KEGG pathway and/or GO terms; visualization (heat maps; volcano plots; PCA scatterplot; dot plots; hierarchical clustering; chromosome view; and more). Benefits: Acquire working knowledge of tools available to NIH researchers for the start to finish RNA-Seq data analysis.
Details
Organizer
NIH Training Library
When
Thu, May 21, 2020 - 1:00 pm - 4:00 pm
Where
Online
Students will learn how to use the point-and-click interface in Partek Flow to go from raw data to experimental results for RNA-Seq analysis. For example: import data from .fastq files; perform QA/QC (Pre-alignment QA/QC, Post-alignment QA/QC); trim bases; align reads to reference genome; quantify gene/transcript abundance; normalize gene counts; detect differentially expressed genes; filter a gene list; identify enriched KEGG pathway and/or GO terms; visualization (heat maps; volcano plots; PCA scatterplot; dot plots; hierarchical clustering; chromosome view; and more). Benefits: Acquire working knowledge of tools available to NIH researchers for the start to finish RNA-Seq data analysis. 2020-05-21 13:00:00 Online NIH Training Library 0 Hands-On RNA-Seq Data Analysis in Partek Flow
920
Description
This workshop will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Topics covered will include: experimental approach, quality control, peak calling, and basic biological interpretation. No computer is required for this class, there is no hands-on work. 9:30-10:30 “ChIP-seq considerations” This section will focus on the importance of experimental design in ChIP-seq. Topics include: variations on ChIP-seq, inputs, replicates, the limitations of ChIP-seq, and the value of follow-up analyses. 10:30-12:00 “Analysis of ...Read More
This workshop will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Topics covered will include: experimental approach, quality control, peak calling, and basic biological interpretation. No computer is required for this class, there is no hands-on work. 9:30-10:30 “ChIP-seq considerations” This section will focus on the importance of experimental design in ChIP-seq. Topics include: variations on ChIP-seq, inputs, replicates, the limitations of ChIP-seq, and the value of follow-up analyses. 10:30-12:00 “Analysis of ChIP-seq data" This section will focus on the analytical pipeline that is implemented by CCBR and NCBR. Time will be spent discussing the tools used, why they were chosen, and how to make sense of their outputs. Example results will be shown. Topics include: quality control and read alignment, deduplication, normalization, gene coverage plotting, peak calling, motif analysis, and more. 12:00-12:30 Questions A WebEx recording of the webinar is available here And here are the slides used: BTEP_ChIP_May2020_Introduction and BTEP_ChIP_May2020_Analysis
Register
Organizer
BTEP
When
Thu, May 28, 2020 - 9:30 am - 12:30 pm
Where
Online Webinar
This workshop will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Topics covered will include: experimental approach, quality control, peak calling, and basic biological interpretation. No computer is required for this class, there is no hands-on work. 9:30-10:30 “ChIP-seq considerations” This section will focus on the importance of experimental design in ChIP-seq. Topics include: variations on ChIP-seq, inputs, replicates, the limitations of ChIP-seq, and the value of follow-up analyses. 10:30-12:00 “Analysis of ChIP-seq data" This section will focus on the analytical pipeline that is implemented by CCBR and NCBR. Time will be spent discussing the tools used, why they were chosen, and how to make sense of their outputs. Example results will be shown. Topics include: quality control and read alignment, deduplication, normalization, gene coverage plotting, peak calling, motif analysis, and more. 12:00-12:30 Questions A WebEx recording of the webinar is available here And here are the slides used: BTEP_ChIP_May2020_Introduction and BTEP_ChIP_May2020_Analysis 2020-05-28 09:30:00 Online Webinar Online Vishal Koparde (CCBR),Tovah Markowitz (NCBR),Paul Schaughency (NCBR) BTEP 0 ChIP-Seq Data Analysis: Probing DNA-Protein Interactions
151
Description
Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research. The hands-on training session will mainly focus on biological interpretation of expression data but will also ...Read More
Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research. The hands-on training session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics. Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects.
Details
Organizer
NIH Training Library
When
Thu, May 28, 2020 - 10:00 am - 3:00 pm
Where
Online
Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research. The hands-on training session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics. Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects. 2020-05-28 10:00:00 Online NIH Training Library 0 Ingenuity Pathway Analysis (Qiagen, IPA)
153
Description
After nearly two decades of improvements, the current human reference genome is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has been finished end to end, and hundreds of unresolved gaps persist. The remaining gaps include ribosomal rDNA arrays, large near-identical segmental duplications, and satellite DNA arrays. These regions harbor largely unexplored variation of unknown consequence, and their absence from the current reference genome can lead to experimental artifacts and ...Read More
After nearly two decades of improvements, the current human reference genome is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has been finished end to end, and hundreds of unresolved gaps persist. The remaining gaps include ribosomal rDNA arrays, large near-identical segmental duplications, and satellite DNA arrays. These regions harbor largely unexplored variation of unknown consequence, and their absence from the current reference genome can lead to experimental artifacts and hide true variants when re-sequencing additional human genomes. Using emerging, long-read sequencing technologies, the Telomere-to-Telomere consortium recently announced the completion of the first human chromosome, chromosome X. I will describe what it took to finish the first human chromosome "T2T" and report on the consortium's progress towards completing the rest of the human genome. Bio: Dr. Adam Phillippy is currently a Senior Investigator and head of the Genome Informatics Section at the National Human Genome Research Institute (NHGRI). His lab develops efficient computational methods for analyzing DNA sequencing data, including tools for genome assembly (Canu), genome alignment (MUMmer), genome clustering (Mash), microbial forensics (Parsnp), and metagenomics (Krona). He is a co-founder of the Telomere-to-Telomere Consortium and the Vertebrate Genomes Project, which seek to enable the complete and gapless assembly of human and all other vertebrate genomes. In 2019, he was awarded the US Presidential Early Career Award for Scientists and Engineers and was granted tenure at NHGRI. His lab's homepage can be found at genomeinformatics.githubio/
Details
When
Mon, Jun 01, 2020 - 3:00 pm - 4:00 pm
Where
Online
After nearly two decades of improvements, the current human reference genome is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has been finished end to end, and hundreds of unresolved gaps persist. The remaining gaps include ribosomal rDNA arrays, large near-identical segmental duplications, and satellite DNA arrays. These regions harbor largely unexplored variation of unknown consequence, and their absence from the current reference genome can lead to experimental artifacts and hide true variants when re-sequencing additional human genomes. Using emerging, long-read sequencing technologies, the Telomere-to-Telomere consortium recently announced the completion of the first human chromosome, chromosome X. I will describe what it took to finish the first human chromosome "T2T" and report on the consortium's progress towards completing the rest of the human genome. Bio: Dr. Adam Phillippy is currently a Senior Investigator and head of the Genome Informatics Section at the National Human Genome Research Institute (NHGRI). His lab develops efficient computational methods for analyzing DNA sequencing data, including tools for genome assembly (Canu), genome alignment (MUMmer), genome clustering (Mash), microbial forensics (Parsnp), and metagenomics (Krona). He is a co-founder of the Telomere-to-Telomere Consortium and the Vertebrate Genomes Project, which seek to enable the complete and gapless assembly of human and all other vertebrate genomes. In 2019, he was awarded the US Presidential Early Career Award for Scientists and Engineers and was granted tenure at NHGRI. His lab's homepage can be found at genomeinformatics.githubio/ 2020-06-01 15:00:00 Online 0 Finishing the human genome
154
Description
QIAGEN’s CLC Biomedical Genomics Workbench enables researchers to analyze NGS data without the use of command line. In this workshop, we will cover RNA-Seq and variant calling as applicable to human and other organisms. We will explore workflows within the Microbial Genomics Module, including tools for pathogen typing and metagenomics (16S and whole genome). Registrants will receive an email with information and instructions to install CLC Genomics Workbench before the class. Contact your systems ...Read More
QIAGEN’s CLC Biomedical Genomics Workbench enables researchers to analyze NGS data without the use of command line. In this workshop, we will cover RNA-Seq and variant calling as applicable to human and other organisms. We will explore workflows within the Microbial Genomics Module, including tools for pathogen typing and metagenomics (16S and whole genome). Registrants will receive an email with information and instructions to install CLC Genomics Workbench before the class. Contact your systems office or TS Bioinformatics for assistance with downloading the software. If you register the day before the class, you may not have time to download and properly install CLC. If you do not have the software installed, this training will be demo only.
Details
Organizer
NIH Training Library
When
Wed, Jun 03, 2020 - 10:00 am - 3:00 pm
Where
Online
QIAGEN’s CLC Biomedical Genomics Workbench enables researchers to analyze NGS data without the use of command line. In this workshop, we will cover RNA-Seq and variant calling as applicable to human and other organisms. We will explore workflows within the Microbial Genomics Module, including tools for pathogen typing and metagenomics (16S and whole genome). Registrants will receive an email with information and instructions to install CLC Genomics Workbench before the class. Contact your systems office or TS Bioinformatics for assistance with downloading the software. If you register the day before the class, you may not have time to download and properly install CLC. If you do not have the software installed, this training will be demo only. 2020-06-03 10:00:00 Online NIH Training Library 0 CLC Biomedical Workbench and Microbial Tools
155
Description
The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Single-cell analysis of healthy- and SARS-CoV-2-infected tissues offers a unique lens to identify these mechanisms. In an international integrated analysis of the Human Cell Atlas Lung Biological Network--which spans more than 100 single-cell and single-nucleus RNA-Seq datasets previously collected from healthy tissues and includes many previously unpublished studies--we identified the ...Read More
The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Single-cell analysis of healthy- and SARS-CoV-2-infected tissues offers a unique lens to identify these mechanisms. In an international integrated analysis of the Human Cell Atlas Lung Biological Network--which spans more than 100 single-cell and single-nucleus RNA-Seq datasets previously collected from healthy tissues and includes many previously unpublished studies--we identified the cell types throughout the body most likely to be susceptible to viral entry. In line with epidemiological observations, we also identified increased expression of key mediators of SARS-CoV-2 cellular entry associated with increasing age, male gender, and smoking. In addition, we identified a gene program shared by these cells that includes genes that may mediate viral entry and play key immune roles, such as IL6 and its receptor and co-receptor, IL1R; TNF-response pathways; and complement genes. Following these studies, as the pandemic reached our local Boston community, we have adapted existing sample-processing pipelines with our collaborators in Boston hospitals and are using single-cell and spatial genomics techniques to procure, process, and analyze blood and post-mortem tissue from COVID-19 patients. We are using these pipelines to examine the tissue and immune cellular response to COVID-19, particularly to understand the factors underlying its severity in some individuals, and will share our preliminary results.
Details
When
Wed, Jun 03, 2020 - 3:00 pm - 4:00 pm
Where
Online
The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Single-cell analysis of healthy- and SARS-CoV-2-infected tissues offers a unique lens to identify these mechanisms. In an international integrated analysis of the Human Cell Atlas Lung Biological Network--which spans more than 100 single-cell and single-nucleus RNA-Seq datasets previously collected from healthy tissues and includes many previously unpublished studies--we identified the cell types throughout the body most likely to be susceptible to viral entry. In line with epidemiological observations, we also identified increased expression of key mediators of SARS-CoV-2 cellular entry associated with increasing age, male gender, and smoking. In addition, we identified a gene program shared by these cells that includes genes that may mediate viral entry and play key immune roles, such as IL6 and its receptor and co-receptor, IL1R; TNF-response pathways; and complement genes. Following these studies, as the pandemic reached our local Boston community, we have adapted existing sample-processing pipelines with our collaborators in Boston hospitals and are using single-cell and spatial genomics techniques to procure, process, and analyze blood and post-mortem tissue from COVID-19 patients. We are using these pipelines to examine the tissue and immune cellular response to COVID-19, particularly to understand the factors underlying its severity in some individuals, and will share our preliminary results. 2020-06-03 15:00:00 Online 0 Toward Understanding COVID-19 Infection, Transmission, and Pathogenesis at Single-Cell Resolution with the Human Cell Atlas
157
Description
OpenCRAVAT is a an open source, scalable decision support system to support cancer variant and gene prioritization. It offers both command line functionality and a dynamic biologist-friendly GUI, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines and explore results in a richly detailed viewing environment. OpenCRAVAT is distinguished from similar tools by the amount and diversity of data resources and computational prediction methods available, ...Read More
OpenCRAVAT is a an open source, scalable decision support system to support cancer variant and gene prioritization. It offers both command line functionality and a dynamic biologist-friendly GUI, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines and explore results in a richly detailed viewing environment. OpenCRAVAT is distinguished from similar tools by the amount and diversity of data resources and computational prediction methods available, which span germline, somatic, common, rare, coding and non-coding variants. We have designed the OpenCRAVAT resource catalog to be open and modular to maximize community and developer involvement, and as a result the catalog is being actively developed and growing larger every month. OpenCRAVAT is available for local and server installation, on Biowulf, cloud instances, and our webserver at run.opencravat.org The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancergov/
Details
Organizer
CBIIT
When
Fri, Jun 05, 2020 - 11:00 am - 12:00 pm
Where
Online
OpenCRAVAT is a an open source, scalable decision support system to support cancer variant and gene prioritization. It offers both command line functionality and a dynamic biologist-friendly GUI, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines and explore results in a richly detailed viewing environment. OpenCRAVAT is distinguished from similar tools by the amount and diversity of data resources and computational prediction methods available, which span germline, somatic, common, rare, coding and non-coding variants. We have designed the OpenCRAVAT resource catalog to be open and modular to maximize community and developer involvement, and as a result the catalog is being actively developed and growing larger every month. OpenCRAVAT is available for local and server installation, on Biowulf, cloud instances, and our webserver at run.opencravat.org The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancergov/ 2020-06-05 11:00:00 Online CBIIT 0 Introduction to OpenCRAVAT
158
Description
Dr. Anuradha Budhu began her scientific career as a student researcher in the Undergraduate Biology Research Program (UBRP) at the University of Arizona, studying gene expression patterns in the larval brain of Drosophila Melanogaster. These studies were continued at The University of Adelaide in Australia through the UBRP Biomedical Research Abroad Vistas Open (Bravo!) Program funded by a Howard Hughes fellowship. After completion of her bachelor's honors degree in Biochemistry/Molecular and Cellular Biology from ...Read More
Dr. Anuradha Budhu began her scientific career as a student researcher in the Undergraduate Biology Research Program (UBRP) at the University of Arizona, studying gene expression patterns in the larval brain of Drosophila Melanogaster. These studies were continued at The University of Adelaide in Australia through the UBRP Biomedical Research Abroad Vistas Open (Bravo!) Program funded by a Howard Hughes fellowship. After completion of her bachelor's honors degree in Biochemistry/Molecular and Cellular Biology from the University of Arizona, Dr. Budhu joined the graduate program in Biochemistry at Cornell University, where she earned her Ph.D. studying the roles of retinoids in mammary carcinoma. In 2002, she joined the Laboratory of Human Carcinogenesis at NCI as a Cancer Research Training Award (CRTA) postdoctoral fellow in the Liver Carcinogenesis Section. She was promoted to Staff Scientist in 2007 and Associate Scientist in 2018. Dr. Budhu has authored over 50 peer-reviewed articles and 6 book chapters. Dr. Budhu is an active member of the Staff Scientist and Staff Clinician (SSSC) community at CCR, having served as Co-Chair of the SSSC Communications Committee and Editor-in-Chief and Founder of the SSSC newsletter (The Dossier). She is the recipient of several awards, including the CCR's Outstanding Postdoctoral Award, the NCI Director's Innovation Award and the NCI Director's Award. Dr. Budhu is currently a Senior Associate Scientist and the Program Manager for the NCI CCR Liver Cancer Program.
Details
When
Fri, Jun 05, 2020 - 12:00 pm - 1:00 pm
Where
Online
Dr. Anuradha Budhu began her scientific career as a student researcher in the Undergraduate Biology Research Program (UBRP) at the University of Arizona, studying gene expression patterns in the larval brain of Drosophila Melanogaster. These studies were continued at The University of Adelaide in Australia through the UBRP Biomedical Research Abroad Vistas Open (Bravo!) Program funded by a Howard Hughes fellowship. After completion of her bachelor's honors degree in Biochemistry/Molecular and Cellular Biology from the University of Arizona, Dr. Budhu joined the graduate program in Biochemistry at Cornell University, where she earned her Ph.D. studying the roles of retinoids in mammary carcinoma. In 2002, she joined the Laboratory of Human Carcinogenesis at NCI as a Cancer Research Training Award (CRTA) postdoctoral fellow in the Liver Carcinogenesis Section. She was promoted to Staff Scientist in 2007 and Associate Scientist in 2018. Dr. Budhu has authored over 50 peer-reviewed articles and 6 book chapters. Dr. Budhu is an active member of the Staff Scientist and Staff Clinician (SSSC) community at CCR, having served as Co-Chair of the SSSC Communications Committee and Editor-in-Chief and Founder of the SSSC newsletter (The Dossier). She is the recipient of several awards, including the CCR's Outstanding Postdoctoral Award, the NCI Director's Innovation Award and the NCI Director's Award. Dr. Budhu is currently a Senior Associate Scientist and the Program Manager for the NCI CCR Liver Cancer Program. 2020-06-05 12:00:00 Online 0 Multi-Omics Applications in Human Liver Cancer
156
Description
Extensive investigations have revealed intra-genomic variation in somatic mutation rates influenced by the sequence composition, structure, and local chromatin features of the genome. I will review the literature on mechanisms underlying the intra-genome mutational heterogeneity and relate it to cancer driver gene identification. I will then discuss a compelling hypothesis relating the variation in mutation rates with the exonic inclusion levels. This investigation has unexpectedly revealed some potentially important differences between the Whole Exome Sequence (...Read More
Extensive investigations have revealed intra-genomic variation in somatic mutation rates influenced by the sequence composition, structure, and local chromatin features of the genome. I will review the literature on mechanisms underlying the intra-genome mutational heterogeneity and relate it to cancer driver gene identification. I will then discuss a compelling hypothesis relating the variation in mutation rates with the exonic inclusion levels. This investigation has unexpectedly revealed some potentially important differences between the Whole Exome Sequence (WES) and Whole Genome Sequence (WGS) data in terms of detecting exonic mutations
Details
When
Mon, Jun 08, 2020 - 3:00 pm - 4:00 pm
Where
Online
Extensive investigations have revealed intra-genomic variation in somatic mutation rates influenced by the sequence composition, structure, and local chromatin features of the genome. I will review the literature on mechanisms underlying the intra-genome mutational heterogeneity and relate it to cancer driver gene identification. I will then discuss a compelling hypothesis relating the variation in mutation rates with the exonic inclusion levels. This investigation has unexpectedly revealed some potentially important differences between the Whole Exome Sequence (WES) and Whole Genome Sequence (WGS) data in terms of detecting exonic mutations 2020-06-08 15:00:00 Online 0 Mutation rate heterogeneity and its determinants in genome by Dr. Arashdeep Singh
159
Description
"This training will provide an introduction to ChIP sequencing data analysis followed by a tutorial showing the use of ChIPseq analysis workflow and preparing participants to independently run basic ChIPseq analysis for peak calling using a “point and click” approach on Galaxy platform. The hands-on exercise will run on a Galaxy platform using ChIP sequencing data. Participants will have a chance to: run quality control check on ChIPseq data, map raw reads to a reference ...Read More
"This training will provide an introduction to ChIP sequencing data analysis followed by a tutorial showing the use of ChIPseq analysis workflow and preparing participants to independently run basic ChIPseq analysis for peak calling using a “point and click” approach on Galaxy platform. The hands-on exercise will run on a Galaxy platform using ChIP sequencing data. Participants will have a chance to: run quality control check on ChIPseq data, map raw reads to a reference genome, generate alignment statistics and check mapping quality, call peaks using MACS, annotate peaks, and, visualize the enriched regions."
Details
Organizer
CBIIT
When
Tue, Jun 09, 2020 - 1:00 pm - 4:00 pm
Where
Online
"This training will provide an introduction to ChIP sequencing data analysis followed by a tutorial showing the use of ChIPseq analysis workflow and preparing participants to independently run basic ChIPseq analysis for peak calling using a “point and click” approach on Galaxy platform. The hands-on exercise will run on a Galaxy platform using ChIP sequencing data. Participants will have a chance to: run quality control check on ChIPseq data, map raw reads to a reference genome, generate alignment statistics and check mapping quality, call peaks using MACS, annotate peaks, and, visualize the enriched regions." 2020-06-09 13:00:00 Online CBIIT 0 ChIP-Seq Sequencing Data Analysis Workshop
160
Description
The presentation will cover cfDNA sequencing capabilities and what has been learned in quality control and ways to measure successful assays through the group's efforts to develop and validate processes for comprehensive (exome) analysis, as well as deep sequencing analysis for customized panels.
The presentation will cover cfDNA sequencing capabilities and what has been learned in quality control and ways to measure successful assays through the group's efforts to develop and validate processes for comprehensive (exome) analysis, as well as deep sequencing analysis for customized panels.
Details
When
Tue, Jun 09, 2020 - 2:00 pm - 3:00 pm
Where
Online
The presentation will cover cfDNA sequencing capabilities and what has been learned in quality control and ways to measure successful assays through the group's efforts to develop and validate processes for comprehensive (exome) analysis, as well as deep sequencing analysis for customized panels. 2020-06-09 14:00:00 Online 0 Comprehensive and Flexible Large Scale Liquid Biopsy Analysis Approaches in The Broad Genomics Platform
161
Description
This is an introductory level class for participants to familiarize themselves with Deep Learning concepts and techniques using MATLAB. These techniques can be used to solve complex problems related to images, signals, text and controls. The training session will be held on two days, June 9th and 10th, 2:00 - 4:00 pm.
This is an introductory level class for participants to familiarize themselves with Deep Learning concepts and techniques using MATLAB. These techniques can be used to solve complex problems related to images, signals, text and controls. The training session will be held on two days, June 9th and 10th, 2:00 - 4:00 pm.
Details
Organizer
NIH Training Library
When
Tue, Jun 09, 2020 - 2:00 pm - 4:00 pm
Where
Online
This is an introductory level class for participants to familiarize themselves with Deep Learning concepts and techniques using MATLAB. These techniques can be used to solve complex problems related to images, signals, text and controls. The training session will be held on two days, June 9th and 10th, 2:00 - 4:00 pm. 2020-06-09 14:00:00 Online NIH Training Library 0 Two-Day Hands-On Virtual Lab: Deep Learning
162
Description
This is an introductory level class for participants to familiarize themselves with Deep Learning concepts and techniques using MATLAB. These techniques can be used to solve complex problems related to images, signals, text and controls. The training session will be held on two days, June 9th and 10th, 2:00 - 4:00 pm.
This is an introductory level class for participants to familiarize themselves with Deep Learning concepts and techniques using MATLAB. These techniques can be used to solve complex problems related to images, signals, text and controls. The training session will be held on two days, June 9th and 10th, 2:00 - 4:00 pm.
Details
Organizer
NIH Training Library
When
Wed, Jun 10, 2020 - 2:00 pm - 4:00 pm
Where
Online
This is an introductory level class for participants to familiarize themselves with Deep Learning concepts and techniques using MATLAB. These techniques can be used to solve complex problems related to images, signals, text and controls. The training session will be held on two days, June 9th and 10th, 2:00 - 4:00 pm. 2020-06-10 14:00:00 Online NIH Training Library 0 Two-Day Hands-On Virtual Lab: Deep Learning
163
Description
This webinar provides an overview of taxonomies, including the origins of taxonomies, where they are used in everyday life, and why they are important. The overview will present existing taxonomies in multiple areas and subjects (science, art, biology, medicine, industry, and news). The participants will learn about the importance of organizing their files and data and basic best practices for taxonomy building. The course is designed as an introduction for those interested in learning more ...Read More
This webinar provides an overview of taxonomies, including the origins of taxonomies, where they are used in everyday life, and why they are important. The overview will present existing taxonomies in multiple areas and subjects (science, art, biology, medicine, industry, and news). The participants will learn about the importance of organizing their files and data and basic best practices for taxonomy building. The course is designed as an introduction for those interested in learning more about taxonomies and information organization or for those interested in a refresher on this topic. All are welcome to attend; the class was designed for researchers, fellows, post-docs, communications specialists, policy specialists, human resources specialists, and librarians.
Details
Organizer
NIH Training Library
When
Thu, Jun 11, 2020 - 11:00 am - 12:00 pm
Where
Online
This webinar provides an overview of taxonomies, including the origins of taxonomies, where they are used in everyday life, and why they are important. The overview will present existing taxonomies in multiple areas and subjects (science, art, biology, medicine, industry, and news). The participants will learn about the importance of organizing their files and data and basic best practices for taxonomy building. The course is designed as an introduction for those interested in learning more about taxonomies and information organization or for those interested in a refresher on this topic. All are welcome to attend; the class was designed for researchers, fellows, post-docs, communications specialists, policy specialists, human resources specialists, and librarians. 2020-06-11 11:00:00 Online NIH Training Library 0 Introduction to Taxonomies
164
Description
This training will provide an introduction to ChIP sequencing data analysis followed by a tutorial showing ChIP-seq analysis workflow and preparing participants to independently run basic ChIP-seq analysis for peak calling using a "point and click" approach on Galaxy platform. The hands-on exercise will run on a Galaxy platform using ChIP-seq data. Participants will have a chance to: run quality control on ChIP-seq data; map raw reads to a reference genome; generate alignment statistics and ...Read More
This training will provide an introduction to ChIP sequencing data analysis followed by a tutorial showing ChIP-seq analysis workflow and preparing participants to independently run basic ChIP-seq analysis for peak calling using a "point and click" approach on Galaxy platform. The hands-on exercise will run on a Galaxy platform using ChIP-seq data. Participants will have a chance to: run quality control on ChIP-seq data; map raw reads to a reference genome; generate alignment statistics and check mapping quality; call peaks using MACS; annotate peaks; visualize the enriched regions.
Details
Organizer
NIH Training Library
When
Fri, Jun 12, 2020 - 11:00 am - 2:00 pm
Where
Online
This training will provide an introduction to ChIP sequencing data analysis followed by a tutorial showing ChIP-seq analysis workflow and preparing participants to independently run basic ChIP-seq analysis for peak calling using a "point and click" approach on Galaxy platform. The hands-on exercise will run on a Galaxy platform using ChIP-seq data. Participants will have a chance to: run quality control on ChIP-seq data; map raw reads to a reference genome; generate alignment statistics and check mapping quality; call peaks using MACS; annotate peaks; visualize the enriched regions. 2020-06-12 11:00:00 Online NIH Training Library 0 Title: ChIP Sequencing Data Analysis
165
Description
Metabolomics as a high-throughput molecular phenotyping technique is growing across all domains in the life-sciences. The data processing and analysis is often performed with many programs using conventional computing solutions but little standardisation for interoperable and reproducible research. With increasing data size this becomes intractable for desktop computers. Cloud computing allows to instantiate on-demand resources (virtual servers, networks, storage), users only pay for the time the resources are used. Microservices can run in clouds that ...Read More
Metabolomics as a high-throughput molecular phenotyping technique is growing across all domains in the life-sciences. The data processing and analysis is often performed with many programs using conventional computing solutions but little standardisation for interoperable and reproducible research. With increasing data size this becomes intractable for desktop computers. Cloud computing allows to instantiate on-demand resources (virtual servers, networks, storage), users only pay for the time the resources are used. Microservices can run in clouds that can dynamically grow or shrink, enabling applications to be scaled. We developed a robust and performant data analysis infrastructure that integrates all necessary components. The software tools are encapsulated as Docker containers. To automate the instantiation of this cloud-portable microservice-based system, the PhenoMeNal project developed a Virtual Research Environment (https://portal.phenomenal-h2020.eu/) to deploy on some of the largest public cloud providers, including Amazon Web Services, Microsoft Azure, Google Cloud Platform and OpenStack-based scientific and private clouds. Kubernetes (https://kubernetes.io/) is used for container orchestration in the cloud. Galaxy (https://galaxyproject.org/) is used as interface for individual tools, users can share workflows and analysis histories. Together, we achieved a complete integration of several major metabolomics software suites resulting in a turn-key workflow for mass-spectrometry-based metabolomics. We will also discuss how the Galaxy-Kubernetes integration has evolved past the lifetime of PhenoMeNal through different projects and collaborations with the Galaxy community.
Details
When
Fri, Jun 12, 2020 - 3:00 pm - 4:00 pm
Where
Online
Metabolomics as a high-throughput molecular phenotyping technique is growing across all domains in the life-sciences. The data processing and analysis is often performed with many programs using conventional computing solutions but little standardisation for interoperable and reproducible research. With increasing data size this becomes intractable for desktop computers. Cloud computing allows to instantiate on-demand resources (virtual servers, networks, storage), users only pay for the time the resources are used. Microservices can run in clouds that can dynamically grow or shrink, enabling applications to be scaled. We developed a robust and performant data analysis infrastructure that integrates all necessary components. The software tools are encapsulated as Docker containers. To automate the instantiation of this cloud-portable microservice-based system, the PhenoMeNal project developed a Virtual Research Environment (https://portal.phenomenal-h2020.eu/) to deploy on some of the largest public cloud providers, including Amazon Web Services, Microsoft Azure, Google Cloud Platform and OpenStack-based scientific and private clouds. Kubernetes (https://kubernetes.io/) is used for container orchestration in the cloud. Galaxy (https://galaxyproject.org/) is used as interface for individual tools, users can share workflows and analysis histories. Together, we achieved a complete integration of several major metabolomics software suites resulting in a turn-key workflow for mass-spectrometry-based metabolomics. We will also discuss how the Galaxy-Kubernetes integration has evolved past the lifetime of PhenoMeNal through different projects and collaborations with the Galaxy community. 2020-06-12 15:00:00 Online 0 The PhenoMeNal cloud-aware e-Infrastructure
166
Description
Participants will learn about data loading, quality control, statistical analysis as well as biological contextualization of miRNA microarray data.
