ncibtep@nih.gov

Bioinformatics Training and Education Program

Classes & Events

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 Programming 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

Details
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

Details
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:

 

Details
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
       

 

Details
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
Details
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

 

Details
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/
 
 

Details
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
Details
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:

 

Details
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.

Details
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

 

Details
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

Details
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.

Details
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:

 

Details
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
Details
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
 

Details
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

Details
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

 

 

Details
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.
 

Details
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

Details
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

Details
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

Details
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

 
Details
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
 

Details
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

Details
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
Details
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
 

 

Details
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

Details
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
Details
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
Details
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

Details
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

 

Details
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.

Details
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 (CIT),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,

 

Details
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 (CIT),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
Details
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
Details
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 (CIT) 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

 

Details
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

 

Details
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.
 

Details
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

 

 

Details
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
Details
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

Details
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
Details
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
Details
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

 

 

Details
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.

Details
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

Details
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 
Details
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

     

Details
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
Details
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

Details
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.)

 

 

Details
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]
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