Participants will learn about data loading, quality control, statistical analysis as well as biological contextualization of miRNA microarray data.
Details
Organizer
NIH Training Library
When
Mon, Jun 15, 2020 - 9:00 am - 10:00 am
Where
Online
Participants will learn about data loading, quality control, statistical analysis as well as biological contextualization of miRNA microarray data. 2020-06-15 09:00:00 Online NIH Training Library 0 Single and Multi-Omics Analysis (GeneSpring Overview)
167
Description
This session will focus on NGS data, multi-omics analysis of array, and sequence data. Participants will work with text files in GeneSpring for NGS gene expression workflow and .vcf files for variant analysis.
This session will focus on NGS data, multi-omics analysis of array, and sequence data. Participants will work with text files in GeneSpring for NGS gene expression workflow and .vcf files for variant analysis.
Details
Organizer
NIH Training Library
When
Mon, Jun 15, 2020 - 10:30 am - 11:30 am
Where
Online
This session will focus on NGS data, multi-omics analysis of array, and sequence data. Participants will work with text files in GeneSpring for NGS gene expression workflow and .vcf files for variant analysis. 2020-06-15 10:30:00 Online NIH Training Library 0 Support for NGS Gene Expression Data, Variant Analysis, and Method Manager
168
Description
The capability to unambiguously and comprehensively identify thousands of metabolites and other chemicals in clinical samples, including the microbiome, will revolutionize the search for environmental, dietary, and metabolic determinants of health and disease. By comparison to near-comprehensive genetic information, comparatively little is understood of the totality of the human metabolome, largely due to insufficiencies in molecular identification methods. Through innovations in computational chemistry and advanced ion mobility separations coupled with mass spectrometry, we are overcoming ...Read More
The capability to unambiguously and comprehensively identify thousands of metabolites and other chemicals in clinical samples, including the microbiome, will revolutionize the search for environmental, dietary, and metabolic determinants of health and disease. By comparison to near-comprehensive genetic information, comparatively little is understood of the totality of the human metabolome, largely due to insufficiencies in molecular identification methods. Through innovations in computational chemistry and advanced ion mobility separations coupled with mass spectrometry, we are overcoming a significant, long standing obstacle in the field of metabolomics: the absence of methods for accurate and comprehensive identification of metabolites without relying on data derived from analysis of authentic reference compounds. We use gas-phase molecular properties that can be both predicted computationally with high accuracy and experimentally measured with high precision, and which can thus be used for comprehensive identification of the metabolome without the need for reference libraries constructed through experimental analysis of authentic chemical standards. The benefits and remaining limitations of the standards-free metabolomics approach will be demonstrated in a variety of examples, including in analysis of blinded chemical mixtures as a part of the EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT) and in analysis of plasma samples from individuals subjected to simulated shift work.
Details
When
Tue, Jun 16, 2020 - 11:00 am - 12:00 pm
Where
Online
The capability to unambiguously and comprehensively identify thousands of metabolites and other chemicals in clinical samples, including the microbiome, will revolutionize the search for environmental, dietary, and metabolic determinants of health and disease. By comparison to near-comprehensive genetic information, comparatively little is understood of the totality of the human metabolome, largely due to insufficiencies in molecular identification methods. Through innovations in computational chemistry and advanced ion mobility separations coupled with mass spectrometry, we are overcoming a significant, long standing obstacle in the field of metabolomics: the absence of methods for accurate and comprehensive identification of metabolites without relying on data derived from analysis of authentic reference compounds. We use gas-phase molecular properties that can be both predicted computationally with high accuracy and experimentally measured with high precision, and which can thus be used for comprehensive identification of the metabolome without the need for reference libraries constructed through experimental analysis of authentic chemical standards. The benefits and remaining limitations of the standards-free metabolomics approach will be demonstrated in a variety of examples, including in analysis of blinded chemical mixtures as a part of the EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT) and in analysis of plasma samples from individuals subjected to simulated shift work. 2020-06-16 11:00:00 Online 0 Shifting the Metabolomics Paradigm: Exploiting Computationally Predicted Metabolite Reference Data for Comprehensive Metabolomics
170
Description
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use ...Read More
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. This is a hands-on course with 24 laptops available in the library’s classroom. If you prefer to work from your own laptop, feel free to install the IGV browser in advance from here - software.broadinstitute.org/software/igv/download(the UCSV browser is web browser based).
Details
Organizer
NIH Training Library
When
Wed, Jun 17, 2020 - 1:00 pm - 2:30 pm
Where
Online
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. This is a hands-on course with 24 laptops available in the library’s classroom. If you prefer to work from your own laptop, feel free to install the IGV browser in advance from here - software.broadinstitute.org/software/igv/download(the UCSV browser is web browser based). 2020-06-17 13:00:00 Online NIH Training Library 0 Genome Browser
921
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. Soon after registering for this course, you will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete the self-guided video tutorial training BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Jun 18, 2020 - 2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. Soon after registering for this course, you will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete the self-guided video tutorial training BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-06-18 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion (June 18th)
171
Description
While the current state of knowledge surrounding cancer origin and tumorigenesis revolve around somatic mutation theory and cancer stem cells, there is still a gap in our understanding of the actual mechanism of cancer progression. In my presentation I will first give a detailed history of the embryological theory of cancer, from early ideas of the Trophoblastic Theory of Cancer to our current understanding of the developmental pathways involved in malignant progression. I will then ...Read More
While the current state of knowledge surrounding cancer origin and tumorigenesis revolve around somatic mutation theory and cancer stem cells, there is still a gap in our understanding of the actual mechanism of cancer progression. In my presentation I will first give a detailed history of the embryological theory of cancer, from early ideas of the Trophoblastic Theory of Cancer to our current understanding of the developmental pathways involved in malignant progression. I will then introduce my project of elucidating developmental pathways that are dormant in adult tissues but are re-activated in cancer. I will show results from our analysis of enhancers and genes, across multiple cancer types and tissues, that are down-regulated during development but up-regulated in cancer (and vice versa). We hypothesize that it is these pathways, which have a foundation in development, that are likely to drive cancer progression. Speaker: Arati Rajeevan (Post-bacc, Dr. Hannenhalli Lab).
Details
When
Mon, Jun 22, 2020 - 3:00 pm - 4:00 pm
Where
Online
While the current state of knowledge surrounding cancer origin and tumorigenesis revolve around somatic mutation theory and cancer stem cells, there is still a gap in our understanding of the actual mechanism of cancer progression. In my presentation I will first give a detailed history of the embryological theory of cancer, from early ideas of the Trophoblastic Theory of Cancer to our current understanding of the developmental pathways involved in malignant progression. I will then introduce my project of elucidating developmental pathways that are dormant in adult tissues but are re-activated in cancer. I will show results from our analysis of enhancers and genes, across multiple cancer types and tissues, that are down-regulated during development but up-regulated in cancer (and vice versa). We hypothesize that it is these pathways, which have a foundation in development, that are likely to drive cancer progression. Speaker: Arati Rajeevan (Post-bacc, Dr. Hannenhalli Lab). 2020-06-22 15:00:00 Online 0 Exploring the Developmental Origins of Cancer
172
Description
This training will provide an introduction to exome sequencing data analysis followed by tutorials showing the use of exome analysis workflow and preparing participants to independently run basic exome analysis for variant detection using a “point and click” approach on a public Galaxy platform. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. Participants will have a chance to: 1. Run quality control check on sequencing data; 2. Align the sequencing ...Read More
This training will provide an introduction to exome sequencing data analysis followed by tutorials showing the use of exome analysis workflow and preparing participants to independently run basic exome analysis for variant detection using a “point and click” approach on a public Galaxy platform. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. Participants will have a chance to: 1. Run quality control check on sequencing data; 2. Align the sequencing reads to a reference genome; 3. Generate alignment statistics and check mapping quality; 4. Identify variants; 5. Visualize the exome sequencing data and variants. For questions please contact Daoud Meerzaman
Details
Organizer
CBIIT
When
Tue, Jun 23, 2020 - 1:00 pm - 4:00 pm
Where
Online
This training will provide an introduction to exome sequencing data analysis followed by tutorials showing the use of exome analysis workflow and preparing participants to independently run basic exome analysis for variant detection using a “point and click” approach on a public Galaxy platform. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. Participants will have a chance to: 1. Run quality control check on sequencing data; 2. Align the sequencing reads to a reference genome; 3. Generate alignment statistics and check mapping quality; 4. Identify variants; 5. Visualize the exome sequencing data and variants. For questions please contact Daoud Meerzaman 2020-06-23 13:00:00 Online CBIIT 0 Whole Exome Sequencing Data Analysis Workshop
39
Description
In response to the urgent need for portable real-time epidemiological data analysis, DCEG scientists designed a real-time Mortality Tracker for COVID-19 and other diseases in the United States. This new data distribution method intersects epidemiology, Cloud and web computing to monitor the broader impact of COVID-19 on death rates across the United States. Co-Presenters: All from the NCI Division of Cancer Epidemiology and Genetics (DCEG) Jonas S. Almeida, Ph.D., Chief Data Scientist Amy Berrington, ...Read More
In response to the urgent need for portable real-time epidemiological data analysis, DCEG scientists designed a real-time Mortality Tracker for COVID-19 and other diseases in the United States. This new data distribution method intersects epidemiology, Cloud and web computing to monitor the broader impact of COVID-19 on death rates across the United States. Co-Presenters: All from the NCI Division of Cancer Epidemiology and Genetics (DCEG) Jonas S. Almeida, Ph.D., Chief Data Scientist Amy Berrington, Ph.D., Radiation Epidemiology Branch Chief Neal Freedman, Ph.D., Senior Investigator Meredith Shiels, Ph.D., Investigator Praphulla Bhawsar, MS, Data Engineer Bhaumik Patel, MS, Software Engineer
Details
Organizer
CBIIT
When
Wed, Jun 24, 2020 - 9:30 am - 10:30 am
Where
In-Person
In response to the urgent need for portable real-time epidemiological data analysis, DCEG scientists designed a real-time Mortality Tracker for COVID-19 and other diseases in the United States. This new data distribution method intersects epidemiology, Cloud and web computing to monitor the broader impact of COVID-19 on death rates across the United States. Co-Presenters: All from the NCI Division of Cancer Epidemiology and Genetics (DCEG) Jonas S. Almeida, Ph.D., Chief Data Scientist Amy Berrington, Ph.D., Radiation Epidemiology Branch Chief Neal Freedman, Ph.D., Senior Investigator Meredith Shiels, Ph.D., Investigator Praphulla Bhawsar, MS, Data Engineer Bhaumik Patel, MS, Software Engineer 2020-06-24 09:30:00 In-Person CBIIT 0 Real-time FAIR Mortality Tracking: A New Data Commons Approach in the Age of COVID
173
Description
In response to the urgent need for portable real-time epidemiological data analysis, DCEG scientists designed a real-time Mortality Tracker for COVID-19 and other diseases in the United States. This new data distribution method intersects epidemiology, Cloud and web computing to monitor the broader impact of COVID-19 on death rates across the United States. Co-Presenters: All from the NCI Division of Cancer Epidemiology and Genetics (DCEG) Jonas S. Almeida, Ph.D., Chief Data Scientist Amy Berrington, ...Read More
In response to the urgent need for portable real-time epidemiological data analysis, DCEG scientists designed a real-time Mortality Tracker for COVID-19 and other diseases in the United States. This new data distribution method intersects epidemiology, Cloud and web computing to monitor the broader impact of COVID-19 on death rates across the United States. Co-Presenters: All from the NCI Division of Cancer Epidemiology and Genetics (DCEG) Jonas S. Almeida, Ph.D., Chief Data Scientist Amy Berrington, Ph.D., Radiation Epidemiology Branch Chief Neal Freedman, Ph.D., Senior Investigator Meredith Shiels, Ph.D., Investigator Praphulla Bhawsar, MS, Data Engineer Bhaumik Patel, MS, Software Engineer
Details
When
Wed, Jun 24, 2020 - 9:30 am - 10:30 am
Where
Online
In response to the urgent need for portable real-time epidemiological data analysis, DCEG scientists designed a real-time Mortality Tracker for COVID-19 and other diseases in the United States. This new data distribution method intersects epidemiology, Cloud and web computing to monitor the broader impact of COVID-19 on death rates across the United States. Co-Presenters: All from the NCI Division of Cancer Epidemiology and Genetics (DCEG) Jonas S. Almeida, Ph.D., Chief Data Scientist Amy Berrington, Ph.D., Radiation Epidemiology Branch Chief Neal Freedman, Ph.D., Senior Investigator Meredith Shiels, Ph.D., Investigator Praphulla Bhawsar, MS, Data Engineer Bhaumik Patel, MS, Software Engineer 2020-06-24 09:30:00 Online 0 Real-time FAIR Mortality Tracking: A New Data Commons Approach in the Age of COVID
4
Description
Join us for a webinar: How to Analyze Single Cell RNA-Seq Data: Point, Click, Done Register: https://www.partek.com/webinar/how-to-analyze-single-cell-rna-seq-data-point-click-done/ Single cell mRNA sequencing allows for the identification of different cell subtypes in a progenitor population. During pancreatic development, Neurog3 positive cells are destined to become endocrine cells generating alpha cells, beta cells, and delta cells. However, single cell data analyses can ...Read More
Join us for a webinar: How to Analyze Single Cell RNA-Seq Data: Point, Click, Done Register: https://www.partek.com/webinar/how-to-analyze-single-cell-rna-seq-data-point-click-done/ Single cell mRNA sequencing allows for the identification of different cell subtypes in a progenitor population. During pancreatic development, Neurog3 positive cells are destined to become endocrine cells generating alpha cells, beta cells, and delta cells. However, single cell data analyses can be challenging for those without programming or command line experience. Partek® Flow® bioinformatic software resolves this challenge with a simple and intuitive graphical interface that doesn’t sacrifice flexibility or statistical power. In this webinar scientists from 1CellBio and Partek will discuss how you can use the inDrop™ platform and Partek Flow to simplify single cell RNA-Seq analysis. There will be a live demonstration of Partek Flow using an inDrop single cell mRNA sequencing dataset. You will learn how to:
  • identify cellular subtypes of Neurog3 positive cells
  • use information-rich and interactive visualizations to identify graph-based clustering and cluster classification
  • perform trajectory analysis of Neurog 3 positive cells
     
Details
When
Wed, Jun 24, 2020 - 11:00 am - 9:00 pm
Where
Online
Join us for a webinar: How to Analyze Single Cell RNA-Seq Data: Point, Click, Done Register: https://www.partek.com/webinar/how-to-analyze-single-cell-rna-seq-data-point-click-done/ Single cell mRNA sequencing allows for the identification of different cell subtypes in a progenitor population. During pancreatic development, Neurog3 positive cells are destined to become endocrine cells generating alpha cells, beta cells, and delta cells. However, single cell data analyses can be challenging for those without programming or command line experience. Partek® Flow® bioinformatic software resolves this challenge with a simple and intuitive graphical interface that doesn’t sacrifice flexibility or statistical power. In this webinar scientists from 1CellBio and Partek will discuss how you can use the inDrop™ platform and Partek Flow to simplify single cell RNA-Seq analysis. There will be a live demonstration of Partek Flow using an inDrop single cell mRNA sequencing dataset. You will learn how to: identify cellular subtypes of Neurog3 positive cells use information-rich and interactive visualizations to identify graph-based clustering and cluster classification perform trajectory analysis of Neurog 3 positive cells       2020-06-24 11:00:00 Online In-Person 0 Single Cell RNA-Seq Trajectory Analysis in Partek Flow
174
Description
Traditional methods of epidemic modeling continue to be used fruitfully for characterizing outbreaks and predicting the spread of disease in populations. However, these methods, typically rely on what are known as “compartment models”, requiring assumptions that are not necessarily sensitive to the ever-changing environmental, behavioral, temporospatial, and social phenomena that influence disease spread. Compartment models can be enriched by the judicious use of robust methods drawn from the field of artificial intelligence that allow us ...Read More
Traditional methods of epidemic modeling continue to be used fruitfully for characterizing outbreaks and predicting the spread of disease in populations. However, these methods, typically rely on what are known as “compartment models”, requiring assumptions that are not necessarily sensitive to the ever-changing environmental, behavioral, temporospatial, and social phenomena that influence disease spread. Compartment models can be enriched by the judicious use of robust methods drawn from the field of artificial intelligence that allow us to model more accurately and more quickly the population and disease dynamics that are central to developing policies for prevention, detection, and treatment. We will explore these approaches, including some that are currently in use as well as a proposal for novel, next-generation machine learning tools for epidemiologic investigation. John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director of the Penn Institute for Biomedical Informatics and is Past-Chair of the Graduate Group in Epidemiology and Biostatistics. Dr. Holmes has been recognized nationally and internationally for his work on developing and applying new artificial intelligence approaches to mining epidemiologic surveillance data. Dr. Holmes’ research interests are focused on the intersection of medical informatics and clinical research, specifically evolutionary computation and machine learning approaches to knowledge discovery in clinical databases, deep electronic phenotyping, interoperable information systems infrastructures for epidemiologic surveillance, and their application to a broad array of clinical domains, including cardiology and pulmonary medicine. He has served as the co-lead of the Governance Core for the SPAN project, a scalable distributed research network, and participates in the FDA Sentinel Initiative.
Details
When
Wed, Jun 24, 2020 - 1:00 pm - 2:00 pm
Where
Online
Traditional methods of epidemic modeling continue to be used fruitfully for characterizing outbreaks and predicting the spread of disease in populations. However, these methods, typically rely on what are known as “compartment models”, requiring assumptions that are not necessarily sensitive to the ever-changing environmental, behavioral, temporospatial, and social phenomena that influence disease spread. Compartment models can be enriched by the judicious use of robust methods drawn from the field of artificial intelligence that allow us to model more accurately and more quickly the population and disease dynamics that are central to developing policies for prevention, detection, and treatment. We will explore these approaches, including some that are currently in use as well as a proposal for novel, next-generation machine learning tools for epidemiologic investigation. John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director of the Penn Institute for Biomedical Informatics and is Past-Chair of the Graduate Group in Epidemiology and Biostatistics. Dr. Holmes has been recognized nationally and internationally for his work on developing and applying new artificial intelligence approaches to mining epidemiologic surveillance data. Dr. Holmes’ research interests are focused on the intersection of medical informatics and clinical research, specifically evolutionary computation and machine learning approaches to knowledge discovery in clinical databases, deep electronic phenotyping, interoperable information systems infrastructures for epidemiologic surveillance, and their application to a broad array of clinical domains, including cardiology and pulmonary medicine. He has served as the co-lead of the Governance Core for the SPAN project, a scalable distributed research network, and participates in the FDA Sentinel Initiative. 2020-06-24 13:00:00 Online 0 NLM Ada Lovelace Computational Health Lecture Series "AI in the Age of COVID-19: Computational Tools for a Pandemic"
922
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. Soon after registering for this course, you will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete the self-guided video tutorial training BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Jun 25, 2020 - 2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. Soon after registering for this course, you will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete the self-guided video tutorial training BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-06-25 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion (June 25th)
175
Description
This training will provide an introduction to exome sequencing data analysis followed by tutorials showing the use of exome analysis workflow and preparing participants to independently run basic exome analysis for variant detection using a "point and click" approach on a public Galaxy platform. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. Participants will have a chance to: run quality control check on sequencing data; align the sequencing ...Read More
This training will provide an introduction to exome sequencing data analysis followed by tutorials showing the use of exome analysis workflow and preparing participants to independently run basic exome analysis for variant detection using a "point and click" approach on a public Galaxy platform. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. Participants will have a chance to: run quality control check on sequencing data; align the sequencing reads to a reference genome; generate alignment statistics and check mapping quality; identify variants; visualize the exome sequencing data and variants.
Details
Organizer
NIH Training Library
When
Fri, Jun 26, 2020 - 11:00 am - 2:00 pm
Where
Online
This training will provide an introduction to exome sequencing data analysis followed by tutorials showing the use of exome analysis workflow and preparing participants to independently run basic exome analysis for variant detection using a "point and click" approach on a public Galaxy platform. The hands-on exercise will run on a Galaxy platform using Illumina paired-end exome sequencing data. Participants will have a chance to: run quality control check on sequencing data; align the sequencing reads to a reference genome; generate alignment statistics and check mapping quality; identify variants; visualize the exome sequencing data and variants. 2020-06-26 11:00:00 Online NIH Training Library 0 Exome Sequencing Data Analysis
152
Description
The overarching goal of our work is to implement data analysis and informatics tools for integration of biological mass spectrometry data (proteomics and metabolomics) with genomic / transcriptomic information to advance cancer research. We are utilizing the Galaxy platform and extending our earlier work developing Galaxy for proteomics (Galaxy-P project), to create a unified environment for implementing and disseminating multi-omic tools and validated workflows. The software resources and activities central to our work include: 1) Proteogenomics tools, ...Read More
The overarching goal of our work is to implement data analysis and informatics tools for integration of biological mass spectrometry data (proteomics and metabolomics) with genomic / transcriptomic information to advance cancer research. We are utilizing the Galaxy platform and extending our earlier work developing Galaxy for proteomics (Galaxy-P project), to create a unified environment for implementing and disseminating multi-omic tools and validated workflows. The software resources and activities central to our work include: 1) Proteogenomics tools, which integrate genomic, transcriptomic and mass spectrometry-based proteomics data to characterize protein sequence variants contributing to cancer. This work includes development of tools for interpreting the impact of these variants, including a multi-omics visualization platform which acts as a Galaxy-plugin for visualizing proteogenomics results; 2) Metaproteomics tools, for characterizing proteins expressed by microbial communities found in host environments which may contribute to cancer development and/or progression, including advanced tools for visualizing and exploring taxonomy-function interactions which may drive host response; 3) Tool development for quantitative mass spectrometry-based metabolomics, which includes customized tools for advanced statistical analysis and also deploying new tools for metabolite identification from high-resolution mass spectrometry data; 4) A focus on dissemination activities, to promote our informatics resources to the cancer research community. This includes conducting training workshops, establishing online tutorials and documentation, and making software available and accessible through a variety of avenues, including publicly available gateways and via containers for local install. In this webinar, we will provide short demonstrations of these tools and workflows and their applications, and provide information how to access these resources. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancergov/
Details
Organizer
CBIIT
When
Sun, Jun 28, 2020 - 10:00 am - 11:00 am
Where
Online
The overarching goal of our work is to implement data analysis and informatics tools for integration of biological mass spectrometry data (proteomics and metabolomics) with genomic / transcriptomic information to advance cancer research. We are utilizing the Galaxy platform and extending our earlier work developing Galaxy for proteomics (Galaxy-P project), to create a unified environment for implementing and disseminating multi-omic tools and validated workflows. The software resources and activities central to our work include: 1) Proteogenomics tools, which integrate genomic, transcriptomic and mass spectrometry-based proteomics data to characterize protein sequence variants contributing to cancer. This work includes development of tools for interpreting the impact of these variants, including a multi-omics visualization platform which acts as a Galaxy-plugin for visualizing proteogenomics results; 2) Metaproteomics tools, for characterizing proteins expressed by microbial communities found in host environments which may contribute to cancer development and/or progression, including advanced tools for visualizing and exploring taxonomy-function interactions which may drive host response; 3) Tool development for quantitative mass spectrometry-based metabolomics, which includes customized tools for advanced statistical analysis and also deploying new tools for metabolite identification from high-resolution mass spectrometry data; 4) A focus on dissemination activities, to promote our informatics resources to the cancer research community. This includes conducting training workshops, establishing online tutorials and documentation, and making software available and accessible through a variety of avenues, including publicly available gateways and via containers for local install. In this webinar, we will provide short demonstrations of these tools and workflows and their applications, and provide information how to access these resources. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website itcr.cancergov/ 2020-06-28 10:00:00 Online CBIIT 0 Introduction to Galaxy-P multi-omics
3
Description
Registration: https://www.nihlibrary.nih.gov/training/overview-common-statistical-tests-6 In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This session will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature.
Registration: https://www.nihlibrary.nih.gov/training/overview-common-statistical-tests-6 In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This session will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature.
Details
Organizer
NIH Training Library
When
Wed, Jul 01, 2020 - 1:00 pm - 10:00 pm
Where
Online
Registration: https://www.nihlibrary.nih.gov/training/overview-common-statistical-tests-6 In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This session will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature. 2020-07-01 13:00:00 Online Online NIH Training Library 0 Overview of Common Statistical Tests
926
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. Soon after registering for this course, you will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Jul 02, 2020 - 2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. Soon after registering for this course, you will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-07-02 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion (July 2nd)
5
Description
In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using ...Read More
In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining. GAIL also provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. This webinar will provide the overview of GAIL and illustrate its use for investigation and visualization of gene-gene networks using various examples including gene signatures associated with breast cancer. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website. Register: cbiit.webex.com/cbiit/onstage/g.php?MTID=e911f33fab865b525caa93682076ce66f
Details
Organizer
CBIIT
When
Tue, Jul 07, 2020 - 11:00 am - 12:00 pm
Where
Online
In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining. GAIL also provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. This webinar will provide the overview of GAIL and illustrate its use for investigation and visualization of gene-gene networks using various examples including gene signatures associated with breast cancer. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website. Register: cbiit.webex.com/cbiit/onstage/g.php?MTID=e911f33fab865b525caa93682076ce66f 2020-07-07 11:00:00 Online Online CBIIT 0 Introduction to Gene-Gene Association Inference Based Literature (GAIL)
38
Description
In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using ...Read More
In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining. GAIL also provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. This webinar will provide the overview of GAIL and illustrate its use for investigation and visualization of gene-gene networks using various examples including gene signatures associated with breast cancer. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website.
Details
Organizer
CBIIT
When
Tue, Jul 07, 2020 - 11:00 am - 12:00 pm
Where
Online
In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining. GAIL also provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. This webinar will provide the overview of GAIL and illustrate its use for investigation and visualization of gene-gene networks using various examples including gene signatures associated with breast cancer. The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website. 2020-07-07 11:00:00 Online CBIIT 0 Introduction to Gene-Gene Association Inferenced Based Literature (GAIL)
923
Description
The recent breakthroughs in high-throughput technologies have resulted in a vast amount of big-data resources. However, it remains a significant challenge to transfer the knowledge from the public data to a new research project due to study design gaps and differences in data organization. Focusing on cancer immunology research, we integrated large-scale omics data and developed web platforms with interactive analysis modules. In the first project, we processed the omics data for over 33K samples ...Read More
The recent breakthroughs in high-throughput technologies have resulted in a vast amount of big-data resources. However, it remains a significant challenge to transfer the knowledge from the public data to a new research project due to study design gaps and differences in data organization. Focusing on cancer immunology research, we integrated large-scale omics data and developed web platforms with interactive analysis modules. In the first project, we processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these datasets with three interactive analysis modules, our web platform TIDE has enabled public data reuse in hypothesis generation, biomarker optimization, and patient stratification in immune-oncology research. In the second project, we have manually labeled 20,608 cytokine and growth factor treatment profiles from the NCBI GEO and ArrayExpress databases. With these curated datasets, our web platform CellSig can reveal the differential expression change of query genes upon diverse cell signals and predict the cytokine and growth factor response in a user's transcriptomic data input. WEBCAST LINK: https://nci.rev.vbrick.com/#/webcasts/bioinformaticsdistinguishedspeaker Attendee’s Instructions: We recommend that you join at least 10 minutes before the meeting begins. PLEASE NOTE: You must disconnect from NIH VPN and close other running applications on your computer to watch the webcast.
  1. Instructions on how to view the Webcast:
  2. Disconnect from NIH VPN and close other running applications on your computer. You may also watch this webcast using your smartphone.
  3. Click on Webcast link provided.
  4. Enter your first and last name in “Display Name” field.
  5. Enter your NIH email in “Email” field.
  6. Check the consent/authorization box.
  7. Sign in as guest.
  8. When the event is visible, click “unmute” in the upper right corner of the event window to hear the streamed audio from your computer speakers. Use your computer to adjust the volume.
  9. Click on the video icon (third icon from the bottom right) to move the presenter’s video.
  10. A recording will be made available on the BTEP website within 1-2 days.
  11. To view the event on an iPhone/iPad or Android device, click on the webcast link and follow the sign in as guest steps.
Register
Organizer
BTEP
When
Thu, Jul 09, 2020 - 3:00 pm - 4:00 pm
Where
Online Webinar
The recent breakthroughs in high-throughput technologies have resulted in a vast amount of big-data resources. However, it remains a significant challenge to transfer the knowledge from the public data to a new research project due to study design gaps and differences in data organization. Focusing on cancer immunology research, we integrated large-scale omics data and developed web platforms with interactive analysis modules. In the first project, we processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these datasets with three interactive analysis modules, our web platform TIDE has enabled public data reuse in hypothesis generation, biomarker optimization, and patient stratification in immune-oncology research. In the second project, we have manually labeled 20,608 cytokine and growth factor treatment profiles from the NCBI GEO and ArrayExpress databases. With these curated datasets, our web platform CellSig can reveal the differential expression change of query genes upon diverse cell signals and predict the cytokine and growth factor response in a user's transcriptomic data input. WEBCAST LINK: https://nci.rev.vbrick.com/#/webcasts/bioinformaticsdistinguishedspeaker Attendee’s Instructions: We recommend that you join at least 10 minutes before the meeting begins. PLEASE NOTE: You must disconnect from NIH VPN and close other running applications on your computer to watch the webcast. Instructions on how to view the Webcast: Disconnect from NIH VPN and close other running applications on your computer. You may also watch this webcast using your smartphone. Click on Webcast link provided. Enter your first and last name in “Display Name” field. Enter your NIH email in “Email” field. Check the consent/authorization box. Sign in as guest. When the event is visible, click “unmute” in the upper right corner of the event window to hear the streamed audio from your computer speakers. Use your computer to adjust the volume. Click on the video icon (third icon from the bottom right) to move the presenter’s video. A recording will be made available on the BTEP website within 1-2 days. To view the event on an iPhone/iPad or Android device, click on the webcast link and follow the sign in as guest steps. 2020-07-09 15:00:00 Online Webinar Online Peng Jiang (CCR/CDSL) BTEP 0 Data-driven Approaches to Identify the Regulators of the Anticancer Immune Response
2
Description
This webinar provides an overview of taxonomies, including the origins of taxonomies, where they are used in everyday life, and why they are important. The overview will present existing taxonomies in multiple areas and subjects (science, art, biology, medicine, industry, and news). The participants will learn about the importance of organizing their files and data and basic best practices for taxonomy building. The course is designed as an introduction for those interested in learning more ...Read More
This webinar provides an overview of taxonomies, including the origins of taxonomies, where they are used in everyday life, and why they are important. The overview will present existing taxonomies in multiple areas and subjects (science, art, biology, medicine, industry, and news). The participants will learn about the importance of organizing their files and data and basic best practices for taxonomy building. The course is designed as an introduction for those interested in learning more about taxonomies and information organization or for those interested in a refresher on this topic. All are welcome to attend; the class was designed for researchers, fellows, post-docs, communications specialists, policy specialists, human resources specialists, and librarians.
Details
Organizer
NIH Training Library
When
Fri, Jul 10, 2020 - 11:00 pm - 11:50 pm
Where
Online
This webinar provides an overview of taxonomies, including the origins of taxonomies, where they are used in everyday life, and why they are important. The overview will present existing taxonomies in multiple areas and subjects (science, art, biology, medicine, industry, and news). The participants will learn about the importance of organizing their files and data and basic best practices for taxonomy building. The course is designed as an introduction for those interested in learning more about taxonomies and information organization or for those interested in a refresher on this topic. All are welcome to attend; the class was designed for researchers, fellows, post-docs, communications specialists, policy specialists, human resources specialists, and librarians. 2020-07-10 23:00:00 Online Online NIH Training Library 0 Introduction to Taxonomies
6
Description
Join us for this Webinar session, where Partek scientist will provide an overview and updates for the latest version of Partek Flow followed with a live demo for Single Cell RNA-Seq data analysis. During the live demo, the Partek scientist will go through the steps on analyzing and visualizing a Single Cell RNA-Seq data using the newly implemented and released data visualization tool in Partek Flow – Data Viewer. The new Data Viewer provides more flexible ...Read More
Join us for this Webinar session, where Partek scientist will provide an overview and updates for the latest version of Partek Flow followed with a live demo for Single Cell RNA-Seq data analysis. During the live demo, the Partek scientist will go through the steps on analyzing and visualizing a Single Cell RNA-Seq data using the newly implemented and released data visualization tool in Partek Flow – Data Viewer. The new Data Viewer provides more flexible and easier ways to integrate information collected from the data and helps biologists discover more biological meanings with the point-and-click user interface. Agenda: Presentation: Partek Flow Overview and Updates in the Latest Version Live Demo: Single Cell RNA-Seq Data Analysis and Visualization in Partek Flow • Data Import • QA/QC • Data Filter and Normalization • Clustering Analysis • Dimension Reduction and Visualize in 2/3 D • Differential Expression
Details
Organizer
CBIIT
When
Tue, Jul 14, 2020 - 11:00 am - 12:00 pm
Where
Online
Join us for this Webinar session, where Partek scientist will provide an overview and updates for the latest version of Partek Flow followed with a live demo for Single Cell RNA-Seq data analysis. During the live demo, the Partek scientist will go through the steps on analyzing and visualizing a Single Cell RNA-Seq data using the newly implemented and released data visualization tool in Partek Flow – Data Viewer. The new Data Viewer provides more flexible and easier ways to integrate information collected from the data and helps biologists discover more biological meanings with the point-and-click user interface. Agenda: Presentation: Partek Flow Overview and Updates in the Latest Version Live Demo: Single Cell RNA-Seq Data Analysis and Visualization in Partek Flow • Data Import • QA/QC • Data Filter and Normalization • Clustering Analysis • Dimension Reduction and Visualize in 2/3 D • Differential Expression 2020-07-14 11:00:00 Online Online CBIIT 0 RNA-Seq Data Analysis using Partek Flow
927
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. Soon after registering for this course, you will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Jul 16, 2020 - 2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. Soon after registering for this course, you will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-07-16 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion (July 16th)
924
Description
A panel of scientists with expertise in Single-Cell RNA-Seq will answer your questions about Single-Cell RNA-Seq experimental design. Please submit your question by July 15, to https://btep.ccr.cancer.gov/question/single_cell_rna_seq/ If you do not already have an account on the BTEP website, you will need to create one to ask a question. Any problems contact ncibtep@nih....Read More
A panel of scientists with expertise in Single-Cell RNA-Seq will answer your questions about Single-Cell RNA-Seq experimental design. Please submit your question by July 15, to https://btep.ccr.cancer.gov/question/single_cell_rna_seq/ If you do not already have an account on the BTEP website, you will need to create one to ask a question. Any problems contact ncibtep@nih.gov On the day of the meeting, please join us at: https://nci.rev.vbrick.com/#/webcasts/singlecellpart1 Attendee’s Instructions: We recommend that you join at least 10 minutes before the meeting begins. PLEASE NOTE: You must disconnect from NIH VPN and close other running applications on your computer to watch the webcast.  Instructions on how to view the Webcast
  1. Disconnect from NIH VPN and close other running applications on your computer. You may also watch this webcast using your smartphone.
  2. Click on Webcast link provided.
  3. Enter your first and last name in “Display Name” field.
  4. Enter your NIH email in “Email” field.
  5. Check the consent/authorization box.
  6. Sign in as guest.
  7. When the event is visible, click “unmute” in the upper right corner of the event window to hear the streamed audio from your computer speakers. Use your computer to adjust the volume.
  8. Click on the video icon (third icon from the bottom right) to move the presenter’s video.
  9. A recording will be made available on the BTEP website within 1-2 days.
To view the event on an iPhone/iPad or Android device, click on the webcast link and follow the sign in as guest steps.  
Register
Organizer
BTEP
When
Thu, Jul 23, 2020 - 10:00 am - 12:00 pm
Where
Online Webinar
A panel of scientists with expertise in Single-Cell RNA-Seq will answer your questions about Single-Cell RNA-Seq experimental design. Please submit your question by July 15, to https://btep.ccr.cancer.gov/question/single_cell_rna_seq/ If you do not already have an account on the BTEP website, you will need to create one to ask a question. Any problems contact ncibtep@nih.gov On the day of the meeting, please join us at: https://nci.rev.vbrick.com/#/webcasts/singlecellpart1 Attendee’s Instructions: We recommend that you join at least 10 minutes before the meeting begins. PLEASE NOTE: You must disconnect from NIH VPN and close other running applications on your computer to watch the webcast.  Instructions on how to view the Webcast Disconnect from NIH VPN and close other running applications on your computer. You may also watch this webcast using your smartphone. Click on Webcast link provided. Enter your first and last name in “Display Name” field. Enter your NIH email in “Email” field. Check the consent/authorization box. Sign in as guest. When the event is visible, click “unmute” in the upper right corner of the event window to hear the streamed audio from your computer speakers. Use your computer to adjust the volume. Click on the video icon (third icon from the bottom right) to move the presenter’s video. A recording will be made available on the BTEP website within 1-2 days. To view the event on an iPhone/iPad or Android device, click on the webcast link and follow the sign in as guest steps.   2020-07-23 10:00:00 Online Webinar Online Michael Kelly (SCAF),Stefan Cordes (NHLBI) BTEP 0 Virtual Single Cell Town Hall: Experimental Design for Optimal Results in Single-Cell RNA-Seq
928
Description
THIS EVENT HAS BEEN CANCELLED
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their ...Read More
THIS EVENT HAS BEEN CANCELLED
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. One week before the scheduled date of the live Discussion webinar for this course, registration will close and all registrants (including you) will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Jul 23, 2020 - 2:00 pm - 3:00 pm
Where
Online Webinar
THIS EVENT HAS BEEN CANCELLEDSingle-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. One week before the scheduled date of the live Discussion webinar for this course, registration will close and all registrants (including you) will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-07-23 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Single-cell RNA-Seq Analysis on NIDAP – Tutorial & Discussion (July 23rd) - CANCELLED
7
Description
The GDC RNA-Seq Analysis pipeline quantifies protein-coding gene expression. RNA-Seq data is aligned to the reference genome to detect splice junctions and then re-aligned to increase quality. Gene expression quantification and fusion detection are performed on the aligned reads. This webinar will provide an in depth look at how RNA-Seq data is processed at the GDC and made available to the research community. JOIN FROM A VIDEO SYSTEM OR APPLICATION Dial sip: 1604488349@cbiit.webex.com ...Read More
The GDC RNA-Seq Analysis pipeline quantifies protein-coding gene expression. RNA-Seq data is aligned to the reference genome to detect splice junctions and then re-aligned to increase quality. Gene expression quantification and fusion detection are performed on the aligned reads. This webinar will provide an in depth look at how RNA-Seq data is processed at the GDC and made available to the research community. JOIN FROM A VIDEO SYSTEM OR APPLICATION Dial sip: 1604488349@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. JOIN BY PHONE 1-650-479-3207 Call-in toll number (US/Canada) Global call-in numbers(link is external) | Can't join the meeting?(link is external) Tap here to call (mobile phones only, hosts not supported): tel:%2B1-650-479-3207,,*01*857220328%23%23*01
Details
When
Mon, Jul 27, 2020 - 2:00 pm - 3:00 pm
Where
Online
The GDC RNA-Seq Analysis pipeline quantifies protein-coding gene expression. RNA-Seq data is aligned to the reference genome to detect splice junctions and then re-aligned to increase quality. Gene expression quantification and fusion detection are performed on the aligned reads. This webinar will provide an in depth look at how RNA-Seq data is processed at the GDC and made available to the research community. JOIN FROM A VIDEO SYSTEM OR APPLICATION Dial sip: 1604488349@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. JOIN BY PHONE 1-650-479-3207 Call-in toll number (US/Canada) Global call-in numbers(link is external) | Can't join the meeting?(link is external) Tap here to call (mobile phones only, hosts not supported): tel:%2B1-650-479-3207,,*01*857220328%23%23*01 2020-07-27 14:00:00 Online In-Person 0 Genome Data Commons RNA-Seq Data Processing
8
Description
A tool for detection of somatic, subclonal, mosaic, and germline CNVs from sequencing Tool Acronym: CNVnator Tool URL: github.com/abyzovlab/CNVnator Code Repository github.com/abyzovlab/CNVnator Grant Info projectreporter.nih.gov/project_info_description.cfm?aid=9502057&icde=39748518&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball= The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the ...Read More
A tool for detection of somatic, subclonal, mosaic, and germline CNVs from sequencing Tool Acronym: CNVnator Tool URL: github.com/abyzovlab/CNVnator Code Repository github.com/abyzovlab/CNVnator Grant Info projectreporter.nih.gov/project_info_description.cfm?aid=9502057&icde=39748518&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball= The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website. Register: cbiit.webex.com/cbiit/onstage/g.php?MTID=e5f2ce410b37da489bfee497919a6aabd
Details
Organizer
CBIIT
When
Tue, Jul 28, 2020 - 12:00 pm - 1:00 pm
Where
Online
A tool for detection of somatic, subclonal, mosaic, and germline CNVs from sequencing Tool Acronym: CNVnator Tool URL: github.com/abyzovlab/CNVnator Code Repository github.com/abyzovlab/CNVnator Grant Info projectreporter.nih.gov/project_info_description.cfm?aid=9502057&icde=39748518&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball= The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website. Register: cbiit.webex.com/cbiit/onstage/g.php?MTID=e5f2ce410b37da489bfee497919a6aabd 2020-07-28 12:00:00 Online Online CBIIT 0 Introduction to CNVnator ITCR tool
925
Description
A panel of scientists with expertise in Single-Cell RNA-Seq will answer your questions on analysis of Single-Cell RNA-Seq data. Questions must be submitted in advance of the meeting by Weds, July 15. Please submit your question to : https://btep.ccr.cancer.gov/question/single_cell_rna_seq/ If you do not already have an account on the BTEP website, you will need to ...Read More
A panel of scientists with expertise in Single-Cell RNA-Seq will answer your questions on analysis of Single-Cell RNA-Seq data. Questions must be submitted in advance of the meeting by Weds, July 15. Please submit your question to : https://btep.ccr.cancer.gov/question/single_cell_rna_seq/ If you do not already have an account on the BTEP website, you will need to create one to submit a question. Any problems contact ncibtep@nih.gov On the day of the meeting, please join us at WEBCAST LINK: https://nci.rev.vbrick.com/#/webcasts/singlecellpart2 Attendee’s Instructions: We recommend that you join at least 10 minutes before the meeting begins PLEASE NOTE: You must disconnect from NIH VPN and close other running applications on your computer to watch the webcast.  Instructions on how to view the Webcast:
  1. Disconnect from NIH VPN and close other running applications on your computer. You may also watch this webcast using your smartphone.
  2. Click on Webcast link provided.
  3. Enter your first and last name in “Display Name” field.
  4. Enter your NIH email in “Email” field.
  5. Check the consent/authorization box.
  6. Sign in as guest.
  7. When the event is visible, click “unmute” in the upper right corner of the event window to hear the streamed audio from your computer speakers. Use your computer to adjust the volume.
  8. Click on the video icon (third icon from the bottom right) to move the presenter’s video.
  9. A recording will be made available on the BTEP website within 1-2 days.
To view the event on an iPhone/iPad or Android device, click on the webcast link and follow the sign in as guest steps  
Register
Organizer
BTEP
When
Thu, Jul 30, 2020 - 10:00 am - 12:00 pm
Where
Online Webinar
A panel of scientists with expertise in Single-Cell RNA-Seq will answer your questions on analysis of Single-Cell RNA-Seq data. Questions must be submitted in advance of the meeting by Weds, July 15. Please submit your question to : https://btep.ccr.cancer.gov/question/single_cell_rna_seq/ If you do not already have an account on the BTEP website, you will need to create one to submit a question. Any problems contact ncibtep@nih.gov On the day of the meeting, please join us at WEBCAST LINK: https://nci.rev.vbrick.com/#/webcasts/singlecellpart2 Attendee’s Instructions: We recommend that you join at least 10 minutes before the meeting begins PLEASE NOTE: You must disconnect from NIH VPN and close other running applications on your computer to watch the webcast.  Instructions on how to view the Webcast: Disconnect from NIH VPN and close other running applications on your computer. You may also watch this webcast using your smartphone. Click on Webcast link provided. Enter your first and last name in “Display Name” field. Enter your NIH email in “Email” field. Check the consent/authorization box. Sign in as guest. When the event is visible, click “unmute” in the upper right corner of the event window to hear the streamed audio from your computer speakers. Use your computer to adjust the volume. Click on the video icon (third icon from the bottom right) to move the presenter’s video. A recording will be made available on the BTEP website within 1-2 days. To view the event on an iPhone/iPad or Android device, click on the webcast link and follow the sign in as guest steps   2020-07-30 10:00:00 Online Webinar Online Michael Kelly (SCAF),Cihan Oguz (NCBR),Vicky Chen (NCBR),Nathan Wong (CCBR),Stefan Cordes (NHLBI),Kimia Dadkhah (SCAF) BTEP 0 Virtual Single Cell Town Hall: Single-Cell RNA-Seq Data Analysis
929
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. One week before the scheduled date of the live Discussion webinar for this course, registration will close and all registrants (including you) will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Jul 30, 2020 - 2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. One week before the scheduled date of the live Discussion webinar for this course, registration will close and all registrants (including you) will receive access to the tutorials and documentation, as well as a calendar invitation to your Discussion class. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-07-30 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion (July 30th)
20
Description
Exploratory data analysis (EDA) is an important step in statistics that enables the validation, summarization, and hypothesis generation in relation to a dataset. This workshop will present multiple tools used to perform EDA tasks and show you how to apply them on three public clinical datasets. The workshop will also demonstrate tools that can assist you in exploration and characterization of your data sets. WebEx: https://bit.ly/3gzqGUnRead More
Exploratory data analysis (EDA) is an important step in statistics that enables the validation, summarization, and hypothesis generation in relation to a dataset. This workshop will present multiple tools used to perform EDA tasks and show you how to apply them on three public clinical datasets. The workshop will also demonstrate tools that can assist you in exploration and characterization of your data sets. WebEx: https://bit.ly/3gzqGUn
Details
Organizer
NCI Data Science Learning Exchange
When
Thu, Aug 06, 2020 - 1:00 pm - 2:30 pm
Where
Online
Exploratory data analysis (EDA) is an important step in statistics that enables the validation, summarization, and hypothesis generation in relation to a dataset. This workshop will present multiple tools used to perform EDA tasks and show you how to apply them on three public clinical datasets. The workshop will also demonstrate tools that can assist you in exploration and characterization of your data sets. WebEx: https://bit.ly/3gzqGUn 2020-08-06 13:00:00 Online Online NCI Data Science Learning Exchange 0 Exploratory Data Analysis (EDA) for Clinical Datasets
12
Description
Copy Number Variation (CNV) is a type of structural variation in which sections of the genome are duplicated or deleted. Associated with being causal mutations for disease phenotypes, it is important in clinical and research settings to identify CNV events in samples or datasets. Golden Helix’s VarSeq-CNV (VS-CNV) is a calling algorithm that uses one testing paradigm to provide a true simplification of a clinical workflow. VarSeq incorporates the ability to accurately call and ...Read More
Copy Number Variation (CNV) is a type of structural variation in which sections of the genome are duplicated or deleted. Associated with being causal mutations for disease phenotypes, it is important in clinical and research settings to identify CNV events in samples or datasets. Golden Helix’s VarSeq-CNV (VS-CNV) is a calling algorithm that uses one testing paradigm to provide a true simplification of a clinical workflow. VarSeq incorporates the ability to accurately call and annotate CNVs and evaluate germline and somatic mutations according to the Association for Molecular Pathology (AMP) guidelines. Golden Helix CancerKB is an AMP workflow feature that streamlines the analysis time and report generation. Attendees will learn the following: setting up the VS-CNV caller using BAM files from whole exome data; filtering down to high quality; understanding high confidence CNV events; annotating CNVs using publicly curated catalogs and databases; adding clinically relevant CNVs to the VSClinical AMP workflow; and utilizing Golden Helix CancerKB to obtain expert-curated interpretations. This class will demonstrate updated software features, provide insights into best practice workflows, and show participants how to implement the software into a pipeline solution. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/varseq-copy-number-variation-vs-cnv-caller-and-golden-helix-cancerkb-amp-workflow-0
Details
Organizer
NIH Training Library
When
Mon, Aug 10, 2020 - 9:00 am - 10:00 am
Where
Online
Copy Number Variation (CNV) is a type of structural variation in which sections of the genome are duplicated or deleted. Associated with being causal mutations for disease phenotypes, it is important in clinical and research settings to identify CNV events in samples or datasets. Golden Helix’s VarSeq-CNV (VS-CNV) is a calling algorithm that uses one testing paradigm to provide a true simplification of a clinical workflow. VarSeq incorporates the ability to accurately call and annotate CNVs and evaluate germline and somatic mutations according to the Association for Molecular Pathology (AMP) guidelines. Golden Helix CancerKB is an AMP workflow feature that streamlines the analysis time and report generation. Attendees will learn the following: setting up the VS-CNV caller using BAM files from whole exome data; filtering down to high quality; understanding high confidence CNV events; annotating CNVs using publicly curated catalogs and databases; adding clinically relevant CNVs to the VSClinical AMP workflow; and utilizing Golden Helix CancerKB to obtain expert-curated interpretations. This class will demonstrate updated software features, provide insights into best practice workflows, and show participants how to implement the software into a pipeline solution. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/varseq-copy-number-variation-vs-cnv-caller-and-golden-helix-cancerkb-amp-workflow-0 2020-08-10 09:00:00 Online Online NIH Training Library 0 VarSeq Copy Number Variation Caller and Golden Helix CancerKB for AMP Workflow
9
Description
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training ...Read More
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. By the end of this class, students should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; describe how to save data in R. Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class. View Details and Register: introduction-r-data-types
Details
Organizer
NIH Training Library
When
Tue, Aug 11, 2020 - 1:00 pm - 2:00 pm
Where
Online
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. By the end of this class, students should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; describe how to save data in R. Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class. View Details and Register: introduction-r-data-types 2020-08-11 13:00:00 Online Online NIH Training Library 0 Introduction to R Data Types
11
Description
Speakers: Nikhita Amod Gogate and Daniel Lyman, George Washington University Data on biomarkers are being collected for a wide range of cancers and stored in data sets around the world. Staying abreast of these discoveries can be difficult. OncoMX was designed to give researchers a single source for managing discoveries in cancer mutation and expression biomarkers. A collaborative project between The George Washington University, NASA’s Jet Propulsion Laboratory, the Swiss Institute of Bioinformatics, and ...Read More
Speakers: Nikhita Amod Gogate and Daniel Lyman, George Washington University Data on biomarkers are being collected for a wide range of cancers and stored in data sets around the world. Staying abreast of these discoveries can be difficult. OncoMX was designed to give researchers a single source for managing discoveries in cancer mutation and expression biomarkers. A collaborative project between The George Washington University, NASA’s Jet Propulsion Laboratory, the Swiss Institute of Bioinformatics, and the University of Delaware, OncoMX offers researchers a centralized, unified, and integrated web resource for tracking and comparing the latest findings on biomarkers to improve cancer detection, prevention, and treatment. This training will demonstrate how to use OncoMX to explore cancer biomarker data, and how to mine existing literature using specifically designed tools and pathways. For questions, contact Daoud Meerzaman.
Details
Organizer
CBIIT
When
Tue, Aug 11, 2020 - 1:30 pm - 2:30 pm
Where
Online
Speakers: Nikhita Amod Gogate and Daniel Lyman, George Washington University Data on biomarkers are being collected for a wide range of cancers and stored in data sets around the world. Staying abreast of these discoveries can be difficult. OncoMX was designed to give researchers a single source for managing discoveries in cancer mutation and expression biomarkers. A collaborative project between The George Washington University, NASA’s Jet Propulsion Laboratory, the Swiss Institute of Bioinformatics, and the University of Delaware, OncoMX offers researchers a centralized, unified, and integrated web resource for tracking and comparing the latest findings on biomarkers to improve cancer detection, prevention, and treatment. This training will demonstrate how to use OncoMX to explore cancer biomarker data, and how to mine existing literature using specifically designed tools and pathways. For questions, contact Daoud Meerzaman. 2020-08-11 13:30:00 Online Online CBIIT 0 OncoMX: Cancer mutation and expression biomarkers
22
Description
Presented by Mark Benson and Keith Hughitt Webex Meeting Number: 160 826 5254 Meeting Password: AGtSsrY?335 URL: https://cbiit.webex.com/cbiit/j.php?MTID=m7bec33bde208c131727bf43c9246cbdd
Presented by Mark Benson and Keith Hughitt Webex Meeting Number: 160 826 5254 Meeting Password: AGtSsrY?335 URL: https://cbiit.webex.com/cbiit/j.php?MTID=m7bec33bde208c131727bf43c9246cbdd
Details
Organizer
BYOB
When
Thu, Aug 13, 2020 - 12:00 pm - 1:00 pm
Where
Online
Presented by Mark Benson and Keith Hughitt Webex Meeting Number: 160 826 5254 Meeting Password: AGtSsrY?335 URL: https://cbiit.webex.com/cbiit/j.php?MTID=m7bec33bde208c131727bf43c9246cbdd 2020-08-13 12:00:00 Online Online BYOB 0 An introduction to NLP for biomedical text analysis
23
Description
Speaker: Dr. Mary Carrington The exceptional influence of HLA and related genes located in the MHC on human disease relative to that of variation in the rest of the genome has now been clearly demonstrated by GWAS. While allelic effects of these genes that confer specificity for peptide presentation have been well-appreciated for decades, more recently it has become evident that other allele-specific features, such as expression levels of HLA molecules or their interaction with ...Read More
Speaker: Dr. Mary Carrington The exceptional influence of HLA and related genes located in the MHC on human disease relative to that of variation in the rest of the genome has now been clearly demonstrated by GWAS. While allelic effects of these genes that confer specificity for peptide presentation have been well-appreciated for decades, more recently it has become evident that other allele-specific features, such as expression levels of HLA molecules or their interaction with binding partners during assembly in the endoplasmic reticulum (ER), also impact HLA function differentially. The level of dependence on the assembly factor tapasin is highly variable across the common HLA class I allotypes. Tapasin dependence influences peptide repertoire both in terms of its size and affinity of presented peptides, where tapasin dependent HLA allotypes present a smaller repertoire of peptides, but likely with higher average affinity compared to tapasin independent allotypes. We have ascribed tapasin dependence coefficients to each common HLA allele and an overall dependence score given the entire HLA genotype present in each subject. I will discuss the impact of these allele-specific properties on HLA function and their effects on disease outcomes. Download ICS Join Zoom Meeting Meeting ID: 916 3499 0819 One tap mobile +13017158592,,91634990819# US (Germantown) +19294362866,,91634990819# US (New York) Dial by your location +1 301 715 8592 US (Germantown) +1 929 436 2866 US (New York) +1 312 626 6799 US (Chicago) +1 346 248 7799 US (Houston) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma) Meeting ID: 916 3499 0819 Find your local number: https://umd.zoom.us/u/ac38Ygg0S7
Details
Organizer
CDSL
When
Mon, Aug 17, 2020 - 3:00 pm - 4:00 pm
Where
Online
Speaker: Dr. Mary Carrington The exceptional influence of HLA and related genes located in the MHC on human disease relative to that of variation in the rest of the genome has now been clearly demonstrated by GWAS. While allelic effects of these genes that confer specificity for peptide presentation have been well-appreciated for decades, more recently it has become evident that other allele-specific features, such as expression levels of HLA molecules or their interaction with binding partners during assembly in the endoplasmic reticulum (ER), also impact HLA function differentially. The level of dependence on the assembly factor tapasin is highly variable across the common HLA class I allotypes. Tapasin dependence influences peptide repertoire both in terms of its size and affinity of presented peptides, where tapasin dependent HLA allotypes present a smaller repertoire of peptides, but likely with higher average affinity compared to tapasin independent allotypes. We have ascribed tapasin dependence coefficients to each common HLA allele and an overall dependence score given the entire HLA genotype present in each subject. I will discuss the impact of these allele-specific properties on HLA function and their effects on disease outcomes. Download ICS Join Zoom Meeting Meeting ID: 916 3499 0819 One tap mobile +13017158592,,91634990819# US (Germantown) +19294362866,,91634990819# US (New York) Dial by your location +1 301 715 8592 US (Germantown) +1 929 436 2866 US (New York) +1 312 626 6799 US (Chicago) +1 346 248 7799 US (Houston) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma) Meeting ID: 916 3499 0819 Find your local number: https://umd.zoom.us/u/ac38Ygg0S7 2020-08-17 15:00:00 Online Online CDSL 0 The impact of immunogenetic variation on human health and disease
13
Description
Single nucleotide polymorphism (SNP) & Variation Suite (SVS) is an analytic tool created to empower researchers to perform complex analyses and visualizations on genomic and phenotypic data. Genome-Wide Association Studies (GWAS) continues to be an effective method for identifying disease susceptible genes in humans and other organisms. Attendees will learn how SVS can be used to perform GWAS and genomic prediction, how to analyze high-quality SNPs by performing the association test, how to use quality control ...Read More
Single nucleotide polymorphism (SNP) & Variation Suite (SVS) is an analytic tool created to empower researchers to perform complex analyses and visualizations on genomic and phenotypic data. Genome-Wide Association Studies (GWAS) continues to be an effective method for identifying disease susceptible genes in humans and other organisms. Attendees will learn how SVS can be used to perform GWAS and genomic prediction, how to analyze high-quality SNPs by performing the association test, how to use quality control metrics, and how to use genomic prediction with K-Fold to estimate which genotypes best predict a desired phenotype. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/snp-and-variation-suite-svs-genome-wide-association-studies-gwas-0
Details
Organizer
NIH Training Library
When
Thu, Aug 20, 2020 - 10:00 am - 11:00 am
Where
Online
Single nucleotide polymorphism (SNP) & Variation Suite (SVS) is an analytic tool created to empower researchers to perform complex analyses and visualizations on genomic and phenotypic data. Genome-Wide Association Studies (GWAS) continues to be an effective method for identifying disease susceptible genes in humans and other organisms. Attendees will learn how SVS can be used to perform GWAS and genomic prediction, how to analyze high-quality SNPs by performing the association test, how to use quality control metrics, and how to use genomic prediction with K-Fold to estimate which genotypes best predict a desired phenotype. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/snp-and-variation-suite-svs-genome-wide-association-studies-gwas-0 2020-08-20 10:00:00 Online Online NIH Training Library 0 SNP and Variation Suite for Genome-Wide Association Studies
21
Description
Abstract: Machine learning (ML) has emerged as an essential tool for building models which can be used to predict clinical outcomes for age-related diseases. A significant challenge of ML is knowing which algorithms and parameter settings are appropriate for a given data set and the hidden patterns to be discovered. Automated ML or AutoML has emerged to take the guesswork out of selecting an ML method by letting the computer optimize the method and parameter ...Read More
Abstract: Machine learning (ML) has emerged as an essential tool for building models which can be used to predict clinical outcomes for age-related diseases. A significant challenge of ML is knowing which algorithms and parameter settings are appropriate for a given data set and the hidden patterns to be discovered. Automated ML or AutoML has emerged to take the guesswork out of selecting an ML method by letting the computer optimize the method and parameter selection. This makes ML more accessible to non-experts. We introduce here the tree-based pipeline optimization tool (TPOT) for automated discovery of ML pipelines. We applied TPOT to predicting coronary artery disease (CAD) phenotypes using 73 nuclear magnetic resonance-derived lipoprotein and metabolite profiles and 27 demographic and clinical features in the Angiography and Genes Study (ANGES) with a sample size of 925 subjects. We show that TPOT outperforms a standard grid search approach for predicting CAD outcomes and identifies pipelines unlikely to be selected by human experts. The TPOT software is programmed in Python and freely available as open-source from Github (https://github.com/EpistasisLab/tpot). Speaker: Jason Moore, Ph.D., Director of the Penn Institute for Biomedical Informatics, Philadelphia, PA Register here with NIH Webex Events: https://nih.webex.com/nih/onstage/g.php?MTID=e7b2a0e8c2dd5ddf486316a551fe555d4
Details
Organizer
NIA
When
Fri, Aug 21, 2020 - 12:00 pm - 1:00 pm
Where
Online
Abstract: Machine learning (ML) has emerged as an essential tool for building models which can be used to predict clinical outcomes for age-related diseases. A significant challenge of ML is knowing which algorithms and parameter settings are appropriate for a given data set and the hidden patterns to be discovered. Automated ML or AutoML has emerged to take the guesswork out of selecting an ML method by letting the computer optimize the method and parameter selection. This makes ML more accessible to non-experts. We introduce here the tree-based pipeline optimization tool (TPOT) for automated discovery of ML pipelines. We applied TPOT to predicting coronary artery disease (CAD) phenotypes using 73 nuclear magnetic resonance-derived lipoprotein and metabolite profiles and 27 demographic and clinical features in the Angiography and Genes Study (ANGES) with a sample size of 925 subjects. We show that TPOT outperforms a standard grid search approach for predicting CAD outcomes and identifies pipelines unlikely to be selected by human experts. The TPOT software is programmed in Python and freely available as open-source from Github (https://github.com/EpistasisLab/tpot). Speaker: Jason Moore, Ph.D., Director of the Penn Institute for Biomedical Informatics, Philadelphia, PA Register here with NIH Webex Events: https://nih.webex.com/nih/onstage/g.php?MTID=e7b2a0e8c2dd5ddf486316a551fe555d4 2020-08-21 12:00:00 Online Online NIA 0 Automated Machine Learning Analysis of Metabolomic Data
24
Description
Speaker: Dr. Helen Shearman, Ph.D., Geneious Prime Registration link Description Geneious Prime offers fundamental molecular biology and sequence analysis tools designed to make bioinformatics work easier and more collaborative. In this 1-hour introductory webinar, participants will learn the range of tools available in this software suite, including a look at molecular cloning and primer design, chromatogram and next generation sequencing analysis, DNA and protein sequence alignment, the National Center for Biotechnology Information (NCBI) Basic ...Read More
Speaker: Dr. Helen Shearman, Ph.D., Geneious Prime Registration link Description Geneious Prime offers fundamental molecular biology and sequence analysis tools designed to make bioinformatics work easier and more collaborative. In this 1-hour introductory webinar, participants will learn the range of tools available in this software suite, including a look at molecular cloning and primer design, chromatogram and next generation sequencing analysis, DNA and protein sequence alignment, the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST), and more. Attendees also will see how to search, share, and automate their work as part of the Geneious Prime workflows. Register
Details
Organizer
CBIIT
When
Wed, Aug 26, 2020 - 3:00 pm - 4:00 pm
Where
Online
Speaker: Dr. Helen Shearman, Ph.D., Geneious Prime Registration link Description Geneious Prime offers fundamental molecular biology and sequence analysis tools designed to make bioinformatics work easier and more collaborative. In this 1-hour introductory webinar, participants will learn the range of tools available in this software suite, including a look at molecular cloning and primer design, chromatogram and next generation sequencing analysis, DNA and protein sequence alignment, the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST), and more. Attendees also will see how to search, share, and automate their work as part of the Geneious Prime workflows. Register 2020-08-26 15:00:00 Online Online CBIIT 0 Introduction to Geneious Prime
25
Description
Speaker: Dr. Christian Zinser, Precigen Bioinformatics Registration The Genomatix Genome Analyzer (GGA) aids in ...Read More
Speaker: Dr. Christian Zinser, Precigen Bioinformatics Registration The Genomatix Genome Analyzer (GGA) aids in analyzing next generation sequencing (NGS) data from ChIP, RNA, DNA, methylation, or small RNA sequencing. This introductory webinar gives an overview of the GGA’s functionalities and biological background data, and explores tasks such as expression analysis, verifying and generating networks and pathways, and examining the literature and binding site motifs, among others. The demonstration includes a look at the GGA’s intuitive web-based graphical user interface. For questions, contact Daoud Meerzaman.
Details
Organizer
CBIIT
When
Fri, Aug 28, 2020 - 10:00 am - 11:00 am
Where
Online
Speaker: Dr. Christian Zinser, Precigen Bioinformatics Registration The Genomatix Genome Analyzer (GGA) aids in analyzing next generation sequencing (NGS) data from ChIP, RNA, DNA, methylation, or small RNA sequencing. This introductory webinar gives an overview of the GGA’s functionalities and biological background data, and explores tasks such as expression analysis, verifying and generating networks and pathways, and examining the literature and binding site motifs, among others. The demonstration includes a look at the GGA’s intuitive web-based graphical user interface. For questions, contact Daoud Meerzaman. 2020-08-28 10:00:00 Online Online CBIIT 0 Introduction to Genomatix Genome Analyzer
10
Description
The training will overview the current status of pathway tools, with focus on software available to NIH community. It will discuss the biological interoperation of mutation and expression data in the context of pathways, pathway databases, and popular web-based pathway tools. The pathway software GSEA, g: Profiler, and PATHVIEW will be used to demonstrate how to run the pathway analysis of expression data against GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes), and ...Read More
The training will overview the current status of pathway tools, with focus on software available to NIH community. It will discuss the biological interoperation of mutation and expression data in the context of pathways, pathway databases, and popular web-based pathway tools. The pathway software GSEA, g: Profiler, and PATHVIEW will be used to demonstrate how to run the pathway analysis of expression data against GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes), and MSigDB (Molecular Signatures Database). https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/pathway-analysis-3
Details
Organizer
NIH Training Library
When
Fri, Aug 28, 2020 - 11:30 am - 12:30 pm
Where
Online
The training will overview the current status of pathway tools, with focus on software available to NIH community. It will discuss the biological interoperation of mutation and expression data in the context of pathways, pathway databases, and popular web-based pathway tools. The pathway software GSEA, g: Profiler, and PATHVIEW will be used to demonstrate how to run the pathway analysis of expression data against GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes), and MSigDB (Molecular Signatures Database). https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/pathway-analysis-3 2020-08-28 11:30:00 Online Online NIH Training Library 0 Pathway Analysis
41
Description
Speaker: Dr. Luke Gilbert is an Assistant Professor in the Department of Urology, the Helen Diller Family Comprehensive Cancer Center and the Innovative Genomics Institute at the University of California, San Francisco. Dr. Gilbert was an early pioneer in repurposed CRISPR systems that are used to turn genes on (CRISPRa) and off (CRISPRi) by editing the epigenome. The Gilbert lab continues to develop new epigenetic editing approaches as well as new CRISPR functional genomics platforms. ...Read More
Speaker: Dr. Luke Gilbert is an Assistant Professor in the Department of Urology, the Helen Diller Family Comprehensive Cancer Center and the Innovative Genomics Institute at the University of California, San Francisco. Dr. Gilbert was an early pioneer in repurposed CRISPR systems that are used to turn genes on (CRISPRa) and off (CRISPRi) by editing the epigenome. The Gilbert lab continues to develop new epigenetic editing approaches as well as new CRISPR functional genomics platforms. Recently, the Gilbert lab developed two next-generation CRISPR functional genomics platforms to systematically and quantitatively map genetic interactions. The Gilbert lab is focusing on utilizing our expertise to tackle big problems in cancer such as metastasis and drug resistance in cancer. They use genome-scale screens, genetic interaction mapping and genome engineering to identify genetic and epigenetic causes underlying why some patients are cured and others are not by cancer therapy. Registration
Details
When
Fri, Aug 28, 2020 - 12:00 pm - 1:00 pm
Where
Online
Speaker: Dr. Luke Gilbert is an Assistant Professor in the Department of Urology, the Helen Diller Family Comprehensive Cancer Center and the Innovative Genomics Institute at the University of California, San Francisco. Dr. Gilbert was an early pioneer in repurposed CRISPR systems that are used to turn genes on (CRISPRa) and off (CRISPRi) by editing the epigenome. The Gilbert lab continues to develop new epigenetic editing approaches as well as new CRISPR functional genomics platforms. Recently, the Gilbert lab developed two next-generation CRISPR functional genomics platforms to systematically and quantitatively map genetic interactions. The Gilbert lab is focusing on utilizing our expertise to tackle big problems in cancer such as metastasis and drug resistance in cancer. They use genome-scale screens, genetic interaction mapping and genome engineering to identify genetic and epigenetic causes underlying why some patients are cured and others are not by cancer therapy. Registration 2020-08-28 12:00:00 Online Online 0 Mapping Cancer Genetics at Single Cell Resolution
33
Description
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer Moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Tracy Fullerton, Director of USC Game Innovation Lab, pursuing experimental design of games in cultural realms including art, science, politics, and learning. Corrie Painter, Patient advocate and research scientist at the Broad Institute; ...Read More
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer Moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Tracy Fullerton, Director of USC Game Innovation Lab, pursuing experimental design of games in cultural realms including art, science, politics, and learning. Corrie Painter, Patient advocate and research scientist at the Broad Institute; directs Count Me In, partnering researchers with patients to speed cancer discoveries. Program Details
Details
When
Mon, Aug 31, 2020 - 1:00 pm - 2:30 pm
Where
Online
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer Moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Tracy Fullerton, Director of USC Game Innovation Lab, pursuing experimental design of games in cultural realms including art, science, politics, and learning. Corrie Painter, Patient advocate and research scientist at the Broad Institute; directs Count Me In, partnering researchers with patients to speed cancer discoveries. Program Details 2020-08-31 13:00:00 Online Online 0 Dataviz + Cancer Microlab 1
43
Description
Alternative splicing (AS) and alternative back-splicing (ABS) are essential to understanding the development of cancer and may play a role as a target of personalized cancer therapeutics. However, the existing reference transcriptome annotation databases are far from being complete. Thus, detecting novel splicing events is an important yet a challenging task. This is partially due to the fact that traditional short-read sequencing (SRS) technologies, despite their low error rate, are limited by their short read ...Read More
Alternative splicing (AS) and alternative back-splicing (ABS) are essential to understanding the development of cancer and may play a role as a target of personalized cancer therapeutics. However, the existing reference transcriptome annotation databases are far from being complete. Thus, detecting novel splicing events is an important yet a challenging task. This is partially due to the fact that traditional short-read sequencing (SRS) technologies, despite their low error rate, are limited by their short read lengths. On the other hand, the more recent long-read sequencing (LRS) technologies, while having the potential for capturing full-length transcripts, are marred with high error rates and significant structural artifacts. In this talk, two computational methods will be presented: CircMiner and Freddie. CircMiner accurately and efficiently detects back-splice sites and their abundances from SRS data using a novel splice-aware pseudo-alignment algorithm. Freddie is an annotation-free isoform discovery and detection tool that uses genome alignments of transcriptomic LRS as input with no reliance on transcriptome annotation databases by solving a combinatorial problem called MErCi (Minimum Error Clustering into Isoforms). Bio: Dr. Faraz Hach is an assistant professor in the Department of Urologic Sciences at the University of British Columbia and a senior research scientist at Vancouver Prostate Centre. He completed his PhD in computing science in Simon Fraser University and was a recipient of the Governor General's Gold Medal. His goal is to build bridges between computational algorithm design and biological problems pertaining to precision medicine with a special focus on cancer genomes. His research involves designing novel and high performance algorithms for analyzing large, high dimensional omics data produced by sequencing technologies. Recently, he is working on developing computational algorithms for the detection of aberrations using sequencing data obtained from tissue and liquid biopsies in order to understand clonal evolution in cancer genomes. Join Zoom meeting
Details
Organizer
CDSL
When
Mon, Aug 31, 2020 - 3:00 pm - 4:00 pm
Where
Online
Alternative splicing (AS) and alternative back-splicing (ABS) are essential to understanding the development of cancer and may play a role as a target of personalized cancer therapeutics. However, the existing reference transcriptome annotation databases are far from being complete. Thus, detecting novel splicing events is an important yet a challenging task. This is partially due to the fact that traditional short-read sequencing (SRS) technologies, despite their low error rate, are limited by their short read lengths. On the other hand, the more recent long-read sequencing (LRS) technologies, while having the potential for capturing full-length transcripts, are marred with high error rates and significant structural artifacts. In this talk, two computational methods will be presented: CircMiner and Freddie. CircMiner accurately and efficiently detects back-splice sites and their abundances from SRS data using a novel splice-aware pseudo-alignment algorithm. Freddie is an annotation-free isoform discovery and detection tool that uses genome alignments of transcriptomic LRS as input with no reliance on transcriptome annotation databases by solving a combinatorial problem called MErCi (Minimum Error Clustering into Isoforms). Bio: Dr. Faraz Hach is an assistant professor in the Department of Urologic Sciences at the University of British Columbia and a senior research scientist at Vancouver Prostate Centre. He completed his PhD in computing science in Simon Fraser University and was a recipient of the Governor General's Gold Medal. His goal is to build bridges between computational algorithm design and biological problems pertaining to precision medicine with a special focus on cancer genomes. His research involves designing novel and high performance algorithms for analyzing large, high dimensional omics data produced by sequencing technologies. Recently, he is working on developing computational algorithms for the detection of aberrations using sequencing data obtained from tissue and liquid biopsies in order to understand clonal evolution in cancer genomes. Join Zoom meeting 2020-08-31 15:00:00 Online Online CDSL 0 Uncovering alternative (back) splicing events using short and long read sequencing technologies
34
Description
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer Moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Joe Gray, OHSU Spatial Systems Biomedicine, creating a multi-scale tumor atlas and using systems analysis of extrinsic and intrinsic factors affecting cancer. Sabrina Culyba, an independent designer with experience spanning animatronics to theme ...Read More
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer Moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Joe Gray, OHSU Spatial Systems Biomedicine, creating a multi-scale tumor atlas and using systems analysis of extrinsic and intrinsic factors affecting cancer. Sabrina Culyba, an independent designer with experience spanning animatronics to theme part rides, virtual/augmented reality, and transformational games. Program Details
Details
When
Tue, Sep 01, 2020 - 2:30 pm - 4:00 pm
Where
Online
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer Moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Joe Gray, OHSU Spatial Systems Biomedicine, creating a multi-scale tumor atlas and using systems analysis of extrinsic and intrinsic factors affecting cancer. Sabrina Culyba, an independent designer with experience spanning animatronics to theme part rides, virtual/augmented reality, and transformational games. Program Details 2020-09-01 14:30:00 Online Online 0 Dataviz + Cancer Microlab 2
14
Description
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/variant-selection-genomics-dna-sequences-2
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/variant-selection-genomics-dna-sequences-2
Details
Organizer
NIH Training Library
When
Wed, Sep 02, 2020 - 10:00 am - 11:00 am
Where
Online
This class introduces mutation types and how to identify the mutations from DNA sequences. Additionally, it describes resources to access information about the impact of variants on proteins associated with drug response guidelines. This class will help participants determine how to find solutions in the selection of variants, especially in clinical research. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/variant-selection-genomics-dna-sequences-2 2020-09-02 10:00:00 Online Online NIH Training Library 0 Variant Selection in Genomic DNA Sequences
35
Description
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Nils Gehlenborg, Harvard professor integrating visual and computational approaches to support sense-making of biology and reproducible collaborative research across epigenomics and genomics. Kunle Odunsi, Physician scientist and co-leader of Roswell Park Cancer Center's ...Read More
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Nils Gehlenborg, Harvard professor integrating visual and computational approaches to support sense-making of biology and reproducible collaborative research across epigenomics and genomics. Kunle Odunsi, Physician scientist and co-leader of Roswell Park Cancer Center's Tumor Immunology and Immunotherapy Program, advancing diagnostics and treatments for gynecological cancer patients. Program Details
Details
When
Wed, Sep 02, 2020 - 1:00 pm - 2:30 pm
Where
Online
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Nils Gehlenborg, Harvard professor integrating visual and computational approaches to support sense-making of biology and reproducible collaborative research across epigenomics and genomics. Kunle Odunsi, Physician scientist and co-leader of Roswell Park Cancer Center's Tumor Immunology and Immunotherapy Program, advancing diagnostics and treatments for gynecological cancer patients. Program Details 2020-09-02 13:00:00 Online Online 0 Dataviz + Cancer Microlab 3
36
Description
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Lindsay Grace, Knight Chair of interactive media at the University of Miami School of Communications, exploring social impact through design. Karen Emmons, National Academy member and Director of Harvard's Community Engagement Program employing ...Read More
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Lindsay Grace, Knight Chair of interactive media at the University of Miami School of Communications, exploring social impact through design. Karen Emmons, National Academy member and Director of Harvard's Community Engagement Program employing interdisciplinary approaches to reducing cancer risk and health disparities. Program Details
Details
When
Fri, Sep 04, 2020 - 11:30 am - 1:00 pm
Where
Online
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Lindsay Grace, Knight Chair of interactive media at the University of Miami School of Communications, exploring social impact through design. Karen Emmons, National Academy member and Director of Harvard's Community Engagement Program employing interdisciplinary approaches to reducing cancer risk and health disparities. Program Details 2020-09-04 11:30:00 Online Online 0 Dataviz + Cancer Microlab 4
26
Description
Speakers: Dave Clements, Galaxy Community Manager, Johns Hopkins University, Steven Weaver, Senior Programmer Analyst, Temple University Galaxy is an open web-based platform for data integration and analysis in the life sciences. Galaxy makes sophisticated bioinformatics analysis accessible to bench researchers without requiring them to learn Linux system administration or command line interfaces. Every tool and tool setting is automatically recorded by Galaxy, making analyses reproducible by default. Analyses can also be shared with colleagues and ...Read More
Speakers: Dave Clements, Galaxy Community Manager, Johns Hopkins University, Steven Weaver, Senior Programmer Analyst, Temple University Galaxy is an open web-based platform for data integration and analysis in the life sciences. Galaxy makes sophisticated bioinformatics analysis accessible to bench researchers without requiring them to learn Linux system administration or command line interfaces. Every tool and tool setting is automatically recorded by Galaxy, making analyses reproducible by default. Analyses can also be shared with colleagues and with the public, enabling others to re-use and reproduce analyses pipelines. In the first part of this webinar, we will introduce Galaxy and its supporting ecosystem and community. This will include the many ways Galaxy is available to researchers, and a brief overview of the Galaxy user interface. In the second part, we will walk through an application of Galaxy to SARS CoV-2 research. We developed and published public reproducible Galaxy workflows for processing raw deep sequencing read data and calling intra-host genomic variants, as well as processing GISAID full-genome data in a comparative evolutionary framework (covid19.datamonkey.org). The goal of our analysis is to make use of all readily available sources of information to create a frequently updated list of sites in the SARS-CoV-2 genome that may be subject to positive or negative selection. High ranking sites on the list, especially those that are consistently detected over time or accumulate additional evidence in their favor with more data, could be taken as a set of candidates for functional impact or other downstream analyses. We search for evidence of selection at three different evolutionary levels: intra-host (next generation sequencing (NGS) data), between SARS-CoV-2 isolates (assembled genome data), and among beta-coronavirus isolates that are closely related to SARS-CoV-2 (assembled genome data). In this webinar, we will review the comparative analysis dashboard that can be used to which sites may have a functional impact or could be used for further downstream analysis, as well as how Galaxy can be used to implement the pipeline on researchers' datasets. Participants will learn how Galaxy is available, the basics of using Galaxy for data analysis, and how it can be applied in immunology in an example domain. Join ZoomGov Meeting Meeting ID: 161 756 1452 Passcode: 586729 One tap mobile +16692545252,,1617561452#,,,,,,0#,,586729# US (San Jose) +16468287666,,1617561452#,,,,,,0#,,586729# US (New York) Dial by your location +1 669 254 5252 US (San Jose) +1 646 828 7666 US (New York) Meeting ID: 161 756 1452 Passcode: 586729 Find your local number: https://nih.zoomgov.com/u/ayFfvRtd4 Join by SIP 1617561452@sip.zoomgov.com Join by H.323 161.199.138.10 (US West) 161.199.136.10 (US East) Meeting ID: 161 756 1452 Passcode: 586729
Details
When
Fri, Sep 04, 2020 - 12:00 pm - 1:00 pm
Where
Online
Speakers: Dave Clements, Galaxy Community Manager, Johns Hopkins University, Steven Weaver, Senior Programmer Analyst, Temple University Galaxy is an open web-based platform for data integration and analysis in the life sciences. Galaxy makes sophisticated bioinformatics analysis accessible to bench researchers without requiring them to learn Linux system administration or command line interfaces. Every tool and tool setting is automatically recorded by Galaxy, making analyses reproducible by default. Analyses can also be shared with colleagues and with the public, enabling others to re-use and reproduce analyses pipelines. In the first part of this webinar, we will introduce Galaxy and its supporting ecosystem and community. This will include the many ways Galaxy is available to researchers, and a brief overview of the Galaxy user interface. In the second part, we will walk through an application of Galaxy to SARS CoV-2 research. We developed and published public reproducible Galaxy workflows for processing raw deep sequencing read data and calling intra-host genomic variants, as well as processing GISAID full-genome data in a comparative evolutionary framework (covid19.datamonkey.org). The goal of our analysis is to make use of all readily available sources of information to create a frequently updated list of sites in the SARS-CoV-2 genome that may be subject to positive or negative selection. High ranking sites on the list, especially those that are consistently detected over time or accumulate additional evidence in their favor with more data, could be taken as a set of candidates for functional impact or other downstream analyses. We search for evidence of selection at three different evolutionary levels: intra-host (next generation sequencing (NGS) data), between SARS-CoV-2 isolates (assembled genome data), and among beta-coronavirus isolates that are closely related to SARS-CoV-2 (assembled genome data). In this webinar, we will review the comparative analysis dashboard that can be used to which sites may have a functional impact or could be used for further downstream analysis, as well as how Galaxy can be used to implement the pipeline on researchers' datasets. Participants will learn how Galaxy is available, the basics of using Galaxy for data analysis, and how it can be applied in immunology in an example domain. Join ZoomGov Meeting Meeting ID: 161 756 1452 Passcode: 586729 One tap mobile +16692545252,,1617561452#,,,,,,0#,,586729# US (San Jose) +16468287666,,1617561452#,,,,,,0#,,586729# US (New York) Dial by your location +1 669 254 5252 US (San Jose) +1 646 828 7666 US (New York) Meeting ID: 161 756 1452 Passcode: 586729 Find your local number: https://nih.zoomgov.com/u/ayFfvRtd4 Join by SIP 1617561452@sip.zoomgov.com Join by H.323 161.199.138.10 (US West) 161.199.136.10 (US East) Meeting ID: 161 756 1452 Passcode: 586729 2020-09-04 12:00:00 Online Online 0 Galaxy for Immunological and Infectious Disease Research
15
Description
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use ...Read More
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. Feel free to install the IGV browser in advance from here - https://software.broadinstitute.org/software/igv/download (the UCSV browser is web browser based) https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/genome-browser-2
Details
Organizer
NIH Training Library
When
Tue, Sep 08, 2020 - 1:00 pm - 2:30 pm
Where
Online
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. Feel free to install the IGV browser in advance from here - https://software.broadinstitute.org/software/igv/download (the UCSV browser is web browser based) https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/genome-browser-2 2020-09-08 13:00:00 Online Online NIH Training Library 0 Genome Browsers
37
Description
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Jeremy Kriegel, User Experience (UX) Director at Audible, Inc. and former UX lead at the Broad Institute, brining interaction design to open source platforms for big data analysis. Crystal Mackall, Physician scientist leading ...Read More
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Jeremy Kriegel, User Experience (UX) Director at Audible, Inc. and former UX lead at the Broad Institute, brining interaction design to open source platforms for big data analysis. Crystal Mackall, Physician scientist leading Stanford University's internationally-recognized translational immuno-oncology research program, focused especially on pediatric cancers. Program Details
Details
When
Wed, Sep 09, 2020 - 12:00 pm - 1:30 pm
Where
Online
Each 90-minute microlab starts with an inspiring conversation between thought leaders from the Cancer moonshot and creative visualization experts, followed by an opportunity to engage new colleagues from other fields in a fascinating discussion on the frontiers of cancer data visualization. Speakers: Jeremy Kriegel, User Experience (UX) Director at Audible, Inc. and former UX lead at the Broad Institute, brining interaction design to open source platforms for big data analysis. Crystal Mackall, Physician scientist leading Stanford University's internationally-recognized translational immuno-oncology research program, focused especially on pediatric cancers. Program Details 2020-09-09 12:00:00 Online Online 0 Dataviz + Cancer Microlab 5
40
Description
Speaker: Shai Shen-Orr, Ph.D., Associate Professor, Israel Institute of Technology Registration Recent technological advances allow us to probe the immune system at high resolution and explore its variation between individuals. Yet the question remains how we move from a ‘data dump’ to a mechanistic model that we can allow to intelligently reason on system-level ...Read More
Speaker: Shai Shen-Orr, Ph.D., Associate Professor, Israel Institute of Technology Registration Recent technological advances allow us to probe the immune system at high resolution and explore its variation between individuals. Yet the question remains how we move from a ‘data dump’ to a mechanistic model that we can allow to intelligently reason on system-level effects of perturbations. Here, I will describe the ‘data-insight gap’ namely, that is, why we repeatedly do not get the bang for the buck from the data we generate. I will describe our ongoing efforts to build a system level cell-centered view of ‘omic’ data over time, and its integration with knowledge in the primary immunology literature. Data and knowledge put together in this cell-centered framework establish a means to 'connect the dots' across immunology as well as systematic de novo hypotheses generation. Biography: - Associate Professor, Faculty of Medicine at the Technion, Israel Institute of Technology. - Since 2012, heads the Systems Immunology & Precision Medicine Lab, which develops novel analytics for studying the immune system. Tools are applied to study the drivers of immune variation and to further immune-based Precision Medicine. - BSc, Technion in Information Systems (1999); MSc, Bioinformatics at the Weizmann Institute of Science (2002); PhD, Harvard University in Biochemistry (2007); postdoctoral studies at Stanford University. - Research has been cited numerous times and featured in systems biology textbooks for students. - Research has laid the foundation of CytoReason, a company building a cell-centered ML model of the immune-system, which it applies to further drug development in collaboration with leading pharma companies. Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Prisca N. Fall, fallpn@mail.nih.gov, 301-402-4582, and/or the Federal Relay (1-800-877-8339).
Details
Organizer
NIA
When
Thu, Sep 10, 2020 - 12:00 pm - 1:00 pm
Where
Online
Speaker: Shai Shen-Orr, Ph.D., Associate Professor, Israel Institute of Technology Registration Recent technological advances allow us to probe the immune system at high resolution and explore its variation between individuals. Yet the question remains how we move from a ‘data dump’ to a mechanistic model that we can allow to intelligently reason on system-level effects of perturbations. Here, I will describe the ‘data-insight gap’ namely, that is, why we repeatedly do not get the bang for the buck from the data we generate. I will describe our ongoing efforts to build a system level cell-centered view of ‘omic’ data over time, and its integration with knowledge in the primary immunology literature. Data and knowledge put together in this cell-centered framework establish a means to 'connect the dots' across immunology as well as systematic de novo hypotheses generation. Biography: - Associate Professor, Faculty of Medicine at the Technion, Israel Institute of Technology. - Since 2012, heads the Systems Immunology & Precision Medicine Lab, which develops novel analytics for studying the immune system. Tools are applied to study the drivers of immune variation and to further immune-based Precision Medicine. - BSc, Technion in Information Systems (1999); MSc, Bioinformatics at the Weizmann Institute of Science (2002); PhD, Harvard University in Biochemistry (2007); postdoctoral studies at Stanford University. - Research has been cited numerous times and featured in systems biology textbooks for students. - Research has laid the foundation of CytoReason, a company building a cell-centered ML model of the immune-system, which it applies to further drug development in collaboration with leading pharma companies. Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Prisca N. Fall, fallpn@mail.nih.gov, 301-402-4582, and/or the Federal Relay (1-800-877-8339). 2020-09-10 12:00:00 Online Online NIA 0 Human Immune Monitoring Comes of Age
67
Description
Speaker: James Zou, Ph.D., Assistant Professor, Biomedical Data Science, Computer Science, and Electrical Engineering, Stanford University Register Dr. Zou will present new computer vision algorithms to capture complex morphologies and phenotypes that are important for human diseases and aging. He will illustrate this with examples from different physical scales: 1) video AI to assess cardiac function (Ouyang et ...Read More
Speaker: James Zou, Ph.D., Assistant Professor, Biomedical Data Science, Computer Science, and Electrical Engineering, Stanford University Register Dr. Zou will present new computer vision algorithms to capture complex morphologies and phenotypes that are important for human diseases and aging. He will illustrate this with examples from different physical scales: 1) video AI to assess cardiac function (Ouyang et al Nature 2020), 2) generating spatial transcriptomics and protein profiles from histology images (He et al Nature BME 2020), and 3) learning morphodynamics of immune cells. This talk will also give an overview of general design principles and tools developed to enable these technologies. Biography: - Chan-Zuckerberg Investigator and the Faculty Director of Stanford AI for Health. - Dr. Zou develops novel machine learning algorithms that have strong statistical guarantees and that are motivated by human health challenges. - Methods used are widely used by tech, biotech, and pharma companies. - Works on questions important for the broader impacts of AI, e.g., interpretations, robustness, fairness, and data governance. - Received several best paper awards at top CS venues, an NSF CAREER Award, a Google Faculty Award, and a Tencent AI award. Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Prisca N. Fall, fallpn@mail.nih.gov, 301-402-4582, and/or the Federal Relay (1-800-877-8339).
Details
Organizer
NIA
When
Fri, Sep 11, 2020 - 1:00 pm - 2:00 pm
Where
Online
Speaker: James Zou, Ph.D., Assistant Professor, Biomedical Data Science, Computer Science, and Electrical Engineering, Stanford University Register Dr. Zou will present new computer vision algorithms to capture complex morphologies and phenotypes that are important for human diseases and aging. He will illustrate this with examples from different physical scales: 1) video AI to assess cardiac function (Ouyang et al Nature 2020), 2) generating spatial transcriptomics and protein profiles from histology images (He et al Nature BME 2020), and 3) learning morphodynamics of immune cells. This talk will also give an overview of general design principles and tools developed to enable these technologies. Biography: - Chan-Zuckerberg Investigator and the Faculty Director of Stanford AI for Health. - Dr. Zou develops novel machine learning algorithms that have strong statistical guarantees and that are motivated by human health challenges. - Methods used are widely used by tech, biotech, and pharma companies. - Works on questions important for the broader impacts of AI, e.g., interpretations, robustness, fairness, and data governance. - Received several best paper awards at top CS venues, an NSF CAREER Award, a Google Faculty Award, and a Tencent AI award. Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Prisca N. Fall, fallpn@mail.nih.gov, 301-402-4582, and/or the Federal Relay (1-800-877-8339). 2020-09-11 13:00:00 Online Online NIA 0 Computer vision to deeply phenotype human diseases and aging across physiological, tissue and molecular scales
50
Description
By Avi Ma'ayan, Director of the Mount Sinai Center for Bioinformatics Daniel Clarke, Senior Data Scientist and Nicole Moiseyev, Summer Scholar Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY In this presentation we will discuss how we are transitioning from hosting the web-based bioinformatics applications we develop from a Mesos-Marathon cluster environment to a Rancher/Kubernetes environment. We are also moving away from hosting ...Read More
By Avi Ma'ayan, Director of the Mount Sinai Center for Bioinformatics Daniel Clarke, Senior Data Scientist and Nicole Moiseyev, Summer Scholar Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY In this presentation we will discuss how we are transitioning from hosting the web-based bioinformatics applications we develop from a Mesos-Marathon cluster environment to a Rancher/Kubernetes environment. We are also moving away from hosting full stack web-based bioinformatics applications such as Enrichr, Harmonizome, BioJupies and ARCHS4 towards developing appyters. Appyters turn Jupyter notebooks into fully functional standalone web applications. They extend the concepts developed for BioJupies to many other applications. Appyters present to users a data entry form that enables them to upload their data and set various parameters for a multitude of bioinformatics analysis pipelines. Once the form is filled, the Appyter executes the corresponding notebook online, saving the output without having to interact directly with the code. Appyters can be applied to a variety of workflows including building customized machine learning pipelines, analyzing RNA-seq data, and producing publishable figures. Appyters enable the rapid development of web-based applications as demonstrated by the breadth of over 50 examples. Join Webex meeting
Details
When
Fri, Sep 11, 2020 - 3:00 pm - 4:00 pm
Where
Online
By Avi Ma'ayan, Director of the Mount Sinai Center for Bioinformatics Daniel Clarke, Senior Data Scientist and Nicole Moiseyev, Summer Scholar Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY In this presentation we will discuss how we are transitioning from hosting the web-based bioinformatics applications we develop from a Mesos-Marathon cluster environment to a Rancher/Kubernetes environment. We are also moving away from hosting full stack web-based bioinformatics applications such as Enrichr, Harmonizome, BioJupies and ARCHS4 towards developing appyters. Appyters turn Jupyter notebooks into fully functional standalone web applications. They extend the concepts developed for BioJupies to many other applications. Appyters present to users a data entry form that enables them to upload their data and set various parameters for a multitude of bioinformatics analysis pipelines. Once the form is filled, the Appyter executes the corresponding notebook online, saving the output without having to interact directly with the code. Appyters can be applied to a variety of workflows including building customized machine learning pipelines, analyzing RNA-seq data, and producing publishable figures. Appyters enable the rapid development of web-based applications as demonstrated by the breadth of over 50 examples. Join Webex meeting 2020-09-11 15:00:00 Online Online 0 Fast Development and Robust Deployment of Data Driven Bioinformatics Web Apps and Workflows
74
Description
Back by popular demand! The HPC staff is restarting the monthly Walk-In Consults, virtually, of course. All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to ...Read More
Back by popular demand! The HPC staff is restarting the monthly Walk-In Consults, virtually, of course. All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1. We'll try to address simpler issues on the spot and follow up on more complex questions after the Zoom-In. Zoom URL: please email staff@hpc.nih.gov to get the ZOOM URL No appointments are necessary, and all problems are welcome. Please observe the following etiquette/protocol when joining: There will be a main room treated as a lobby and triage area. There, you can talk to one or a few staff members who will try to understand basic information about your issue. Then you may be invited to join to a staff member's personal breakout room for more detailed 1:1 consultation. Once you are finished with your focused consultation you can return to the main meeting room if you have additional questions or topics to discuss. Attendees are highly encouraged to - mute when not speaking - refrain from screen sharing unless asked to do so - screen share as you would in a public space with the understanding that other NIH HPC users and staff may join and view what you are sharing (i.e. look over your shoulder) - be prepared to wait your turn if staff are already helping other users
Details
Organizer
HPC Biowulf
When
Wed, Sep 16, 2020 - 1:00 pm - 3:00 pm
Where
Online
Back by popular demand! The HPC staff is restarting the monthly Walk-In Consults, virtually, of course. All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1. We'll try to address simpler issues on the spot and follow up on more complex questions after the Zoom-In. Zoom URL: please email staff@hpc.nih.gov to get the ZOOM URL No appointments are necessary, and all problems are welcome. Please observe the following etiquette/protocol when joining: There will be a main room treated as a lobby and triage area. There, you can talk to one or a few staff members who will try to understand basic information about your issue. Then you may be invited to join to a staff member's personal breakout room for more detailed 1:1 consultation. Once you are finished with your focused consultation you can return to the main meeting room if you have additional questions or topics to discuss. Attendees are highly encouraged to - mute when not speaking - refrain from screen sharing unless asked to do so - screen share as you would in a public space with the understanding that other NIH HPC users and staff may join and view what you are sharing (i.e. look over your shoulder) - be prepared to wait your turn if staff are already helping other users 2020-09-16 13:00:00 Online Online HPC Biowulf 0 Zoom-in Consult for Biowulf Users
16
Description
THIS EVENT HAS BEEN CANCELED This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing. This is an introductory two-part course for those who want to ...Read More
THIS EVENT HAS BEEN CANCELED This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing. This is an introductory two-part course for those who want to learn about research data management and sharing, or for those who are interested in a refresher. Part 1 of this training will cover understanding research data, how to manage research data, and how to work with data. During Part 2, participants will learn about sharing and archiving data. Audience: Researchers, fellows, post-docs, and trainees. This single registration signs you up for both classes, Part 1 and Part 2.
Details
Organizer
NIH Training Library
When
Thu, Sep 17, 2020 - 11:00 am - 12:00 pm
Where
Online
THIS EVENT HAS BEEN CANCELED This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing. This is an introductory two-part course for those who want to learn about research data management and sharing, or for those who are interested in a refresher. Part 1 of this training will cover understanding research data, how to manage research data, and how to work with data. During Part 2, participants will learn about sharing and archiving data. Audience: Researchers, fellows, post-docs, and trainees. This single registration signs you up for both classes, Part 1 and Part 2. 2020-09-17 11:00:00 Online Online NIH Training Library 0 CANCELED Data Management and Sharing (Two-Part Course)
930
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin after registration for this course ends on September 10th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on September 17th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Sep 17, 2020 - 2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin after registration for this course ends on September 10th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on September 17th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-09-17 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion
931
Description
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For ...Read More
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin after registration for this course ends on September 3rd. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on September 10th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Sep 17, 2020 - 3:30 pm - 4:30 pm
Where
Online Webinar
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin after registration for this course ends on September 3rd. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on September 10th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-09-17 15:30:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Single-cell RNA-Seq Analysis on NIDAP – Tutorial & Discussion
185
Description
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/3f71e6a0573f40b0929aaeb16f22e417 Presenter: Maxwell Lee Laboratory of Cancer Biology and Genetics, NCI/CCR
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/3f71e6a0573f40b0929aaeb16f22e417 Presenter: Maxwell Lee Laboratory of Cancer Biology and Genetics, NCI/CCR
Details
Organizer
NCI SS/SC
When
Mon, Sep 21, 2020 - 10:00 am - 11:00 am
Where
Online
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/3f71e6a0573f40b0929aaeb16f22e417 Presenter: Maxwell Lee Laboratory of Cancer Biology and Genetics, NCI/CCR 2020-09-21 10:00:00 Online NCI SS/SC 0 Introduction and k-means clustering
77
Description
Join Meeting The Surgery Branch (SB) of the National Cancer Institute (NCI) is a combined laboratory and clinical research unit devoted to the development of innovative cancer immunotherapies. Efforts run the gamut from basic studies of cancer immunology to the conduct of clinical immunotherapy trials for patients with metastatic cancer. Dr. Paul Robbins ...Read More
Join Meeting The Surgery Branch (SB) of the National Cancer Institute (NCI) is a combined laboratory and clinical research unit devoted to the development of innovative cancer immunotherapies. Efforts run the gamut from basic studies of cancer immunology to the conduct of clinical immunotherapy trials for patients with metastatic cancer. Dr. Paul Robbins will discuss SB’s recent advances in the development of cancer immunotherapies, with special emphasis on the use of whole exome sequencing and RNA-seq analysis to identify T cells and T cell receptors that recognize antigens arising from somatic mutations in patient tumors (neoantigens). SB has leveraged extensive capabilities available within the NIH Integrated Data Analysis Portal (NIDAP) to accelerate their work. Dr. Robbins will describe the integration of genomic sequencing data with patient clinical data from multiple sources and the development of customized, bench-to-bedside analytic workflows within the platform. NCI Investigators wishing to initiate data management, NIDAP-enabled analyses, or explore development of new workflows should contact Janelle Cortner by email or on Teams; Investigators from ICs beyond NCI who are interested in NIDAP should contact Sam Michael. Investigators may also contact or John Holgate. Thank you, Center for Biomedical Informatics and Information Technology (CBIIT) National Cancer Institute Individuals needing reasonable accommodations should contact nciittraining@mail.nih.gov. For those employees who are deaf, hard-of-hearing, or speech impaired, the Federal Relay Service provides free telecommunications relay services (TRS). Individuals requiring interpreting or CART services should submit a request online in the Interpreting Services System at least five business days before the start of the session.
Details
Organizer
CBIIT
When
Mon, Sep 21, 2020 - 1:00 pm - 2:00 pm
Where
Online
Join Meeting The Surgery Branch (SB) of the National Cancer Institute (NCI) is a combined laboratory and clinical research unit devoted to the development of innovative cancer immunotherapies. Efforts run the gamut from basic studies of cancer immunology to the conduct of clinical immunotherapy trials for patients with metastatic cancer. Dr. Paul Robbins will discuss SB’s recent advances in the development of cancer immunotherapies, with special emphasis on the use of whole exome sequencing and RNA-seq analysis to identify T cells and T cell receptors that recognize antigens arising from somatic mutations in patient tumors (neoantigens). SB has leveraged extensive capabilities available within the NIH Integrated Data Analysis Portal (NIDAP) to accelerate their work. Dr. Robbins will describe the integration of genomic sequencing data with patient clinical data from multiple sources and the development of customized, bench-to-bedside analytic workflows within the platform. NCI Investigators wishing to initiate data management, NIDAP-enabled analyses, or explore development of new workflows should contact Janelle Cortner by email or on Teams; Investigators from ICs beyond NCI who are interested in NIDAP should contact Sam Michael. Investigators may also contact or John Holgate. Thank you, Center for Biomedical Informatics and Information Technology (CBIIT) National Cancer Institute Individuals needing reasonable accommodations should contact nciittraining@mail.nih.gov. For those employees who are deaf, hard-of-hearing, or speech impaired, the Federal Relay Service provides free telecommunications relay services (TRS). Individuals requiring interpreting or CART services should submit a request online in the Interpreting Services System at least five business days before the start of the session. 2020-09-21 13:00:00 Online Online CBIIT 0 High Throughput Sequencing Analysis in Cancer Immunotherapy
80
Description
Join Zoom Meeting BIO: Jean Claude Zenklusen was born 1964 in Visp, Switzerland. He earned a Master in Sciences (Biochemistry) from the University of Buenos Aires in 1990. He received his PhD in Cancer Biology & Genetics from The University of Texas, Graduate School of Biomedical Sciences, in 1995. In 1996, he took a post-doctoral position at the National Genome Research Institute where, while participating in the Human Genome Project, he ...Read More
Join Zoom Meeting BIO: Jean Claude Zenklusen was born 1964 in Visp, Switzerland. He earned a Master in Sciences (Biochemistry) from the University of Buenos Aires in 1990. He received his PhD in Cancer Biology & Genetics from The University of Texas, Graduate School of Biomedical Sciences, in 1995. In 1996, he took a post-doctoral position at the National Genome Research Institute where, while participating in the Human Genome Project, he cloned two novel Tumor Suppressor Genes. From 2003 until 2009, he was the Senior Staff Scientist at the Neuro-Oncology Branch of the National Cancer Institute directing the Glioma Molecular Diagnostic Initiative and its companion data portal, Rembrandt. From 2009 until 2013, he served as the Scientific Program Director of the Office of Cancer Genomics, where he oversaw a variety of large-scale projects. In August 2013 he was named as Director of The Cancer Genome Atlas, the largest-scale cancer genomics project to date
Details
Organizer
CDSL
When
Mon, Sep 21, 2020 - 3:00 pm - 4:00 pm
Where
Online
Join Zoom Meeting BIO: Jean Claude Zenklusen was born 1964 in Visp, Switzerland. He earned a Master in Sciences (Biochemistry) from the University of Buenos Aires in 1990. He received his PhD in Cancer Biology & Genetics from The University of Texas, Graduate School of Biomedical Sciences, in 1995. In 1996, he took a post-doctoral position at the National Genome Research Institute where, while participating in the Human Genome Project, he cloned two novel Tumor Suppressor Genes. From 2003 until 2009, he was the Senior Staff Scientist at the Neuro-Oncology Branch of the National Cancer Institute directing the Glioma Molecular Diagnostic Initiative and its companion data portal, Rembrandt. From 2009 until 2013, he served as the Scientific Program Director of the Office of Cancer Genomics, where he oversaw a variety of large-scale projects. In August 2013 he was named as Director of The Cancer Genome Atlas, the largest-scale cancer genomics project to date 2020-09-21 15:00:00 Online Online CDSL 0 Beyond TCGA: Genomics programs at the Center for Cancer Genomics
76
Description
Registration CPTAC researchers from all over the country are excited to share their newest discoveries in cancer research, using the power of proteogenomics. Join us for a full day of scientific talks on tumor biology, with a session dedicated to live demonstrations of CPTAC-developed data analysis tools. The CPTAC program is coordinated through the Office of Cancer Clinical Proteomics Research (OCCPR). OCCPR and its programs ...Read More
Registration CPTAC researchers from all over the country are excited to share their newest discoveries in cancer research, using the power of proteogenomics. Join us for a full day of scientific talks on tumor biology, with a session dedicated to live demonstrations of CPTAC-developed data analysis tools. The CPTAC program is coordinated through the Office of Cancer Clinical Proteomics Research (OCCPR). OCCPR and its programs aim to improve prevention, early detection, diagnosis, and treatment of cancer by bringing more understanding to the biology of cancer through proteogenome science and technology development. The programs generate a slew of freely available data, reagents and software for the scientific community.
Details
When
Tue, Sep 22, 2020 - 10:00 am - 6:10 pm
Where
Online
Registration CPTAC researchers from all over the country are excited to share their newest discoveries in cancer research, using the power of proteogenomics. Join us for a full day of scientific talks on tumor biology, with a session dedicated to live demonstrations of CPTAC-developed data analysis tools. The CPTAC program is coordinated through the Office of Cancer Clinical Proteomics Research (OCCPR). OCCPR and its programs aim to improve prevention, early detection, diagnosis, and treatment of cancer by bringing more understanding to the biology of cancer through proteogenome science and technology development. The programs generate a slew of freely available data, reagents and software for the scientific community. 2020-09-22 10:00:00 Online 0 CPTAC Virtual Scientific Symposium
65
Description
[one_half] This two-part workshop will demonstrate how deep learning can be used to classify different types of cancer. Part I will focus on data preparation, starting with genomic data. Part II will demonstrate how to create a deep learning model, with hands-on instruction on how to use the processed data to build a convolutional neural network (CNN) model that can classify different cancer types. You can see preliminary workshop materials—Jupyter notebooks and documentation—...Read More
[one_half] This two-part workshop will demonstrate how deep learning can be used to classify different types of cancer. Part I will focus on data preparation, starting with genomic data. Part II will demonstrate how to create a deep learning model, with hands-on instruction on how to use the processed data to build a convolutional neural network (CNN) model that can classify different cancer types. You can see preliminary workshop materials—Jupyter notebooks and documentation—on Github, at https://github.com/ravichas/ML-TC1 Presenter: Sarangan Ravichandran, PhD, PMP, Data Scientist, Frederick National Laboratory for Cancer Research (FNL) and Adjunct Professor in Bioinformatics, Hood College Webex
Details
Organizer
NCI Data Science Learning Exchange
When
Tue, Sep 22, 2020 - 1:00 pm - 3:00 pm
Where
Online
[one_half] This two-part workshop will demonstrate how deep learning can be used to classify different types of cancer. Part I will focus on data preparation, starting with genomic data. Part II will demonstrate how to create a deep learning model, with hands-on instruction on how to use the processed data to build a convolutional neural network (CNN) model that can classify different cancer types. You can see preliminary workshop materials—Jupyter notebooks and documentation—on Github, at https://github.com/ravichas/ML-TC1 Presenter: Sarangan Ravichandran, PhD, PMP, Data Scientist, Frederick National Laboratory for Cancer Research (FNL) and Adjunct Professor in Bioinformatics, Hood College Webex 2020-09-22 13:00:00 Online Online NCI Data Science Learning Exchange 0 Cancer Type/Site Classification Using Deep Learning
17
Description
For the Introductory Training we will cover basic topics such as: MetaCore overview; how to use MetaCore as a knowledge mining tool; how to upload data; running functional enrichments and exploring pathway maps; and running workflows. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/metacore-introductory-training-0
For the Introductory Training we will cover basic topics such as: MetaCore overview; how to use MetaCore as a knowledge mining tool; how to upload data; running functional enrichments and exploring pathway maps; and running workflows. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/metacore-introductory-training-0
Details
Organizer
NIH Training Library
When
Wed, Sep 23, 2020 - 1:30 pm - 4:00 pm
Where
Online
For the Introductory Training we will cover basic topics such as: MetaCore overview; how to use MetaCore as a knowledge mining tool; how to upload data; running functional enrichments and exploring pathway maps; and running workflows. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/metacore-introductory-training-0 2020-09-23 13:30:00 Online Online NIH Training Library 0 MetaCore Introductory Training
18
Description
For the Advanced Training, topics covered depend upon survey results: find key hubs using over-connectivity analysis; using Microarray repository for gene comparisons against public data; building networks with MetaCore; constructing your own pathway maps; performing toxicogenomic analysis in MetaCore; analyzing and building networks with miRNA and mRNA data; analyzing multi-omics data (RNA-seq, proteomics, metabolomics, etc). https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/metacore-advanced-session-1
For the Advanced Training, topics covered depend upon survey results: find key hubs using over-connectivity analysis; using Microarray repository for gene comparisons against public data; building networks with MetaCore; constructing your own pathway maps; performing toxicogenomic analysis in MetaCore; analyzing and building networks with miRNA and mRNA data; analyzing multi-omics data (RNA-seq, proteomics, metabolomics, etc). https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/metacore-advanced-session-1
Details
Organizer
NIH Training Library
When
Thu, Sep 24, 2020 - 9:30 am - 12:00 pm
Where
Online
For the Advanced Training, topics covered depend upon survey results: find key hubs using over-connectivity analysis; using Microarray repository for gene comparisons against public data; building networks with MetaCore; constructing your own pathway maps; performing toxicogenomic analysis in MetaCore; analyzing and building networks with miRNA and mRNA data; analyzing multi-omics data (RNA-seq, proteomics, metabolomics, etc). https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/metacore-advanced-session-1 2020-09-24 09:30:00 Online Online NIH Training Library 0 MetaCore Advanced Session
27
Description
Over the last 15 years, since its release in September 2005, the Gene Set Enrichment Analysis (GSEA) software and its companion gene set collections, the Molecular Signatures Database (MSigDB) have served as key resources for interpreting the biological significance of expression changes in large transcriptomic datasets. In the past year we've released several major updates to the GSEA-MSigDB suite, bringing with them many new features, including new gene sets for investigating the cutting edge of biology, and ...Read More
Over the last 15 years, since its release in September 2005, the Gene Set Enrichment Analysis (GSEA) software and its companion gene set collections, the Molecular Signatures Database (MSigDB) have served as key resources for interpreting the biological significance of expression changes in large transcriptomic datasets. In the past year we've released several major updates to the GSEA-MSigDB suite, bringing with them many new features, including new gene sets for investigating the cutting edge of biology, and an increased focus on support for model organisms in GSEA. This webinar will cover the basics of the GSEA method, the resources available in the Molecular Signatures Database, and a preview of some features we plan to release in the coming year. Presenter: Anthony S. Castanza, PhD Curator, Molecular Signatures Database Mesirov Lab, Department of Medicine University of California, San Diego Registration
Details
Organizer
CBIIT
When
Thu, Sep 24, 2020 - 1:00 pm - 2:00 pm
Where
Online
Over the last 15 years, since its release in September 2005, the Gene Set Enrichment Analysis (GSEA) software and its companion gene set collections, the Molecular Signatures Database (MSigDB) have served as key resources for interpreting the biological significance of expression changes in large transcriptomic datasets. In the past year we've released several major updates to the GSEA-MSigDB suite, bringing with them many new features, including new gene sets for investigating the cutting edge of biology, and an increased focus on support for model organisms in GSEA. This webinar will cover the basics of the GSEA method, the resources available in the Molecular Signatures Database, and a preview of some features we plan to release in the coming year. Presenter: Anthony S. Castanza, PhD Curator, Molecular Signatures Database Mesirov Lab, Department of Medicine University of California, San Diego Registration 2020-09-24 13:00:00 Online Online CBIIT 0 Introduction to Gene Set Enrichment Analysis - Molecular Signatures Database (GSEA-MSigDB) suite
932
Description
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For ...Read More
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin after registration for this course ends on September 17th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on September 24th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Thu, Sep 24, 2020 - 2:00 pm - 3:00 pm
Where
Online Webinar
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin after registration for this course ends on September 17th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on September 24th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-09-24 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Single-cell RNA-Seq Analysis on NIDAP – Tutorial & Discussion
19
Description
NCI Cancer Research Data Commons (CRDC) provides access to large-scale datasets such as The Cancer Genome Atlas (TCGA) that span multiple data types, including genomics, proteomics, imaging, and clinical data. The CRDC offers bioinformatics tools directly in the cloud, thus eliminating the need to download and store large-scale datasets. The cloud also offers greater computational capacity to manage big data analysis, further accelerating research and promoting new discoveries. The training session will provide an overview ...Read More
NCI Cancer Research Data Commons (CRDC) provides access to large-scale datasets such as The Cancer Genome Atlas (TCGA) that span multiple data types, including genomics, proteomics, imaging, and clinical data. The CRDC offers bioinformatics tools directly in the cloud, thus eliminating the need to download and store large-scale datasets. The cloud also offers greater computational capacity to manage big data analysis, further accelerating research and promoting new discoveries. The training session will provide an overview of NCI CRDC and highlight several research studies to show the utility of the CRDC and its resources. In addition, there will be hands-on training on one of the following platforms: the NCI Cloud Resource platform Seven Bridges’ Cancer Genomics Cloud (SB-CGC), Broad Institute’s FireCloud, or the Institute for Systems Biology’s Cancer Genomics Cloud (ISB-CGC) for prospective users. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/nci-cancer-research-data-commons-cloud-computing-0
Details
Organizer
NIH Training Library
When
Fri, Sep 25, 2020 - 11:00 am - 2:00 pm
Where
Online
NCI Cancer Research Data Commons (CRDC) provides access to large-scale datasets such as The Cancer Genome Atlas (TCGA) that span multiple data types, including genomics, proteomics, imaging, and clinical data. The CRDC offers bioinformatics tools directly in the cloud, thus eliminating the need to download and store large-scale datasets. The cloud also offers greater computational capacity to manage big data analysis, further accelerating research and promoting new discoveries. The training session will provide an overview of NCI CRDC and highlight several research studies to show the utility of the CRDC and its resources. In addition, there will be hands-on training on one of the following platforms: the NCI Cloud Resource platform Seven Bridges’ Cancer Genomics Cloud (SB-CGC), Broad Institute’s FireCloud, or the Institute for Systems Biology’s Cancer Genomics Cloud (ISB-CGC) for prospective users. https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/nci-cancer-research-data-commons-cloud-computing-0 2020-09-25 11:00:00 Online Online NIH Training Library 0 NCI Cancer Research Data Commons: Cloud Computing
933
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on September 25th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 1st. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Fri, Sep 25 - Thu, Oct 01, 2020 -2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on September 25th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 1st. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-09-25 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion
75
Description
Register/Join This webinar is intended for cancer researchers and bioinformaticians who are ...Read More
Register/Join This webinar is intended for cancer researchers and bioinformaticians who are interested in learning more about the NCI Genomic Data Commons’ (GDC’s) bioinformatics pipelines for data harmonization. The GDC DNA-Seq analysis pipeline identifies somatic variants within whole exome sequencing (WXS) and whole genome sequencing (WGS) data. Somatic variants are identified by comparing allele frequencies in normal and tumor sample alignments, annotating each mutation, and aggregating mutations from multiple cases. In this webinar, The University of Chicago’s Drs. Bill Wysocki and Zhenyu Zhang will: Provide an overview of the GDC DNA-Seq alignment workflows Review the GDC WXS somatic variant calling workflow Review the GDC WGS somatic variant calling, copy number variation, and structural variation workflows Demonstrate how to download DNA-Seq data generated from GDC workflows
Details
Organizer
CBIIT
When
Mon, Sep 28, 2020 - 2:00 pm - 3:00 pm
Where
Online
Register/Join This webinar is intended for cancer researchers and bioinformaticians who are interested in learning more about the NCI Genomic Data Commons’ (GDC’s) bioinformatics pipelines for data harmonization. The GDC DNA-Seq analysis pipeline identifies somatic variants within whole exome sequencing (WXS) and whole genome sequencing (WGS) data. Somatic variants are identified by comparing allele frequencies in normal and tumor sample alignments, annotating each mutation, and aggregating mutations from multiple cases. In this webinar, The University of Chicago’s Drs. Bill Wysocki and Zhenyu Zhang will: Provide an overview of the GDC DNA-Seq alignment workflows Review the GDC WXS somatic variant calling workflow Review the GDC WGS somatic variant calling, copy number variation, and structural variation workflows Demonstrate how to download DNA-Seq data generated from GDC workflows 2020-09-28 14:00:00 Online Online CBIIT 0 NCI Genomic Data Commons DNA-Seq Data Processing
85
Description
Register The purpose of this class is to introduce some of the fundamentals of meta-analysis. The focus will be on randomized clinical trials; however, the presenter will also briefly discuss the application of meta-analyses in laboratory and observational (epidemiological) studies. Most of the content for this talk was gleaned from the new Cochrane Handbook for Systematic Reviews of Interventions (2/e), an authoritative reference on the ...Read More
Register The purpose of this class is to introduce some of the fundamentals of meta-analysis. The focus will be on randomized clinical trials; however, the presenter will also briefly discuss the application of meta-analyses in laboratory and observational (epidemiological) studies. Most of the content for this talk was gleaned from the new Cochrane Handbook for Systematic Reviews of Interventions (2/e), an authoritative reference on the broader topic of systematic reviews. The audience should have an acquaintance with basic statistical concepts (including, but not limited to: dichotomous and continuous outcomes, odds ratios, standard deviation and error, weighted average, fixed and random effects and associated models, etc.). However, it will only be necessary to understand the principles and interpretation of these ideas, not the underlying mathematics and calculations to learn the principles presented. This class will not contain a “hands-on” portion or a set of exercises to be completed in a statistical software package during the training or afterwards. Instructor(s) -- NIH Staff: Paul Juneau
Details
Organizer
NIH Training Library
When
Tue, Sep 29, 2020 - 9:30 am - 10:30 am
Where
Online
Register The purpose of this class is to introduce some of the fundamentals of meta-analysis. The focus will be on randomized clinical trials; however, the presenter will also briefly discuss the application of meta-analyses in laboratory and observational (epidemiological) studies. Most of the content for this talk was gleaned from the new Cochrane Handbook for Systematic Reviews of Interventions (2/e), an authoritative reference on the broader topic of systematic reviews. The audience should have an acquaintance with basic statistical concepts (including, but not limited to: dichotomous and continuous outcomes, odds ratios, standard deviation and error, weighted average, fixed and random effects and associated models, etc.). However, it will only be necessary to understand the principles and interpretation of these ideas, not the underlying mathematics and calculations to learn the principles presented. This class will not contain a “hands-on” portion or a set of exercises to be completed in a statistical software package during the training or afterwards. Instructor(s) -- NIH Staff: Paul Juneau 2020-09-29 09:30:00 Online Online NIH Training Library 0 Meta-Analysis: Quantifying a Systematic Review
93
Description
Register Through large collaborative research projects, the field of neuroscience is gaining access to unprecedented amounts of information about the structure and function of the brain. These datasets are already having a profound impact on neuroscience, and they promise to help us understand the biological underpinnings of human brain health. At the same time, the deluge of data is generating new challenges, as researchers struggle ...Read More
Register Through large collaborative research projects, the field of neuroscience is gaining access to unprecedented amounts of information about the structure and function of the brain. These datasets are already having a profound impact on neuroscience, and they promise to help us understand the biological underpinnings of human brain health. At the same time, the deluge of data is generating new challenges, as researchers struggle to store, manage, analyze, and interpret the data. You can use scalable cloud computing methods to lower some of these barriers. But the adoption of these methods is in its infancy. Join this presentation to learn about tools to smoothly transition neuroscience into cloud computing. In particular, Cloudknot is a software library that we developed that packages existing Python code for deployment at scale in AWS Batch. In this session, see the use of this software in our research that uses large-scale magnetic resonance imaging (MRI) datasets to understand the role of brain connections in cognition and behavior. Speaker: Ariel Rokem, PhD, Research Assistant Professor, University of Washington
Details
When
Wed, Sep 30, 2020 - 12:00 pm - 1:00 pm
Where
Online
Register Through large collaborative research projects, the field of neuroscience is gaining access to unprecedented amounts of information about the structure and function of the brain. These datasets are already having a profound impact on neuroscience, and they promise to help us understand the biological underpinnings of human brain health. At the same time, the deluge of data is generating new challenges, as researchers struggle to store, manage, analyze, and interpret the data. You can use scalable cloud computing methods to lower some of these barriers. But the adoption of these methods is in its infancy. Join this presentation to learn about tools to smoothly transition neuroscience into cloud computing. In particular, Cloudknot is a software library that we developed that packages existing Python code for deployment at scale in AWS Batch. In this session, see the use of this software in our research that uses large-scale magnetic resonance imaging (MRI) datasets to understand the role of brain connections in cognition and behavior. Speaker: Ariel Rokem, PhD, Research Assistant Professor, University of Washington 2020-09-30 12:00:00 Online Online 0 Cloud Computing for the Era of Brain Observatories
28
Description
This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of RStudio, including how to import data, create scripts, and run code. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services and Bioinformatics programs that is designed to teach non-programmers ...Read More
This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of RStudio, including how to import data, create scripts, and run code. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services and Bioinformatics programs that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. By the end of this class, students should be able to: list reasons for using R; describe the purpose of the RStudio Script, Console, Environment, and Plots panes; describe the various methods for finding help on R and RStudio; create a script in R and run code from a script and from the console; define the following terms as they relate to R: object, assign, call, function, and arguments; assign values to objects in R; learn about R objects; describe the major R data types; understand how to create and work with R vectors; describe the steps for importing data into R. Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the data before the class. Please bring your laptop with R and RStudio installed. Registration
Details
Organizer
NIH Training Library
When
Thu, Oct 01, 2020 - 1:00 pm - 2:30 pm
Where
Online
This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of RStudio, including how to import data, create scripts, and run code. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services and Bioinformatics programs that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. By the end of this class, students should be able to: list reasons for using R; describe the purpose of the RStudio Script, Console, Environment, and Plots panes; describe the various methods for finding help on R and RStudio; create a script in R and run code from a script and from the console; define the following terms as they relate to R: object, assign, call, function, and arguments; assign values to objects in R; learn about R objects; describe the major R data types; understand how to create and work with R vectors; describe the steps for importing data into R. Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the data before the class. Please bring your laptop with R and RStudio installed. Registration 2020-10-01 13:00:00 Online Online NIH Training Library 0 Introduction to R and RStudio
934
Description
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For ...Read More
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on October 2nd. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 8th. IT IS YOUR RESPONSIBILITY to complete ~3.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Fri, Oct 02 - Thu, Oct 08, 2020 -2:00 pm - 3:00 pm
Where
Online Webinar
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on October 2nd. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 8th. IT IS YOUR RESPONSIBILITY to complete ~3.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-10-02 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Single-cell RNA-Seq Analysis on NIDAP – Tutorial & Discussion
135
https://cbiit.webex.com/recordingservice/sites/cbiit/recording/play/52ad020326ee4c858f0a09b35247478a 2020-10-05 10:00:00 Online NCI SS/SC 0 Gaussian mixture model (GMM) and Latent Dirichlet Allocation (LDA)
29
Description
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training ...Read More
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. By the end of this class, participants should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; and describe how to save data in R. Participants are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class. Registration
Details
Organizer
NIH Training Library
When
Tue, Oct 06, 2020 - 1:00 pm - 2:30 pm
Where
Online
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons. By the end of this class, participants should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; and describe how to save data in R. Participants are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class. Registration 2020-10-06 13:00:00 Online Online NIH Training Library 0 Introduction to R Data Types
30
Description
Participants will learn how to develop artificial intelligence (AI) applications focused on biomedical imaging datasets. The course will cover the entire AI pipeline from image exploration and labeling to development and deployment of predictive models on images using both machine learning and deep learning approaches. This includes annotating and exploring image datasets, exploring techniques for developing machine and deep learning models, optimizing the parameters of the deep networks, and automatically generating code for edge deployment ...Read More
Participants will learn how to develop artificial intelligence (AI) applications focused on biomedical imaging datasets. The course will cover the entire AI pipeline from image exploration and labeling to development and deployment of predictive models on images using both machine learning and deep learning approaches. This includes annotating and exploring image datasets, exploring techniques for developing machine and deep learning models, optimizing the parameters of the deep networks, and automatically generating code for edge deployment of AI models. This is an introductory level class. No installation of MATLAB is necessary. Registration
Details
Organizer
NIH Training Library
When
Tue, Oct 06, 2020 - 1:00 pm - 4:15 pm
Where
Online
Participants will learn how to develop artificial intelligence (AI) applications focused on biomedical imaging datasets. The course will cover the entire AI pipeline from image exploration and labeling to development and deployment of predictive models on images using both machine learning and deep learning approaches. This includes annotating and exploring image datasets, exploring techniques for developing machine and deep learning models, optimizing the parameters of the deep networks, and automatically generating code for edge deployment of AI models. This is an introductory level class. No installation of MATLAB is necessary. Registration 2020-10-06 13:00:00 Online Online NIH Training Library 0 Data Science and Artificial Intelligence: Medical Imaging Datasets Using MATLAB
31
Description
QIAGEN’s Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based on published biological evidence and your knowledge of disease biology. IVA allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This workshop will focus on how to use IVA to upload ...Read More
QIAGEN’s Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based on published biological evidence and your knowledge of disease biology. IVA allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This workshop will focus on how to use IVA to upload datasets, efficiently use different filters within variant analysis to identify causal variants, and export data. The class will also review recent IVA updates. The Human Gene Mutation Database (HGMD) database provides quick access to known published gene lesions responsible for human inherited disease. This session will explore how to query genes and mutations in HGMD and how to view the curated content. Registration
Details
Organizer
NIH Training Library
When
Wed, Oct 07, 2020 - 10:00 am - 3:00 pm
Where
Online
QIAGEN’s Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based on published biological evidence and your knowledge of disease biology. IVA allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This workshop will focus on how to use IVA to upload datasets, efficiently use different filters within variant analysis to identify causal variants, and export data. The class will also review recent IVA updates. The Human Gene Mutation Database (HGMD) database provides quick access to known published gene lesions responsible for human inherited disease. This session will explore how to query genes and mutations in HGMD and how to view the curated content. Registration 2020-10-07 10:00:00 Online Online NIH Training Library 0 Variant Filtering and Interpretation Using Ingenuity Variant Analysis (IVA) and Human Gene Mutation Database (HGMD)
90
Description
Register MacVector is a powerful but easy to use Macintosh application that lets you document, analyze and manipulate DNA and Protein sequences. A simple graphical interface lets you generate beautiful plasmid maps, design primers, assemble ABI and NGS sequences, perform local and internet database searches based on sequence similarity or keyword, align sequences using a ...Read More
Register MacVector is a powerful but easy to use Macintosh application that lets you document, analyze and manipulate DNA and Protein sequences. A simple graphical interface lets you generate beautiful plasmid maps, design primers, assemble ABI and NGS sequences, perform local and internet database searches based on sequence similarity or keyword, align sequences using a wide variety of options, replicate cloning experiments with restriction enzymes, Gateway or Gibson assembly approaches and much, much more. This presentation will introduce new users to the capabilities of MacVector while allowing existing users the opportunity to learn about the new functionality in MacVector 17.5 and our upcoming 18.0 release. In addition, we will walk though a number of common workflows where you are sure to learn a variety of tips and tricks to speed up and simplify working with sequence data. Presenter: Kevin Kendall Field Application Scientist at MacVector
Details
Organizer
CBIIT
When
Thu, Oct 08, 2020 - 12:00 pm - 1:00 pm
Where
Online
Register MacVector is a powerful but easy to use Macintosh application that lets you document, analyze and manipulate DNA and Protein sequences. A simple graphical interface lets you generate beautiful plasmid maps, design primers, assemble ABI and NGS sequences, perform local and internet database searches based on sequence similarity or keyword, align sequences using a wide variety of options, replicate cloning experiments with restriction enzymes, Gateway or Gibson assembly approaches and much, much more. This presentation will introduce new users to the capabilities of MacVector while allowing existing users the opportunity to learn about the new functionality in MacVector 17.5 and our upcoming 18.0 release. In addition, we will walk though a number of common workflows where you are sure to learn a variety of tips and tricks to speed up and simplify working with sequence data. Presenter: Kevin Kendall Field Application Scientist at MacVector 2020-10-08 12:00:00 Online Online CBIIT 0 Introduction to Sequence Analysis using MacVector
936
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin this course ends on October 9th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 15th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Fri, Oct 09 - Thu, Oct 15, 2020 -2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin this course ends on October 9th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 15th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-10-09 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion
176
Description
Send email to staff@hpc.nih.gov to get the Zoom URL All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss ...Read More
Send email to staff@hpc.nih.gov to get the Zoom URL All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1. We'll try to address simpler issues on the spot and follow up on more complex questions after the Zoom-In.
Details
When
Wed, Oct 14, 2020 - 1:00 pm - 3:00 pm
Where
Online
Send email to staff@hpc.nih.gov to get the Zoom URL All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1. We'll try to address simpler issues on the spot and follow up on more complex questions after the Zoom-In. 2020-10-14 13:00:00 Online 0 Zoom-In Consult with Biowulf Staff
89
Description
Register Helen Shearman, PhD, Senior Field Application Scientist, will be presenting a one-hour overview demonstration on SnapGene. SnapGene offers a fast and easy way to plan, visualize, and document your everyday cloning procedures. SnapGene makes cloning easier, improves communication, and provides a record of DNA constructs. More information can be found on ...Read More
Register Helen Shearman, PhD, Senior Field Application Scientist, will be presenting a one-hour overview demonstration on SnapGene. SnapGene offers a fast and easy way to plan, visualize, and document your everyday cloning procedures. SnapGene makes cloning easier, improves communication, and provides a record of DNA constructs. More information can be found on our website at snapgene.com Presenter: Dr. Helen Shearman Field Application Scientist at SnapGene
Details
Organizer
CBIIT
When
Thu, Oct 15, 2020 - 3:00 pm - 4:00 pm
Where
Online
Register Helen Shearman, PhD, Senior Field Application Scientist, will be presenting a one-hour overview demonstration on SnapGene. SnapGene offers a fast and easy way to plan, visualize, and document your everyday cloning procedures. SnapGene makes cloning easier, improves communication, and provides a record of DNA constructs. More information can be found on our website at snapgene.com Presenter: Dr. Helen Shearman Field Application Scientist at SnapGene 2020-10-15 15:00:00 Online Online CBIIT 0 Introduction to SnapGene
935
Description
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For ...Read More
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on October 16th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 22nd. IT IS YOUR RESPONSIBILITY to complete ~3.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Fri, Oct 16 - Thu, Oct 22, 2020 -2:00 pm - 3:00 pm
Where
Online Webinar
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on October 16th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 22nd. IT IS YOUR RESPONSIBILITY to complete ~3.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-10-16 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Single-cell RNA-Seq Analysis on NIDAP – Tutorial & Discussion
136
Description
Details
Organizer
CDSL
When
Mon, Oct 19, 2020 - 10:00 am - 11:00 am
Where
Online
Register 2020-10-19 10:00:00 Online CDSL 0 Non-negative matrix factorization (NMF) and its connection to k-means clustering
179
Description
Register This webinar is intended for cancer researchers who are interested in learning more about the data that is available in the NCI Genomic Data Commons (GDC).The GDC is a unified repository that enables data sharing across cancer genomic studies in support of precision medicine. The GDC provides access to clinical and genomic data from 65+ projects through supported cancer research programs and organizations that ...Read More
Register This webinar is intended for cancer researchers who are interested in learning more about the data that is available in the NCI Genomic Data Commons (GDC).The GDC is a unified repository that enables data sharing across cancer genomic studies in support of precision medicine. The GDC provides access to clinical and genomic data from 65+ projects through supported cancer research programs and organizations that contribute data to the GDC. This webinar will provide an overview of the programs and projects that provide data to the GDC and review the data types from these projects that are made available through GDC data access tools. Speakers: Ms. Sharon Gaheen, GDC Technical Project Manager, Leidos Biomedical Research Inc. Dr. Biju Issac, Scientific Lead, Leidos Biomedical Research Inc.
Details
When
Mon, Oct 19, 2020 - 2:00 pm - 3:00 pm
Where
Online
Register This webinar is intended for cancer researchers who are interested in learning more about the data that is available in the NCI Genomic Data Commons (GDC).The GDC is a unified repository that enables data sharing across cancer genomic studies in support of precision medicine. The GDC provides access to clinical and genomic data from 65+ projects through supported cancer research programs and organizations that contribute data to the GDC. This webinar will provide an overview of the programs and projects that provide data to the GDC and review the data types from these projects that are made available through GDC data access tools. Speakers: Ms. Sharon Gaheen, GDC Technical Project Manager, Leidos Biomedical Research Inc. Dr. Biju Issac, Scientific Lead, Leidos Biomedical Research Inc. 2020-10-19 14:00:00 Online 0 GDC About the Data
181
Description
Register R markdown offers tools to generate and update reports automatically—including figures, tables, ...Read More
Register R markdown offers tools to generate and update reports automatically—including figures, tables, mathematical equations and code. Instead of copying tables and figures into another document, you can update reports quickly and easily at the click of a button whenever you have new data to share. Join this workshop to see a demonstration and learn how to use it! Presenter: Randall Johnson, PhD, Bioinformatics Analyst/Technical Project Manager at Frederick National Laboratory for Cancer Research (FNL) and organizer of the Bioinformatics Users Forum
Details
Organizer
NCI Data Science Learning Exchange
When
Tue, Oct 20, 2020 - 11:00 am - 12:00 pm
Where
Online
Register R markdown offers tools to generate and update reports automatically—including figures, tables, mathematical equations and code. Instead of copying tables and figures into another document, you can update reports quickly and easily at the click of a button whenever you have new data to share. Join this workshop to see a demonstration and learn how to use it! Presenter: Randall Johnson, PhD, Bioinformatics Analyst/Technical Project Manager at Frederick National Laboratory for Cancer Research (FNL) and organizer of the Bioinformatics Users Forum 2020-10-20 11:00:00 Online NCI Data Science Learning Exchange 0 Introduction to R markdown for Automated Reports
32
Description
Participants will learn how to develop artificial intelligence (AI) applications using MATLAB, even if they do not have a formal background in machine and deep learning. The goal of this course is to introduce tools and fundamental approaches for developing predictive models on biomedical signals. The course will cover the entire AI pipeline, from signal exploration to deployment, including: annotating time series biomedical signals automatically, creating deep learning models using Convolutional Neural Networks (CNNs) and ...Read More
Participants will learn how to develop artificial intelligence (AI) applications using MATLAB, even if they do not have a formal background in machine and deep learning. The goal of this course is to introduce tools and fundamental approaches for developing predictive models on biomedical signals. The course will cover the entire AI pipeline, from signal exploration to deployment, including: annotating time series biomedical signals automatically, creating deep learning models using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) for biomedical signal data, creating machine learning models for biomedical signal data, applying advanced signal pre-processing techniques for automated feature extraction, and automatically generating code for edge deployment of AI models. This is an introductory level class. No installation of MATLAB is necessary. Registration
Details
Organizer
NIH Training Library
When
Tue, Oct 20, 2020 - 1:00 pm - 4:15 pm
Where
Online
Participants will learn how to develop artificial intelligence (AI) applications using MATLAB, even if they do not have a formal background in machine and deep learning. The goal of this course is to introduce tools and fundamental approaches for developing predictive models on biomedical signals. The course will cover the entire AI pipeline, from signal exploration to deployment, including: annotating time series biomedical signals automatically, creating deep learning models using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) for biomedical signal data, creating machine learning models for biomedical signal data, applying advanced signal pre-processing techniques for automated feature extraction, and automatically generating code for edge deployment of AI models. This is an introductory level class. No installation of MATLAB is necessary. Registration 2020-10-20 13:00:00 Online Online NIH Training Library 0 Data Science and Artificial Intelligence: Signals and Time Series Datasets Using MATLAB
180
Description
Register Data preprocessing and compound identification are two important steps for making sense of mass spectrometry-based untargeted metabolomics data. In this webinar, Dr. Xiuxia Du will demonstrate the software tool “ADAP” and the online resource “ADAP-KDB,” which were developed by her research group. ADAP extracts compound information from untargeted LC-MS and GC-MS data through data preprocessing. It carries out a sequence of computational steps, including ...Read More
Register Data preprocessing and compound identification are two important steps for making sense of mass spectrometry-based untargeted metabolomics data. In this webinar, Dr. Xiuxia Du will demonstrate the software tool “ADAP” and the online resource “ADAP-KDB,” which were developed by her research group. ADAP extracts compound information from untargeted LC-MS and GC-MS data through data preprocessing. It carries out a sequence of computational steps, including peak picking, peak grouping, alignment, and spectral deconvolution. Dr. Du will describe the principles of the computational algorithms that underlie these steps. ADAP-KDB is a spectral knowledge base that uses information from publicly available data repositories (such as the NIH’s Metabolomics Data Repository) for prioritizing unknown compounds. It consists of a computational workflow for extracting prioritization information and an online portal that allows researchers to manage and search the knowledge base. Registration for this webinar is required in advance. Speaker: Xiuxia Du, Ph.D. Dr. Du is a professor at the Department of Bioinformatics and Genomics, within the College of Computing and Informatics at the University of North Carolina at Charlotte.
Details
When
Wed, Oct 21, 2020 - 11:00 am - 12:00 pm
Where
Online
Register Data preprocessing and compound identification are two important steps for making sense of mass spectrometry-based untargeted metabolomics data. In this webinar, Dr. Xiuxia Du will demonstrate the software tool “ADAP” and the online resource “ADAP-KDB,” which were developed by her research group. ADAP extracts compound information from untargeted LC-MS and GC-MS data through data preprocessing. It carries out a sequence of computational steps, including peak picking, peak grouping, alignment, and spectral deconvolution. Dr. Du will describe the principles of the computational algorithms that underlie these steps. ADAP-KDB is a spectral knowledge base that uses information from publicly available data repositories (such as the NIH’s Metabolomics Data Repository) for prioritizing unknown compounds. It consists of a computational workflow for extracting prioritization information and an online portal that allows researchers to manage and search the knowledge base. Registration for this webinar is required in advance. Speaker: Xiuxia Du, Ph.D. Dr. Du is a professor at the Department of Bioinformatics and Genomics, within the College of Computing and Informatics at the University of North Carolina at Charlotte. 2020-10-21 11:00:00 Online 0 NIH Metabolomics Scientific Interest Group Webinar Series: ADAP and ADAP-KDB
186
Description
Register Presenter: Dr. Sam Dougaparsad Field Application Scientist It has been established that over half of all cancer types are driven by DNA Copy Number Variants (CNVs). In this presentation, we will demonstrate the use of Nexus Copy Number software for detection of CNVs from various microarray and NGS platforms and performance of ...Read More
Register Presenter: Dr. Sam Dougaparsad Field Application Scientist It has been established that over half of all cancer types are driven by DNA Copy Number Variants (CNVs). In this presentation, we will demonstrate the use of Nexus Copy Number software for detection of CNVs from various microarray and NGS platforms and performance of cohort analysis. In particular, we will use data from The Cancer Genome Atlas (TCGA) that has been manually curated and available to NCI personnel through the NexusDB database to showcase some of the advanced statistical and visualization capabilities of Nexus Copy Number. This will include identification of recurrent aberrations in different cancer types and sub-populations, statistical comparison analysis between different sets of samples, identification of regions with high predictive power for survival analysis and many other features. Subsequent sessions will expand on integrating expression and sequence variant data into the analysis.
Details
When
Thu, Oct 22, 2020 - 1:00 pm - 2:00 pm
Where
Online
Register Presenter: Dr. Sam Dougaparsad Field Application Scientist It has been established that over half of all cancer types are driven by DNA Copy Number Variants (CNVs). In this presentation, we will demonstrate the use of Nexus Copy Number software for detection of CNVs from various microarray and NGS platforms and performance of cohort analysis. In particular, we will use data from The Cancer Genome Atlas (TCGA) that has been manually curated and available to NCI personnel through the NexusDB database to showcase some of the advanced statistical and visualization capabilities of Nexus Copy Number. This will include identification of recurrent aberrations in different cancer types and sub-populations, statistical comparison analysis between different sets of samples, identification of regions with high predictive power for survival analysis and many other features. Subsequent sessions will expand on integrating expression and sequence variant data into the analysis. 2020-10-22 13:00:00 Online 0 Overview of CNV Analysis Using Nexus Copy Number Software
937
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin this course ends on October 23rd. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 29th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Fri, Oct 23 - Thu, Oct 29, 2020 -2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin this course ends on October 23rd. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on October 29th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-10-23 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion
188
Description
Register The Sequencing Facility (https://ostr.cancer.gov/...Read More
Register The Sequencing Facility (https://ostr.cancer.gov/resources/fnl-cores/sequencing-facility) is a second and third generation high-throughput sequencing core established by the Center for Cancer Research (CCR). Sequencing Facility’s (SF’s) primary mission is to utilize high-throughput sequencing technologies to enrich cancer research and ensure that the NCI community can remain at the leading edge of next-generation sequencing technology. The SF offers sequencing services on Illumina, Pacific Biosciences, and Oxford Nanopore platforms, as well as Single Cell and Genomics Mapping technology. These platforms have complementary strengths and can be used separately or in a combined approach to answer many genomics questions. Seminar Schedule: 9:00 AM – 9:10 AM: Introduction to CCR-Sequencing Facility Bao Tran, SF Director 9:10 AM – 9:40 AM: Overview of Illumina Sequencing Production Laboratory Jyoti Shetty, SF Illumina Laboratory Manager 9:40 AM – 10:00 AM: PacBio: Technology and Applications Caroline Fromont, SF Pacbio Laboratory 10:00 AM – 11:15 AM: New and Upcoming Sequencing Technologies at SF - New Solutions for Old Problems Monika Mehta, SF R&D Manager 11:15 AM – 12:00 PM: Overview of Data Analysis Workflows and Deliverables for Key NGS Applications at SF Yongmei Zhao, SF Bioinformatics Manager 12:00 PM – 12: 30 PM: Q & A Section All Presenters Questions/Comments and/or suggestions may be directed to Bao Tran at 301-360-3460 or tranb2@mail.nih.gov.
Details
When
Wed, Oct 28, 2020 - 9:00 am - 12:30 pm
Where
Online
Register The Sequencing Facility (https://ostr.cancer.gov/resources/fnl-cores/sequencing-facility) is a second and third generation high-throughput sequencing core established by the Center for Cancer Research (CCR). Sequencing Facility’s (SF’s) primary mission is to utilize high-throughput sequencing technologies to enrich cancer research and ensure that the NCI community can remain at the leading edge of next-generation sequencing technology. The SF offers sequencing services on Illumina, Pacific Biosciences, and Oxford Nanopore platforms, as well as Single Cell and Genomics Mapping technology. These platforms have complementary strengths and can be used separately or in a combined approach to answer many genomics questions. Seminar Schedule: 9:00 AM – 9:10 AM: Introduction to CCR-Sequencing Facility Bao Tran, SF Director 9:10 AM – 9:40 AM: Overview of Illumina Sequencing Production Laboratory Jyoti Shetty, SF Illumina Laboratory Manager 9:40 AM – 10:00 AM: PacBio: Technology and Applications Caroline Fromont, SF Pacbio Laboratory 10:00 AM – 11:15 AM: New and Upcoming Sequencing Technologies at SF - New Solutions for Old Problems Monika Mehta, SF R&D Manager 11:15 AM – 12:00 PM: Overview of Data Analysis Workflows and Deliverables for Key NGS Applications at SF Yongmei Zhao, SF Bioinformatics Manager 12:00 PM – 12: 30 PM: Q & A Section All Presenters Questions/Comments and/or suggestions may be directed to Bao Tran at 301-360-3460 or tranb2@mail.nih.gov. 2020-10-28 09:00:00 Online 0 CCR Sequencing Facility Seminar
86
Description
Register In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class to assist researchers in writing a manuscript. Participants will become familiar with statistical issues that can arise as well as recommendations to prevent them. At the end of the class, participants should have a good sense of what to do and not to do ...Read More
Register In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class to assist researchers in writing a manuscript. Participants will become familiar with statistical issues that can arise as well as recommendations to prevent them. At the end of the class, participants should have a good sense of what to do and not to do when writing the statistical sections of a manuscript. Most of the examples will be related to clinical research; however, anyone can benefit from the tips shared. Plenty of time will be devoted for questions, and references will be provided for more in-depth self-study. This class complements, How to Write a Research Paper Parts 1 & 2, as it specifically covers key points in writing the statistical portion of a manuscript – particularly for clinical research. Attendance to How to Write a Research Paper is not required to benefit from this class. Instructor(s) -- External Vendor: Paul Wakim
Details
Organizer
NIH Training Library
When
Wed, Oct 28, 2020 - 10:00 am - 11:30 am
Where
Online
Register In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class to assist researchers in writing a manuscript. Participants will become familiar with statistical issues that can arise as well as recommendations to prevent them. At the end of the class, participants should have a good sense of what to do and not to do when writing the statistical sections of a manuscript. Most of the examples will be related to clinical research; however, anyone can benefit from the tips shared. Plenty of time will be devoted for questions, and references will be provided for more in-depth self-study. This class complements, How to Write a Research Paper Parts 1 & 2, as it specifically covers key points in writing the statistical portion of a manuscript – particularly for clinical research. Attendance to How to Write a Research Paper is not required to benefit from this class. Instructor(s) -- External Vendor: Paul Wakim 2020-10-28 10:00:00 Online Online NIH Training Library 0 Statistical Considerations in Preparing Your Paper
184
Description
Register The NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a ...Read More
Register The NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteogenomics. The next TEAG forum will focus on NCI resources generated through the CPTAC pipeline that are available for conducting proteomic research. CPTAC Tumor Characterization Program and Data Resources Ana I. Robles, Ph.D. Office of Cancer Clinical Proteomics Research (OCCPR) National Cancer Institute CPTAC Pipeline Components and the Assay Portal Mehdi Mesri, M.Med.Sci., Ph.D. OCCPR National Cancer Institute Proteomic Pipeline Support and NCI Collaborations Tara Hiltke, Ph.D. OCCPR National Cancer Institute
Details
When
Wed, Oct 28, 2020 - 10:00 am - 11:30 am
Where
Online
Register The NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteogenomics. The next TEAG forum will focus on NCI resources generated through the CPTAC pipeline that are available for conducting proteomic research. CPTAC Tumor Characterization Program and Data Resources Ana I. Robles, Ph.D. Office of Cancer Clinical Proteomics Research (OCCPR) National Cancer Institute CPTAC Pipeline Components and the Assay Portal Mehdi Mesri, M.Med.Sci., Ph.D. OCCPR National Cancer Institute Proteomic Pipeline Support and NCI Collaborations Tara Hiltke, Ph.D. OCCPR National Cancer Institute 2020-10-28 10:00:00 Online 0 Trans-NCI Extramural Awareness Group (TEAG) Forum
938
Description
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For ...Read More
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on October 30th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on November 5th. IT IS YOUR RESPONSIBILITY to complete ~3.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Fri, Oct 30 - Thu, Nov 05, 2020 -2:00 pm - 2:00 pm
Where
Online Webinar
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on October 30th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on November 5th. IT IS YOUR RESPONSIBILITY to complete ~3.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-10-30 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Single-cell RNA-Seq Analysis on NIDAP – Tutorial & Discussion
137
Description
Details
Organizer
CDSL
When
Mon, Nov 02, 2020 - 10:00 am - 11:00 am
Where
Online
Register 2020-11-02 10:00:00 Online CDSL 0 Hierarchical clustering
68
Description
Attendees will learn how to use Partek Flow to identify cell subtypes using both gene and protein expression in a peripheral blood mononuclear cell (PBMC) sample using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) technology. CITE-Seq is a multimodal single cell phenotyping method. CITE-Seq data analysis starts with importing a count matrix file, followed by QC and filtering data. Clustering analysis and dimension reduction techniques are then used to visualize and identify subtypes ...Read More
Attendees will learn how to use Partek Flow to identify cell subtypes using both gene and protein expression in a peripheral blood mononuclear cell (PBMC) sample using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) technology. CITE-Seq is a multimodal single cell phenotyping method. CITE-Seq data analysis starts with importing a count matrix file, followed by QC and filtering data. Clustering analysis and dimension reduction techniques are then used to visualize and identify subtypes of cells. Differential expression detection among different subtypes will be demonstrated, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Basic knowledge of Partek Flow is required. Register
Details
Organizer
NIH Training Library
When
Mon, Nov 02, 2020 - 10:30 am - 12:00 pm
Where
Online
Attendees will learn how to use Partek Flow to identify cell subtypes using both gene and protein expression in a peripheral blood mononuclear cell (PBMC) sample using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) technology. CITE-Seq is a multimodal single cell phenotyping method. CITE-Seq data analysis starts with importing a count matrix file, followed by QC and filtering data. Clustering analysis and dimension reduction techniques are then used to visualize and identify subtypes of cells. Differential expression detection among different subtypes will be demonstrated, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Basic knowledge of Partek Flow is required. Register 2020-11-02 10:30:00 Online Online NIH Training Library 0 CITE-Seq Data Analysis in Partek Flow
189
Description
Register Learn about data science education resources available through NIH and ...Read More
Register Learn about data science education resources available through NIH and NCI, as well as online resources to learn about the data lifecycle, data management, data analysis and visualization, and other data science topics. For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov Check the Research Data Sources page on the Scientific Library website to learn more about research data online resources.
Details
Organizer
Scientific Library at Frederick
When
Mon, Nov 02, 2020 - 1:00 pm - 1:20 pm
Where
Online
Register Learn about data science education resources available through NIH and NCI, as well as online resources to learn about the data lifecycle, data management, data analysis and visualization, and other data science topics. For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov Check the Research Data Sources page on the Scientific Library website to learn more about research data online resources. 2020-11-02 13:00:00 Online Online Scientific Library at Frederick 0 Data Science Education Resources
207
Description
https://nci.rev.vbrick.com/#/videos/9db0a539-5bf2-4bc2-9e07-523161137e6e The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of ...Read More
https://nci.rev.vbrick.com/#/videos/9db0a539-5bf2-4bc2-9e07-523161137e6e The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule
Details
When
Tue, Nov 03, 2020 - 11:00 am - 1:00 pm
Where
Online
https://nci.rev.vbrick.com/#/videos/9db0a539-5bf2-4bc2-9e07-523161137e6e The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule 2020-11-03 11:00:00 Online Online 0 Software Carpentry: R for Reproducible Scientific Analysis
198
Description
Register Dr. Christina Curtis is an Assistant Professor in the ...Read More
Register Dr. Christina Curtis is an Assistant Professor in the Departments of Medicine (Oncology) and Genetics in the School of Medicine at Stanford University where she leads the Cancer Computational and Systems Biology Group and is Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. The Curtis laboratory is focused on the development and application of innovative experimental, computational, and analytical approaches to improve the diagnosis, treatment, and early detection of cancer. Dr. Curtis will be presenting on next generation sequencing and tumor initiation and progression.
Details
When
Tue, Nov 03, 2020 - 3:00 pm - 4:00 pm
Where
Online
Register Dr. Christina Curtis is an Assistant Professor in the Departments of Medicine (Oncology) and Genetics in the School of Medicine at Stanford University where she leads the Cancer Computational and Systems Biology Group and is Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. The Curtis laboratory is focused on the development and application of innovative experimental, computational, and analytical approaches to improve the diagnosis, treatment, and early detection of cancer. Dr. Curtis will be presenting on next generation sequencing and tumor initiation and progression. 2020-11-03 15:00:00 Online Online 0 Next Generation Sequencing and Tumor Initiation and Progression
69
Description
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use ...Read More
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. Feel free to install the IGV browser in advance from here - IGV download (the UCSV browser is web browser based). Register
Details
Organizer
NIH Training Library
When
Wed, Nov 04, 2020 - 1:00 pm - 2:00 pm
Where
Online
This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them. Feel free to install the IGV browser in advance from here - IGV download (the UCSV browser is web browser based). Register 2020-11-04 13:00:00 Online Online NIH Training Library 0 Genome Browsers
177
Description
[one_third]Send email to staff@hpc.nih.gov to get the Zoom URL All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff ...Read More
[one_third]Send email to staff@hpc.nih.gov to get the Zoom URL All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1. We'll try to address simpler issues on the spot and follow up on more complex questions after the Zoom-In.
Details
Organizer
HPC Biowulf
When
Wed, Nov 04, 2020 - 1:00 pm - 3:00 pm
Where
Online
[one_third]Send email to staff@hpc.nih.gov to get the Zoom URL All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1. We'll try to address simpler issues on the spot and follow up on more complex questions after the Zoom-In. 2020-11-04 13:00:00 Online HPC Biowulf 0 Zoom-In Consult with Biowulf Staff
195
Description
Register Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly ...Read More
Register Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics. Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects.
Details
Organizer
NIH Training Library
When
Thu, Nov 05, 2020 - 10:00 am - 3:00 pm
Where
Online
Register Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA's knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics. Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects. 2020-11-05 10:00:00 Online Online NIH Training Library 0 Ingenuity Pathway Analysis (IPA)
187
Description
Register Presenter: Dr. Han Liang Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to evaluate biomarkers and mechanisms underlying sensitivity and resistance to cancer therapy. The MD Anderson Cancer Center platform currently contains ~500 protein markers, covering all major signaling pathways. The Cancer Proteome Atlas (TCPA) we developed (http://tcpaportal.org) is the sole dedicated ...Read More
Register Presenter: Dr. Han Liang Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to evaluate biomarkers and mechanisms underlying sensitivity and resistance to cancer therapy. The MD Anderson Cancer Center platform currently contains ~500 protein markers, covering all major signaling pathways. The Cancer Proteome Atlas (TCPA) we developed (http://tcpaportal.org) is the sole dedicated bioinformatics resource for RPPA data. Currently, it contains two analytic platforms: one contains >8000 patient samples; and the other contains >1500 cell line samples. We are developing the third component focusing on adaptive RPPA response given drug treatments. We have built an integrated data portal that contains user-friendly analytic and visualization tools for a broad biomedical community to utilize these data.To learn more please visit: https://tcpaportal.org/tcpa/ The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://itcr.cancer.gov/
Details
When
Thu, Nov 05, 2020 - 11:00 am - 12:00 pm
Where
Online
Register Presenter: Dr. Han Liang Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to evaluate biomarkers and mechanisms underlying sensitivity and resistance to cancer therapy. The MD Anderson Cancer Center platform currently contains ~500 protein markers, covering all major signaling pathways. The Cancer Proteome Atlas (TCPA) we developed (http://tcpaportal.org) is the sole dedicated bioinformatics resource for RPPA data. Currently, it contains two analytic platforms: one contains >8000 patient samples; and the other contains >1500 cell line samples. We are developing the third component focusing on adaptive RPPA response given drug treatments. We have built an integrated data portal that contains user-friendly analytic and visualization tools for a broad biomedical community to utilize these data.To learn more please visit: https://tcpaportal.org/tcpa/ The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://itcr.cancer.gov/ 2020-11-05 11:00:00 Online Online 0 Introduction to Cancer Proteome Atlas
943
Description
This two-part lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. Part I This section will introduce the general principles underlying RNA-SEQ and the basic steps in experimental design, RNA sample preparation, sequencing technologies (their strengths and weaknesses), quality control, sequence mapping and alignment. RNASeq_Final_2020.1 ...Read More
This two-part lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. Part I This section will introduce the general principles underlying RNA-SEQ and the basic steps in experimental design, RNA sample preparation, sequencing technologies (their strengths and weaknesses), quality control, sequence mapping and alignment. RNASeq_Final_2020.1 Meeting Information for Thursday, Nov 5 at 1 PM Meeting Link Meeting number: 172 256 6113 Password: wF4gdaPq2@4 Join by phone 1-650-479-3207 Call-in toll number (US/Canada)
Register
Organizer
BTEP
When
Thu, Nov 05, 2020 - 1:00 pm - 2:00 pm
Where
Online Webinar
This two-part lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. Part I This section will introduce the general principles underlying RNA-SEQ and the basic steps in experimental design, RNA sample preparation, sequencing technologies (their strengths and weaknesses), quality control, sequence mapping and alignment. RNASeq_Final_2020.1 Meeting Information for Thursday, Nov 5 at 1 PM Meeting Link Meeting number: 172 256 6113 Password: wF4gdaPq2@4 Join by phone 1-650-479-3207 Call-in toll number (US/Canada) 2020-11-05 13:00:00 Online Webinar Online Peter FitzGerald (GAU) BTEP 0 An Introduction to RNA-Seq Data Analysis, Part One
194
Description
Register Overview: Characterizing the transcriptomic profiles of individual cells by single-cell RNA sequencing (scRNA-seq) has become a universal tool to identify both known and novel cell populations, ushering science in a new era of single cell biology. However, scRNA-seq utilizes dissociated cells with results in the loss of spatial organization of the cell population being analyzed. It is therefore essential to complement scRNA-seq analysis with ...Read More
Register Overview: Characterizing the transcriptomic profiles of individual cells by single-cell RNA sequencing (scRNA-seq) has become a universal tool to identify both known and novel cell populations, ushering science in a new era of single cell biology. However, scRNA-seq utilizes dissociated cells with results in the loss of spatial organization of the cell population being analyzed. It is therefore essential to complement scRNA-seq analysis with RNAscope in situ hybridization (ISH) in order to obtain visual confirmation of both single cell and spatial gene expression. In this webinar, Dr. Ariel Levine from NIH NINDS will share her latest publication using RNAscope HiPlex assay, to reveal spinal cord cell type organization, validate a combinatorial set of markers for in-tissue spatial gene expression analysis, and optimize the computational classification Presented by: Ariel Levine, Ph.D. Earl Stadtman Investigator National Institute of Neurological Disorders and Stroke
Details
When
Thu, Nov 05, 2020 - 1:00 pm - 2:00 pm
Where
Online
Register Overview: Characterizing the transcriptomic profiles of individual cells by single-cell RNA sequencing (scRNA-seq) has become a universal tool to identify both known and novel cell populations, ushering science in a new era of single cell biology. However, scRNA-seq utilizes dissociated cells with results in the loss of spatial organization of the cell population being analyzed. It is therefore essential to complement scRNA-seq analysis with RNAscope in situ hybridization (ISH) in order to obtain visual confirmation of both single cell and spatial gene expression. In this webinar, Dr. Ariel Levine from NIH NINDS will share her latest publication using RNAscope HiPlex assay, to reveal spinal cord cell type organization, validate a combinatorial set of markers for in-tissue spatial gene expression analysis, and optimize the computational classification Presented by: Ariel Levine, Ph.D. Earl Stadtman Investigator National Institute of Neurological Disorders and Stroke 2020-11-05 13:00:00 Online Online 0 Spatial Transcriptomic Analysis and Cell Type Characterization Using the RNAscope HiPlex Assay
203
Description
Register Abstract: The need to integrate knowledge types into big data analytics, generally referred to as explanatory-artificial-intelligence (x-AI), is growing. This talk will describe progress with three approaches to such knowledge enrichment: 1) the use of Independent Component Analysis (ICA) to define independently modulated sets of genes in bacterial transcriptomes, 2) the use of pangenome analysis for the thousands of bacterial genome sequences being generated, ...Read More
Register Abstract: The need to integrate knowledge types into big data analytics, generally referred to as explanatory-artificial-intelligence (x-AI), is growing. This talk will describe progress with three approaches to such knowledge enrichment: 1) the use of Independent Component Analysis (ICA) to define independently modulated sets of genes in bacterial transcriptomes, 2) the use of pangenome analysis for the thousands of bacterial genome sequences being generated, and 3) the use of machine learning methods for the analysis of antimicrobial resistance. The first case illustrates the principle of ‘getting answers to questions not asked,’ the second case illuminates ‘what is learned with scale,’ and the third case shows how mechanisms are built into genome-wide association studies (GWAS) using flux balance analysis (FBA). Presenter: Bernhard Palsson, PhD Distinguished Galletti Professor of Bioengineering, Department of Bioengineering, UC San Diego Professor of Pediatrics, UC San Diego School of Medicine Meeting ID: 161 756 1452 Passcode: 586729 One tap mobile +16692545252,,1617561452#,,,,,,0#,,586729# US (San Jose) +16468287666,,1617561452#,,,,,,0#,,586729# US (New York) Dial by your location +1 669 254 5252 US (San Jose) +1 646 828 7666 US (New York) Meeting ID: 161 756 1452 Passcode: 586729 Find your local number https://nih.zoomgov.com/u/ayFfvRtd4 Join by SIP 1617561452@sip.zoomgov.com Join by H.323 161.199.138.10 (US West) 161.199.136.10 (US East) Meeting ID: 161 756 1452 Passcode: 586729 For questions, please contact Steve Tsang at steve.tsang@nih.gov
Details
Organizer
NIAID
When
Fri, Nov 06, 2020 - 12:00 pm - 1:00 pm
Where
Online
Register Abstract: The need to integrate knowledge types into big data analytics, generally referred to as explanatory-artificial-intelligence (x-AI), is growing. This talk will describe progress with three approaches to such knowledge enrichment: 1) the use of Independent Component Analysis (ICA) to define independently modulated sets of genes in bacterial transcriptomes, 2) the use of pangenome analysis for the thousands of bacterial genome sequences being generated, and 3) the use of machine learning methods for the analysis of antimicrobial resistance. The first case illustrates the principle of ‘getting answers to questions not asked,’ the second case illuminates ‘what is learned with scale,’ and the third case shows how mechanisms are built into genome-wide association studies (GWAS) using flux balance analysis (FBA). Presenter: Bernhard Palsson, PhD Distinguished Galletti Professor of Bioengineering, Department of Bioengineering, UC San Diego Professor of Pediatrics, UC San Diego School of Medicine Meeting ID: 161 756 1452 Passcode: 586729 One tap mobile +16692545252,,1617561452#,,,,,,0#,,586729# US (San Jose) +16468287666,,1617561452#,,,,,,0#,,586729# US (New York) Dial by your location +1 669 254 5252 US (San Jose) +1 646 828 7666 US (New York) Meeting ID: 161 756 1452 Passcode: 586729 Find your local number https://nih.zoomgov.com/u/ayFfvRtd4 Join by SIP 1617561452@sip.zoomgov.com Join by H.323 161.199.138.10 (US West) 161.199.136.10 (US East) Meeting ID: 161 756 1452 Passcode: 586729 For questions, please contact Steve Tsang at steve.tsang@nih.gov 2020-11-06 12:00:00 Online Online NIAID 0 Progress with knowledge enriched data analytics
939
Description
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the ...Read More
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin this course ends on November 6th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on November 12th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Fri, Nov 06 - Thu, Nov 12, 2020 -2:00 pm - 3:00 pm
Where
Online Webinar
RNA sequencing (RNA-seq) is a widely used method in genomics, which enables the interrogation of whole cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a RNA-seq workflow on the NIDAP collaboration platform, which is available and free to use for all NIH researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin this course ends on November 6th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on November 12th. IT IS YOUR RESPONSIBILITY to complete ~4.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind RNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic Bulk RNA-seq analysis, including filtering and QC, PCA, expression heatmaps, differential expression of genes analysis, volcano plots, and pathway/gene set analysis. After you complete this training, you will become eligible for further training workshops in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-11-06 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Bulk RNA-Seq Analysis on NIDAP – Tutorial & Discussion
190
Description
Register Learn how to locate data ...Read More
Register Learn how to locate data sharing policies for NIH, other public and private research funders, and journal publishers, and find templates for creating data management plans through DMP Tool. For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources.
Details
Organizer
Scientific Library at Frederick
When
Mon, Nov 09, 2020 - 1:00 pm - 1:20 pm
Where
Online
Register Learn how to locate data sharing policies for NIH, other public and private research funders, and journal publishers, and find templates for creating data management plans through DMP Tool. For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources. 2020-11-09 13:00:00 Online Online Scientific Library at Frederick 0 Data Sharing Policies and Data Management Plans
942
Description
Link for ALL class sessions including help sessions. Register Meeting number: 172 866 2623 Password: NYy4m3V3i3* Dial in: 650-479-3207 The goal of this workshop is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and ...Read More
Link for ALL class sessions including help sessions. Register Meeting number: 172 866 2623 Password: NYy4m3V3i3* Dial in: 650-479-3207 The goal of this workshop is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install R and Rstudio onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. Nov 3, 11 AM - 1 PM, Week 1, Introduction to R and RStudio, Project Management with RStudio and Seeking Help (link to recording) Nov 10, 11 AM - 1 PM, Week 2, Data Structures, Exploring Data Frames and Subsetting Data Nov 17, 11 AM - 1 PM, Week 3, Creating Publication-Quality Graphics with ggplot2 Nov 24 NO CLASS Thanksgiving Holiday Dec 1, 11 AM - 1 PM, Week 4, Control Flow, Vectorization and Functions Explained Dec 8, 11 AM - 1 PM, Week 5, Writing Data, Dataframe Manipulation with dplyr and Dataframe Manipulation with tidyr Dec 15,11 AM - 1 PM, Week 6, Producing Reports with knitr and Writing Good Software For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule Help Sessions Nov 5, 12 - 1 PM, Week 1, Setting up access to the Course Materials with Git, Questions from Week 1 Nov 12, 12 - 1 PM, Week 2 Nov 19, 12 - 1 PM, Week 3 Dec 3, 12 - 1 PM, Week 4 Dec 10, 12 - 1 PM, Week 5 Dec 17, 12 - 1 PM, Week 6
Register
Organizer
BTEP
When
Tue, Nov 10, 2020 - 11:00 am - 1:00 pm
Where
Online Webinar
Link for ALL class sessions including help sessions. Register Meeting number: 172 866 2623 Password: NYy4m3V3i3* Dial in: 650-479-3207 The goal of this workshop is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install R and Rstudio onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. Nov 3, 11 AM - 1 PM, Week 1, Introduction to R and RStudio, Project Management with RStudio and Seeking Help (link to recording) Nov 10, 11 AM - 1 PM, Week 2, Data Structures, Exploring Data Frames and Subsetting Data Nov 17, 11 AM - 1 PM, Week 3, Creating Publication-Quality Graphics with ggplot2 Nov 24 NO CLASS Thanksgiving Holiday Dec 1, 11 AM - 1 PM, Week 4, Control Flow, Vectorization and Functions Explained Dec 8, 11 AM - 1 PM, Week 5, Writing Data, Dataframe Manipulation with dplyr and Dataframe Manipulation with tidyr Dec 15,11 AM - 1 PM, Week 6, Producing Reports with knitr and Writing Good Software For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule Help Sessions Nov 5, 12 - 1 PM, Week 1, Setting up access to the Course Materials with Git, Questions from Week 1 Nov 12, 12 - 1 PM, Week 2 Nov 19, 12 - 1 PM, Week 3 Dec 3, 12 - 1 PM, Week 4 Dec 10, 12 - 1 PM, Week 5 Dec 17, 12 - 1 PM, Week 6 2020-11-10 11:00:00 Online Webinar Online Amy Stonelake (BTEP),George Zaki (FNLCR) BTEP 0 Software Carpentry: R for Reproducible Scientific Analysis
206
Description
Register The goal of these workshops ...Read More
Register The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule
Details
When
Tue, Nov 10, 2020 - 11:00 am - 1:00 pm
Where
Online
Register The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule 2020-11-10 11:00:00 Online Online 0 Software Carpentry: R for Reproducible Scientific Analysis
204
Description
Register In this seminar, Dr. Nikhil Wagle, M.D., and Dr. Corrie Painter, Ph.D., will speak on behalf of “Count Me In,” a non-profit organization that allows researchers to work directly with patients and advocacy groups, along with software engineers and computational scientists, to collect, analyze, and share de-identified data. Presenters: Nikhil Wagle, M.D., ...Read More
Register In this seminar, Dr. Nikhil Wagle, M.D., and Dr. Corrie Painter, Ph.D., will speak on behalf of “Count Me In,” a non-profit organization that allows researchers to work directly with patients and advocacy groups, along with software engineers and computational scientists, to collect, analyze, and share de-identified data. Presenters: Nikhil Wagle, M.D., is the director of “Count Me In” and a medical oncologist and cancer researcher at Dana-Farber Cancer Institute and the Board Institute of MIT and Harvard. Corrie Painter, Ph.D., is the associate director of “Count Me In” and a research scientist at the Broad Institute of MIT and Harvard.
Details
Organizer
CBIIT
When
Tue, Nov 10, 2020 - 12:00 pm - 1:00 pm
Where
Online
Register In this seminar, Dr. Nikhil Wagle, M.D., and Dr. Corrie Painter, Ph.D., will speak on behalf of “Count Me In,” a non-profit organization that allows researchers to work directly with patients and advocacy groups, along with software engineers and computational scientists, to collect, analyze, and share de-identified data. Presenters: Nikhil Wagle, M.D., is the director of “Count Me In” and a medical oncologist and cancer researcher at Dana-Farber Cancer Institute and the Board Institute of MIT and Harvard. Corrie Painter, Ph.D., is the associate director of “Count Me In” and a research scientist at the Broad Institute of MIT and Harvard. 2020-11-10 12:00:00 Online Online CBIIT 0 Partnering with the Public for Biomedical Research Seminar Series: “Count Me In; Partnering with Patients to Accelerate Cancer Discoveries”
944
Description
This two-part lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. Part II This second part of the talk will cover quantification of gene expression, differential gene expression and the use of visualization techniques to further analyze and to effectively communicate the salient points of the data and analysis. Additionally, this section will provide guidance on finding resource about ...Read More
This two-part lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. Part II This second part of the talk will cover quantification of gene expression, differential gene expression and the use of visualization techniques to further analyze and to effectively communicate the salient points of the data and analysis. Additionally, this section will provide guidance on finding resource about different file formats used to represent Next Generation Sequence (NGS) data, and a brief preview of the other BTEP talks coming up during RNA-Seq Week(s).   NGS_File_Formats RNASEQ_part2   Meeting Information Meeting Link Meeting number:172 058 3898 Password:NFhZ7k2Tn$9 Join by video system Dial 1722566113@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in number (US/Canada) Access code: 172 256 6113
Register
Organizer
BTEP
When
Tue, Nov 10, 2020 - 1:00 pm - 2:00 pm
Where
Online Webinar
This two-part lecture will provide an overview of RNA-Seq technology, its various application and shortcomings, as well as detailing the steps involved in analyzing the resulting data. Part II This second part of the talk will cover quantification of gene expression, differential gene expression and the use of visualization techniques to further analyze and to effectively communicate the salient points of the data and analysis. Additionally, this section will provide guidance on finding resource about different file formats used to represent Next Generation Sequence (NGS) data, and a brief preview of the other BTEP talks coming up during RNA-Seq Week(s).   NGS_File_Formats RNASEQ_part2   Meeting Information Meeting Link Meeting number:172 058 3898 Password:NFhZ7k2Tn$9 Join by video system Dial 1722566113@cbiit.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone 1-650-479-3207 Call-in number (US/Canada) Access code: 172 256 6113 2020-11-10 13:00:00 Online Webinar Online Peter FitzGerald (GAU) BTEP 0 An Introduction to RNA-Seq Analysis, Part Two
70
Description
During the first part of the session attendees will learn how to use Partek Flow to perform special transcriptions by overlaying cell location information and histological images on single cell RNA-Seq data using 10X genomics data. During the second part of the session, attendees will learn how to run trajectory analysis and calculate pseudo-time. Register
During the first part of the session attendees will learn how to use Partek Flow to perform special transcriptions by overlaying cell location information and histological images on single cell RNA-Seq data using 10X genomics data. During the second part of the session, attendees will learn how to run trajectory analysis and calculate pseudo-time. Register
Details
Organizer
NIH Training Library
When
Thu, Nov 12, 2020 - 10:30 am - 12:00 pm
Where
Online
During the first part of the session attendees will learn how to use Partek Flow to perform special transcriptions by overlaying cell location information and histological images on single cell RNA-Seq data using 10X genomics data. During the second part of the session, attendees will learn how to run trajectory analysis and calculate pseudo-time. Register 2020-11-12 10:30:00 Online Online NIH Training Library 0 Spatial Transcriptomics and Trajectory Analysis with Partek Flow
945
Description
THIS EVENT HAS BEEN CANCELLED
Bulk RNA-Seq Analysis on Partek Flow Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=mb7dc1d31feef38ae505d7e458152cc58 You do not need to have your HPC or Partek Flow access set-up to attend class, but here is how to do it. The Office of ...Read More
THIS EVENT HAS BEEN CANCELLED
Bulk RNA-Seq Analysis on Partek Flow Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=mb7dc1d31feef38ae505d7e458152cc58 You do not need to have your HPC or Partek Flow access set-up to attend class, but here is how to do it. The Office of Science and Technology Resources (OSTR) has purchased licenses to Partek Flow for CCR scientists. To access these licenses, please follow the directions on the NIH HPC website at https://partekflow.cit.nih.gov/. You will need to have an HPC account (Biowulf) to access Partek Flow, instructions for getting one set-up are here (https://hpc.nih.gov/docs/accounts.html). If you are not a CCR scientist, please contact the NIH Library, as they also have Partek Flow licenses available to NIH scientists (https://www.nihlibrary.nih.gov/services/bioinformatics-support/analysis-tools).
Register
Organizer
BTEP
When
Thu, Nov 12, 2020 - 1:00 pm - 2:00 pm
Where
Online Webinar
THIS EVENT HAS BEEN CANCELLEDBulk RNA-Seq Analysis on Partek Flow Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=mb7dc1d31feef38ae505d7e458152cc58 You do not need to have your HPC or Partek Flow access set-up to attend class, but here is how to do it. The Office of Science and Technology Resources (OSTR) has purchased licenses to Partek Flow for CCR scientists. To access these licenses, please follow the directions on the NIH HPC website at https://partekflow.cit.nih.gov/. You will need to have an HPC account (Biowulf) to access Partek Flow, instructions for getting one set-up are here (https://hpc.nih.gov/docs/accounts.html). If you are not a CCR scientist, please contact the NIH Library, as they also have Partek Flow licenses available to NIH scientists (https://www.nihlibrary.nih.gov/services/bioinformatics-support/analysis-tools). 2020-11-12 13:00:00 Online Webinar Online Xiaowen Wang (Partek) BTEP 0 BTEP bulk RNA-Seq Weeks: Partek Flow - CANCELLED
44
Description
New computational opportunities and challenges have emerged within the cancer research and clinical application fields, as the size, source and complexity of cancer datasets have grown. Simultaneously, advances in computational capabilities, with exceptional growth in AI and deep learning, are reaching unprecedented scales. This Sixth Computational Approaches for Cancer Workshop 2020 (CAFCW20) will bring together a wide-range of individuals including clinicians, cancer biologists, mathematicians, data scientists, computational scientists, engineers, developers, thought leaders and others with an ...Read More
New computational opportunities and challenges have emerged within the cancer research and clinical application fields, as the size, source and complexity of cancer datasets have grown. Simultaneously, advances in computational capabilities, with exceptional growth in AI and deep learning, are reaching unprecedented scales. This Sixth Computational Approaches for Cancer Workshop 2020 (CAFCW20) will bring together a wide-range of individuals including clinicians, cancer biologists, mathematicians, data scientists, computational scientists, engineers, developers, thought leaders and others with an interest in advancing the use of computation to better understand, diagnose, treat and prevent cancer. As an interdisciplinary workshop, the sharing of insight and challenges fosters collaborations and future innovations accelerating progress in computationally and data-driven cancer research and clinical applications. High-performance computing (HPC) has been and will continue to be a key component of cancer research. Industry, academic and government interest is demonstrably high with ongoing commitments, new announcements, advances and new opportunities involving cancer and computing. One need only review recommendations provided by the National Cancer Moonshot Blue Ribbon Panel to confirm the increasingly visible and critical role computing and HPC in particular will play in accelerating cancer research objectives. As HPC-related efforts from projects funded through the 21st Century Cures Act begin to mature, the workshop will provide an ongoing avenue for new computational approaches involving HPC at all scales to be shared with the growing community. The Computational Approaches for Cancer workshop series originated in early 2015, following observations that the topic of cancer was already pervasive at the SC conference, yet no venue at SC existed to bring the key community together. The response has been favorable for the first five workshops with over 80 participants in each of the first two years, expanding to an estimated 150 attendees at SC17 and at room capacity in SC18 and SC19. Enthusiasm for the workshop continues to grow with many ideas and challenges shared, collaborations envisioned and needs identified. The successful call for papers in SC17 resulted in proceedings published for Open Access in BMC Bioinformatics, a growing number of submissions in SC18 and a record number of submissions in SC19. At SC19, the best paper award was given to a team who has progressively presented their work at the series of SC Computational Approaches for Cancer workshops. Questions? Contact cafcw@nih.gov
Details
When
Fri, Nov 13, 2020 - 10:00 am - 6:30 pm
Where
Online
New computational opportunities and challenges have emerged within the cancer research and clinical application fields, as the size, source and complexity of cancer datasets have grown. Simultaneously, advances in computational capabilities, with exceptional growth in AI and deep learning, are reaching unprecedented scales. This Sixth Computational Approaches for Cancer Workshop 2020 (CAFCW20) will bring together a wide-range of individuals including clinicians, cancer biologists, mathematicians, data scientists, computational scientists, engineers, developers, thought leaders and others with an interest in advancing the use of computation to better understand, diagnose, treat and prevent cancer. As an interdisciplinary workshop, the sharing of insight and challenges fosters collaborations and future innovations accelerating progress in computationally and data-driven cancer research and clinical applications. High-performance computing (HPC) has been and will continue to be a key component of cancer research. Industry, academic and government interest is demonstrably high with ongoing commitments, new announcements, advances and new opportunities involving cancer and computing. One need only review recommendations provided by the National Cancer Moonshot Blue Ribbon Panel to confirm the increasingly visible and critical role computing and HPC in particular will play in accelerating cancer research objectives. As HPC-related efforts from projects funded through the 21st Century Cures Act begin to mature, the workshop will provide an ongoing avenue for new computational approaches involving HPC at all scales to be shared with the growing community. The Computational Approaches for Cancer workshop series originated in early 2015, following observations that the topic of cancer was already pervasive at the SC conference, yet no venue at SC existed to bring the key community together. The response has been favorable for the first five workshops with over 80 participants in each of the first two years, expanding to an estimated 150 attendees at SC17 and at room capacity in SC18 and SC19. Enthusiasm for the workshop continues to grow with many ideas and challenges shared, collaborations envisioned and needs identified. The successful call for papers in SC17 resulted in proceedings published for Open Access in BMC Bioinformatics, a growing number of submissions in SC18 and a record number of submissions in SC19. At SC19, the best paper award was given to a team who has progressively presented their work at the series of SC Computational Approaches for Cancer workshops. Questions? Contact cafcw@nih.gov 2020-11-13 10:00:00 Online Online 0 Sixth Computational Approaches for Cancer Workshop (CAFCW20)
940
Description
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For ...Read More
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on November 13th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on November 19th. IT IS YOUR RESPONSIBILITY to complete ~3.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data.
Register
Organizer
BTEP
When
Fri, Nov 13 - Thu, Nov 19, 2020 -2:00 pm - 3:00 pm
Where
Online Webinar
Single-cell RNA sequencing (scRNA-seq) is a relatively new and powerful method in genomics, which enables the interrogation of whole single cellular transcriptomes. The CCR Collaborative Bioinformatics Resource (CCBR) has implemented a basic scRNA-seq workflow, based on the popular Seurat workflow, on the NIH Integrated Data Analysis Platform (NIDAP). This platform is available and free to use for all NCI researchers. The platform allows users to explore and build their analyses in a graphical environment. For researchers who are conversant in code, or who are interested in learning, all of the workflow code is easily viewable and modifiable, as well. This course will consist of a series of video tutorials that you may work through at your own speed, followed by a live virtual Discussion class in which you can ask our instructors to clarify or expand on any topics covered in the video tutorials. You will be emailed instructions on how to begin on November 13th. At that time, you will receive access to the tutorials and documentation, as well as a calendar invitation to your live Discussion class. Your live Discussion class will occur at 2pm on November 19th. IT IS YOUR RESPONSIBILITY to complete ~3.5 hours of self-guided video tutorials BEFORE to your scheduled Discussion class so that you can effectively participate in the discussion. Topics you will learn about include the theory behind scRNA-seq and many of the methodologies we use in the analysis, as well as a full tutorial on how to complete a basic scRNA-seq analysis, including filtering and QC, PCA and merging of objects, TSNE and UMAP projecctions, cell type annotation, coloring cells by gene expression or other metadata, and differential expression of genes analysis. After you complete this training, you will become eligible for further training tutorials in which you will learn how to upload your own data to the platform to begin your own analyses and gain access to workshops with our trainers to help you overcome any dataset-specific challenges that you may encounter. PLEASE NOTE: Trainees will need their own NIH user name and password and an NIH computer capable of connecting to the secure NIH network using VPN.  This is necessary to ensure the security and privacy of the data. 2020-11-13 14:00:00 Online Webinar Online Joshua Meyer (CCBR) BTEP 0 Single-cell RNA-Seq Analysis on NIDAP – Tutorial & Discussion
199
Description
Register Description: This webinar offers an introduction to FlowJo, an application designed to help with data ...Read More
Register Description: This webinar offers an introduction to FlowJo, an application designed to help with data archiving, analysis, and reporting. The training begins by showing how to load samples and then outlines the steps to take to generate meaningful results. Participants will learn how best to classify and show results (e.g., dot plots, zebra plots, histograms) and how to prepare figures for publication. Other features will address how to customize your app and ideas for making the app run faster and more efficiently. For questions, contact Dr. Daoud Meerzaman at meerzamd@mail.nih.gov Speaker: Dr. Christian Aguilera-Sandoval, FlowJo, LLC
Details
Organizer
CBIIT
When
Fri, Nov 13, 2020 - 2:00 pm - 4:00 pm
Where
Online
Register Description: This webinar offers an introduction to FlowJo, an application designed to help with data archiving, analysis, and reporting. The training begins by showing how to load samples and then outlines the steps to take to generate meaningful results. Participants will learn how best to classify and show results (e.g., dot plots, zebra plots, histograms) and how to prepare figures for publication. Other features will address how to customize your app and ideas for making the app run faster and more efficiently. For questions, contact Dr. Daoud Meerzaman at meerzamd@mail.nih.gov Speaker: Dr. Christian Aguilera-Sandoval, FlowJo, LLC 2020-11-13 14:00:00 Online Online CBIIT 0 Introduction to FlowJo Cytometry
219
Description
https://ncihub.org/resources/2404 Abstract: In many applications in genomics, large data sets are created and lightly used before being shared with other researchers (ideally) or simply tossed away on hard drives. The Cancer Cloud project has enabled some of this very large data to be shared among qualified researchers in order to facilitate a greater understanding of oncogenesis. One issue that continuously comes up, however, is that ...Read More
https://ncihub.org/resources/2404 Abstract: In many applications in genomics, large data sets are created and lightly used before being shared with other researchers (ideally) or simply tossed away on hard drives. The Cancer Cloud project has enabled some of this very large data to be shared among qualified researchers in order to facilitate a greater understanding of oncogenesis. One issue that continuously comes up, however, is that simply using the data requires specialized skills outside of the biological realm. A blend of computer science and biology is required in order to properly be able to access and appropriately run computations on data as it gets too big to scale. This presentation goes over an application on the ISB Cancer Cloud where whole genome sequencing was used to generate variant calls for downstream research. Due to the size of the whole genome sequences, this was cost prohibitive to do it on lab computers and had to be done in the cloud. Also due to the size of the data, custom processes needed to be put into place to manage and queue the computations as well as to parallelize and reconstruct them properly. This workflow has been made available open source for adaptation to other pipelines and the WGS variant data is being made available to qualified researchers in the cancer cloud. Presenter: Dr. John Torcivia, Director of AI Deployment, Clarifai, Inc. Department of Biochemistry, George Washington University Abstracts, Slides and Recordings from past meetings can be found here: https://ncihub.org/groups/cwig (New Link!) For questions and subscription, please contact , Durga Addepalli at kanakadurga.addepalli@nih.gov
Details
When
Fri, Nov 13, 2020 - 3:00 pm - 4:00 pm
Where
Online
https://ncihub.org/resources/2404 Abstract: In many applications in genomics, large data sets are created and lightly used before being shared with other researchers (ideally) or simply tossed away on hard drives. The Cancer Cloud project has enabled some of this very large data to be shared among qualified researchers in order to facilitate a greater understanding of oncogenesis. One issue that continuously comes up, however, is that simply using the data requires specialized skills outside of the biological realm. A blend of computer science and biology is required in order to properly be able to access and appropriately run computations on data as it gets too big to scale. This presentation goes over an application on the ISB Cancer Cloud where whole genome sequencing was used to generate variant calls for downstream research. Due to the size of the whole genome sequences, this was cost prohibitive to do it on lab computers and had to be done in the cloud. Also due to the size of the data, custom processes needed to be put into place to manage and queue the computations as well as to parallelize and reconstruct them properly. This workflow has been made available open source for adaptation to other pipelines and the WGS variant data is being made available to qualified researchers in the cancer cloud. Presenter: Dr. John Torcivia, Director of AI Deployment, Clarifai, Inc. Department of Biochemistry, George Washington University Abstracts, Slides and Recordings from past meetings can be found here: https://ncihub.org/groups/cwig (New Link!) For questions and subscription, please contact , Durga Addepalli at kanakadurga.addepalli@nih.gov 2020-11-13 15:00:00 Online Online 0 Application of Genomics Big Data on the Cancer Cloud: Making use of difficult data
197
Description
Register RStudio will be planning quarterly webinars to support the HHS Data Science and Statistical Programming communities. The first installment will be focused on Creating Reproducible Data Science. Please join Alex Gold as he shares best practices and demonstrates project-based workflow, version control with git, creating templates and packages, and reproducing data science environments. Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using ...Read More
Register RStudio will be planning quarterly webinars to support the HHS Data Science and Statistical Programming communities. The first installment will be focused on Creating Reproducible Data Science. Please join Alex Gold as he shares best practices and demonstrates project-based workflow, version control with git, creating templates and packages, and reproducing data science environments. Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting. For more information about RStudio in Life Sciences: https://rstudio.com/solutions/pharma/ Learn more about RStudio’s recommended professional data science solution for every team: https://rstudio.com/products/team/ Contact our team with any questions: life-sciences-healthcare@rstudio.com Presenter: Alex Gold
Details
When
Fri, Nov 13, 2020 - 4:44 pm - 4:44 pm
Where
Online
Register RStudio will be planning quarterly webinars to support the HHS Data Science and Statistical Programming communities. The first installment will be focused on Creating Reproducible Data Science. Please join Alex Gold as he shares best practices and demonstrates project-based workflow, version control with git, creating templates and packages, and reproducing data science environments. Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting. For more information about RStudio in Life Sciences: https://rstudio.com/solutions/pharma/ Learn more about RStudio’s recommended professional data science solution for every team: https://rstudio.com/products/team/ Contact our team with any questions: life-sciences-healthcare@rstudio.com Presenter: Alex Gold 2020-11-13 16:44:18 Online Online 0 Creating Reproducible Data Science
91
Description
Register The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify ...Read More
Register The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify critical questions that will guide future research directions. We are inviting current thought leaders in the field from the U.S. and Europe in order to promote intellectual discourse among colleagues on this subject. We anticipate a highly stimulating and interactive meeting and a published report to follow. Sessions Include: • Enhancer genetics. • Enhancers in development. • Enhancer biophysics. • Enhancer in disease. • Enhancers and ncRNA. Registration is required in order to receive the WebEx link. REASONABLE ACCOMMODATION Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Romi Sawhney, at 240-760-6400, and/or the Federal Relay Service (1-800-877-8339). Requests should be made at least two business days in advance of the event.
Details
When
Mon, Nov 16, 2020 - 8:50 am - 6:00 pm
Where
Online
Register The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify critical questions that will guide future research directions. We are inviting current thought leaders in the field from the U.S. and Europe in order to promote intellectual discourse among colleagues on this subject. We anticipate a highly stimulating and interactive meeting and a published report to follow. Sessions Include: • Enhancer genetics. • Enhancers in development. • Enhancer biophysics. • Enhancer in disease. • Enhancers and ncRNA. Registration is required in order to receive the WebEx link. REASONABLE ACCOMMODATION Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Romi Sawhney, at 240-760-6400, and/or the Federal Relay Service (1-800-877-8339). Requests should be made at least two business days in advance of the event. 2020-11-16 08:50:00 Online Online 0 Enhancers, Gene Regulation and Genome Organization meeting.
138
Description
Details
Organizer
CDSL
When
Mon, Nov 16, 2020 - 10:00 am - 11:00 am
Where
Online
Register 2020-11-16 10:00:00 Online CDSL 0 Spectral clustering and its connection to Laplacian Eigenmaps
215
Description
Register Cancer heterogeneity is one of the major challenges that hampers the ability to cure the disease. Tumors differ in their genetic profiles and the cellular interactions in the microenvironment, and each tumor may have multiple different clones with distinct molecular characteristics. Therefore understanding cancer heterogeneity has major translational implications. In my lab we use mass spectrometry-based proteomics to understand cancer heterogeneity in breast cancer and melanoma. ...Read More
Register Cancer heterogeneity is one of the major challenges that hampers the ability to cure the disease. Tumors differ in their genetic profiles and the cellular interactions in the microenvironment, and each tumor may have multiple different clones with distinct molecular characteristics. Therefore understanding cancer heterogeneity has major translational implications. In my lab we use mass spectrometry-based proteomics to understand cancer heterogeneity in breast cancer and melanoma. We combine analysis of clinical samples with histopathological analysis and functional validations, to unravel novel regulators of cancer progression. Analysis of hundreds of breast cancer tumor regions associated between clinical parameters and the protein networks, and showed their heterogeneity within single tumors. Our research showed the importance of each clinical feature and the significance of the immune system in affecting tumor heterogeneity. We also showed the microenvironment effects on melanoma, in the context of immunotherapy response. These unsupervised proteomic networks set the basis for future improved therapy and precision oncology. Presenter: Dr. Tami Geiger, from the Sackler Faculty of Medicine, Tel Aviv University, Israel. Meeting ID: 916 3499 0819 One tap mobile +13017158592,,91634990819# US (Washington D.C) +19294362866,,91634990819# US (New York) Dial by your location +1 301 715 8592 US (Washington D.C) +1 929 436 2866 US (New York) +1 312 626 6799 US (Chicago) +1 346 248 7799 US (Houston) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma) Meeting ID: 916 3499 0819 Find your local number: https://umd.zoom.us/zoomconference?m=O4DLGIb-r68F6tV586-lZPIBn4AGVlpe&_x_zm_rtaid=XmaBsPGmTeufOfL1u9ZYCg.1605297715617.1ba59dbec70972ce94cb94737a2e8ad5&_x_zm_rhtaid=242 Join by H.323 162.255.37.11 (US West) 162.255.36.11 (US East) 115.114.131.7 (India Mumbai) 115.114.115.7 (India Hyderabad) 213.19.144.110 (Amsterdam Netherlands) 213.244.140.110 (Germany) 103.122.166.55 (Australia) 149.137.40.110 (Singapore) 64.211.144.160 (Brazil) 69.174.57.160 (Canada) 207.226.132.110 (Japan) Meeting ID: 916 3499 0819
Details
When
Mon, Nov 16, 2020 - 11:00 am - 12:00 pm
Where
Online
Register Cancer heterogeneity is one of the major challenges that hampers the ability to cure the disease. Tumors differ in their genetic profiles and the cellular interactions in the microenvironment, and each tumor may have multiple different clones with distinct molecular characteristics. Therefore understanding cancer heterogeneity has major translational implications. In my lab we use mass spectrometry-based proteomics to understand cancer heterogeneity in breast cancer and melanoma. We combine analysis of clinical samples with histopathological analysis and functional validations, to unravel novel regulators of cancer progression. Analysis of hundreds of breast cancer tumor regions associated between clinical parameters and the protein networks, and showed their heterogeneity within single tumors. Our research showed the importance of each clinical feature and the significance of the immune system in affecting tumor heterogeneity. We also showed the microenvironment effects on melanoma, in the context of immunotherapy response. These unsupervised proteomic networks set the basis for future improved therapy and precision oncology. Presenter: Dr. Tami Geiger, from the Sackler Faculty of Medicine, Tel Aviv University, Israel. Meeting ID: 916 3499 0819 One tap mobile +13017158592,,91634990819# US (Washington D.C) +19294362866,,91634990819# US (New York) Dial by your location +1 301 715 8592 US (Washington D.C) +1 929 436 2866 US (New York) +1 312 626 6799 US (Chicago) +1 346 248 7799 US (Houston) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma) Meeting ID: 916 3499 0819 Find your local number: https://umd.zoom.us/zoomconference?m=O4DLGIb-r68F6tV586-lZPIBn4AGVlpe&_x_zm_rtaid=XmaBsPGmTeufOfL1u9ZYCg.1605297715617.1ba59dbec70972ce94cb94737a2e8ad5&_x_zm_rhtaid=242 Join by H.323 162.255.37.11 (US West) 162.255.36.11 (US East) 115.114.131.7 (India Mumbai) 115.114.115.7 (India Hyderabad) 213.19.144.110 (Amsterdam Netherlands) 213.244.140.110 (Germany) 103.122.166.55 (Australia) 149.137.40.110 (Singapore) 64.211.144.160 (Brazil) 69.174.57.160 (Canada) 207.226.132.110 (Japan) Meeting ID: 916 3499 0819 2020-11-16 11:00:00 Online Online 0 Proteomic analysis of cancer internal heterogeneity
191
Description
Register Learn how to find data standards through DCC Disciplinary Metadata, ...Read More
Register Learn how to find data standards through DCC Disciplinary Metadata, FAIRsharing.org, and NIH Common Data Elements (CDEs). For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov Check Research Data Resources page on the Scientific Library website to learn more about research data online resources.
Details
Organizer
Scientific Library at Frederick
When
Mon, Nov 16, 2020 - 1:00 pm - 1:20 pm
Where
Online
Register Learn how to find data standards through DCC Disciplinary Metadata, FAIRsharing.org, and NIH Common Data Elements (CDEs). For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov Check Research Data Resources page on the Scientific Library website to learn more about research data online resources. 2020-11-16 13:00:00 Online Online Scientific Library at Frederick 0 Finding Data Standards
92
Description
Register The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify ...Read More
Register The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify critical questions that will guide future research directions. We are inviting current thought leaders in the field from the U.S. and Europe in order to promote intellectual discourse among colleagues on this subject. We anticipate a highly stimulating and interactive meeting and a published report to follow. Sessions Include: • Enhancer genetics. • Enhancers in development. • Enhancer biophysics. • Enhancer in disease. • Enhancers and ncRNA. Registration is required in order to receive the WebEx link. REASONABLE ACCOMMODATION Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Romi Sawhney, at 240-760-6400, and/or the Federal Relay Service (1-800-877-8339). Requests should be made at least two business days in advance of the event.
Details
When
Tue, Nov 17, 2020 - 9:00 am - 3:15 pm
Where
Online
Register The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify critical questions that will guide future research directions. We are inviting current thought leaders in the field from the U.S. and Europe in order to promote intellectual discourse among colleagues on this subject. We anticipate a highly stimulating and interactive meeting and a published report to follow. Sessions Include: • Enhancer genetics. • Enhancers in development. • Enhancer biophysics. • Enhancer in disease. • Enhancers and ncRNA. Registration is required in order to receive the WebEx link. REASONABLE ACCOMMODATION Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Romi Sawhney, at 240-760-6400, and/or the Federal Relay Service (1-800-877-8339). Requests should be made at least two business days in advance of the event. 2020-11-17 09:00:00 Online Online 0 Enhancers, Gene Regulation and Genome Organization
208
Description
Register The goal of ...Read More
Register The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule
Details
When
Tue, Nov 17, 2020 - 11:00 am - 1:00 pm
Where
Online
Register The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule 2020-11-17 11:00:00 Online Online 0 Software Carpentry: R for Reproducible Scientific Analysis
218
Description
Please join us for a brief overview of NCI’s HALO cloud deployment and the first in a series of talks describing HALO-enabled research. Dr. Noemi Kedei, MD, CCR, will cover examples of using HALO to analyze highly multiplex CODEX images, and images acquired for DSP GeoMx and RNAScope. NCI's HALO is an enterprise-wide 2D image management and analysis platform, hosted in NCI’s AWS Cloud One environment. The HALO software supports cell-based, object-based, and ...Read More
Please join us for a brief overview of NCI’s HALO cloud deployment and the first in a series of talks describing HALO-enabled research. Dr. Noemi Kedei, MD, CCR, will cover examples of using HALO to analyze highly multiplex CODEX images, and images acquired for DSP GeoMx and RNAScope. NCI's HALO is an enterprise-wide 2D image management and analysis platform, hosted in NCI’s AWS Cloud One environment. The HALO software supports cell-based, object-based, and area-based analysis of brightfield and fluorescence images for research and clinical digital pathology, as well as numerous multiplex and hi-plex applications. The platform is an outgrowth of CBIIT’s Intramural NCI STRIDES-based Transition and Exploration Program (IN STEP), aimed at developing broadly useful research infrastructure in the cloud to support NCI’s Intramural Research Program (IRP). NCI's HALO eliminates the need for numerous groups to maintain separate but similar infrastructure for digital pathology (including contracts and computational workstations), and enables interoperability, collaboration, access to elastic computation, and efficient use of shared resources. NCI HALO supports collaborations with Investigators from other ICs, and there is capacity available for other ICs to demo the platform.
Details
When
Tue, Nov 17, 2020 - 1:00 pm - 2:00 pm
Where
Online
Please join us for a brief overview of NCI’s HALO cloud deployment and the first in a series of talks describing HALO-enabled research. Dr. Noemi Kedei, MD, CCR, will cover examples of using HALO to analyze highly multiplex CODEX images, and images acquired for DSP GeoMx and RNAScope. NCI's HALO is an enterprise-wide 2D image management and analysis platform, hosted in NCI’s AWS Cloud One environment. The HALO software supports cell-based, object-based, and area-based analysis of brightfield and fluorescence images for research and clinical digital pathology, as well as numerous multiplex and hi-plex applications. The platform is an outgrowth of CBIIT’s Intramural NCI STRIDES-based Transition and Exploration Program (IN STEP), aimed at developing broadly useful research infrastructure in the cloud to support NCI’s Intramural Research Program (IRP). NCI's HALO eliminates the need for numerous groups to maintain separate but similar infrastructure for digital pathology (including contracts and computational workstations), and enables interoperability, collaboration, access to elastic computation, and efficient use of shared resources. NCI HALO supports collaborations with Investigators from other ICs, and there is capacity available for other ICs to demo the platform. 2020-11-17 13:00:00 Online Online 0 Analysis of CODEX and other Hi-Plex Images in HALO
201
Description
https://www.youtube.com/watch?v=uoEhqeB4HTo&feature=youtu.be This introductory course teaches the basics of deep learning and of different types of deep learning networks through a set of hands-on biological examples implemented in Keras, one example per class. Class #5 will focus on the Deep Reinforcement Learning Networks and their application to small drug molecules design. Expected knowledge: ...Read More
https://www.youtube.com/watch?v=uoEhqeB4HTo&feature=youtu.be This introductory course teaches the basics of deep learning and of different types of deep learning networks through a set of hands-on biological examples implemented in Keras, one example per class. Class #5 will focus on the Deep Reinforcement Learning Networks and their application to small drug molecules design. Expected knowledge: Basic Python, Basic Linux/Unix This class is part of a series, but each class is stand-alone. Instructor: Gennady Denisov (NIH HPC staff) The class is free.
Details
Organizer
HPC Biowulf
When
Wed, Nov 18, 2020 - 9:30 am - 12:00 pm
Where
Online
https://www.youtube.com/watch?v=uoEhqeB4HTo&feature=youtu.be This introductory course teaches the basics of deep learning and of different types of deep learning networks through a set of hands-on biological examples implemented in Keras, one example per class. Class #5 will focus on the Deep Reinforcement Learning Networks and their application to small drug molecules design. Expected knowledge: Basic Python, Basic Linux/Unix This class is part of a series, but each class is stand-alone. Instructor: Gennady Denisov (NIH HPC staff) The class is free. 2020-11-18 09:30:00 Online Online HPC Biowulf 0 Deep Learning by Example on Biowulf
71
Description
Register What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study? This is Part 1 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as ...Read More
Register What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study? This is Part 1 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 1 will address the frequentist approach, and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 2 of this class series. Instructor: Paul Wakim (External Vendor)
Details
Organizer
NIH Training Library
When
Wed, Nov 18, 2020 - 10:00 am - 11:30 am
Where
Online
Register What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study? This is Part 1 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 1 will address the frequentist approach, and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 2 of this class series. Instructor: Paul Wakim (External Vendor) 2020-11-18 10:00:00 Online Online NIH Training Library 0 Statistical Inference for Non-Statisticians: Part 1
72
Description
Artificial Intelligence (AI) and Machine Learning (ML) are hot topics today, but are already part of our everyday lives. This class will introduce the basic principles of AI and ML and the current state of this field. The instructor will share examples of AI used in common applications and health research. AI limitations and ethical considerations will also be discussed. This course is designed for individuals new to the topic or for those who need ...Read More
Artificial Intelligence (AI) and Machine Learning (ML) are hot topics today, but are already part of our everyday lives. This class will introduce the basic principles of AI and ML and the current state of this field. The instructor will share examples of AI used in common applications and health research. AI limitations and ethical considerations will also be discussed. This course is designed for individuals new to the topic or for those who need a refresher. Register Speaker: Alicia Lillich (NIH STAFF)
Details
Organizer
NIH Training Library
When
Wed, Nov 18, 2020 - 11:00 am - 12:00 pm
Where
Online
Artificial Intelligence (AI) and Machine Learning (ML) are hot topics today, but are already part of our everyday lives. This class will introduce the basic principles of AI and ML and the current state of this field. The instructor will share examples of AI used in common applications and health research. AI limitations and ethical considerations will also be discussed. This course is designed for individuals new to the topic or for those who need a refresher. Register Speaker: Alicia Lillich (NIH STAFF) 2020-11-18 11:00:00 Online Online NIH Training Library 0 Introduction to Artificial Intelligence and Machine Learning
202
Description
Register Presenter: Dr. James Zou
Details
Organizer
CBIIT
When
Wed, Nov 18, 2020 - 11:00 am - 12:00 pm
Where
Online
Register Presenter: Dr. James Zou 2020-11-18 11:00:00 Online Online CBIIT 0 Computer Vision to Deeply Phenotype Human Diseases Across Physiological, Tissue, and Molecular Scales
212
Description
Register RStudio will be planning quarterly webinars to support the HHS Data Science and Statistical Programming communities. The first installment will be focused on Creating Reproducible Data Science. Please join Alex Gold as he shares best practices and demonstrates project-based workflow, version control with git, creating templates and packages, and reproducing data science environments. Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using ...Read More
Register RStudio will be planning quarterly webinars to support the HHS Data Science and Statistical Programming communities. The first installment will be focused on Creating Reproducible Data Science. Please join Alex Gold as he shares best practices and demonstrates project-based workflow, version control with git, creating templates and packages, and reproducing data science environments. Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting. For more information about RStudio in Life Sciences: https://rstudio.com/solutions/pharma/ Learn more about RStudio’s recommended professional data science solution for every team: https://rstudio.com/products/team/ Contact our team with any questions: life-sciences-healthcare@rstudio.com
Details
When
Wed, Nov 18, 2020 - 2:00 pm - 3:00 pm
Where
Online
Register RStudio will be planning quarterly webinars to support the HHS Data Science and Statistical Programming communities. The first installment will be focused on Creating Reproducible Data Science. Please join Alex Gold as he shares best practices and demonstrates project-based workflow, version control with git, creating templates and packages, and reproducing data science environments. Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting. For more information about RStudio in Life Sciences: https://rstudio.com/solutions/pharma/ Learn more about RStudio’s recommended professional data science solution for every team: https://rstudio.com/products/team/ Contact our team with any questions: life-sciences-healthcare@rstudio.com 2020-11-18 14:00:00 Online Online 0 Creating Reproducible Data Science
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Description
Register What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study? This is Part 2 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as ...Read More
Register What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study? This is Part 2 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 2 will address the Bayesian approach, and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 1 of this class series. Speaker: Paul Wakim (External Vendor)
Details
Organizer
NIH Training Library
When
Thu, Nov 19, 2020 - 10:00 am - 11:30 am
Where
Online
Register What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study? This is Part 2 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 2 will address the Bayesian approach, and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 1 of this class series. Speaker: Paul Wakim (External Vendor) 2020-11-19 10:00:00 Online Online NIH Training Library 0 Statistical Inference for Non-Statisticians: Part 2
946
Description