Listing of past BTEP classes

https://hpc.nih.gov/training/handouts/200220_python_in_hpc.pdf https://xkcd.com/353/

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/

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

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

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

 

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
       

 

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

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

 

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/
 
 

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

 

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
•  

 

There will be no talk this week.

 
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

 

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

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.

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)

 

 
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

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
 

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

 
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

 

 

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.
 

 
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

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

 
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

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

 

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
 

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

 
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
 

 

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

 

• 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

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

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

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

 

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.

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,

 

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

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

 

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

 

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.
 

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

 

 

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

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

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"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

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.

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

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 

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

   

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"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

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

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.)

 

 

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.)

 

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

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

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

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

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

This session will include:

• A brief history of TCGA
• Overview of TCGA data portal
• TCGA data access policy and download
• Discussion of relevant TCGA publications

Day 1 PM (Thu, Jan 7) - 1:30 - 4:30 pm  Web-based exploration of TCGA data
(Parthav Jailwala, MS - CCBR, NCI)

1. In this session, after an introductory overview of graphical tools and features in FireBrowse, we will carryout a brief hands-on exercise of running different queries using the online FireBrowse portal.

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

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

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

 

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

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

• Access and integration of CNV, sequence variant and RNA-Seq expression TCGA data directly from Nexus.
• Visualization and statistical approaches for discovery.
• Sample stratification by clinical annotation factors or biomarkers.
• Finding CNVs predictive of survival or other outcome data.
• Generate publication-ready figures and charts during analysis.

Day 2 PM (Fri, Jan 8) - 1:30 - 4:30 pm  cBioPortal
(Nikolaus Schultz, PhD - Memorial Sloan-Kettering Cancer Center and Anand S. Merchant, MD, PhD. - CCBR, NCI)

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

• Introduction to cBioPortal - Niki Schultz
• OncoQueryLanguage (OQL) - Key words and Codes
• Features, Analytics, and Interpreting Results
• Tutorials/Hands-on Exercises - Anand S. Merchant

BTEP Workshop on Exome-Seq Data Analysis and Variant Annotation (2-day) This workshop will cover the basics and best practices of exome-seq analysis including downstream interpretation of variants using a variety of in-house, open-source and commercial web tools (CCBR Exome-Seq Pipeliner, AVIA, Ingenuity Variant Analysis, and CRAVAT/MuPit). Please note that this workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that location. Dates: March 8-9, 2016 (Tuesday and Wednesday) Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm (both days) Venues: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 6 Remote Simulcast – Scientific Library Training Room, Bldg 549, NCI-Frederick

For more information on the Frederick simulcast, please contact:

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

WORKSHOP AGENDA

Day 1 - Tuesday, March 8, 2016

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

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

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

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

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

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

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

12:30 - 1:30 pm LUNCH BREAK

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

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

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

• Hue Vuong, PhD: Introduction and Tutorial on AVIA

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

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

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

Day 2 - Wednesday, March 9, 2016

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

 Devendra (Dev) Mistry, Field Application Specialist

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

12:30 - 1:30 pm LUNCH BREAK

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

Michael Ryan, PhD

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

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

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

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

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

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

Venues:

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

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

For more information on the Frederick simulcast, please contact:

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

WORKSHOP AGENDA

Monday, April 4, 2016

Day 1 -   9:30 am - 12:30 pm

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

Link to Talk Slides on SlideShare

Day 1 -  1:30-4:30 pm

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

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

 

Tuesday, April 5, 2016

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

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

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

 

Day 2 - 1:30-4:30 pm

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

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

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

Objectives:

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

·       Flow

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

·       PGS

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

Files required for Partek are available under Course Materials below.

Partek Genomics Suite with Pathway Training License Installation instructions

For Mac Laptop:

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

For Windows Laptop:

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

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

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq Data Analysis. There will also be a talk on ENCODE and a comprehensive discussion of topics ranging from experimental design to data visualization. This workshop will include presentations on using open source applications (MACS, HOMER, MEME, SICER) as well as commercial software (Genomatix).  To access the NCI Genomatix server, to request for a password through IT helpdesk to genomeanalyzer.nci.nih.gov. NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. This workshop will be remotely telecast to the Library Training Room in Bldg 549 at NCI-Frederick for attendees who select to register at that location. Please register only if you intend to attend the workshop. Date: May 16-17, 2016 (Monday and Tuesday) Time: 9:30 am – 12:30 pm and 1:30 – 4:30 pm Locations: Live Workshop - NIH Bethesda - Bldg 10, FAES Room 4 Remote Simulcast - NCI-Frederick - Scientific Library Training Room, Bldg 549 For more information on the Frederick simulcast, please contact:   Tracie Frederick, Technology Informationist, Scientific Library NCI at Frederick Phone: 301-846-1094 Email: frederickt@mail.nih.gov  

Workshop Agenda

Monday, May 16, 2016

Day 1 - Session 1: 9:30 am – 12:30 pm

9:30 am – 11 am

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

Title:  Introduction to ChIP-Seq

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

 

11 am – 12:30 pm

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

Title: Understanding ENCODE (ENCyclopedia Of DNA Elements)

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

Day 1 – Session 2

1:30 pm – 4:30 pm

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

Title: ChIP-Seq Analysis Workflow

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

Tuesday, May 17, 2016

Day 2 – Session 1

9:30 am –12:30 pm

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

                     Weiqun Peng, Ph.D., George Washington University

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

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

Day 2 – Session 2

1:30 pm – 4:30 pm

Presenter:  Susan Dombrowski, Ph.D, Genomatix

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

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

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

Software requirements for the computer system to run Genomatix:

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

 

The 'NCBI Resources for CCR Scientists' workshop, on June 6 and June 8, 2016, will focus on teaching a wide range of resources developed within NCBI that are relevant for CCR scientists. It will include both hands-on and lecture components. Attending the workshop will enable one to understand the tools and databases available at NCBI and how to best use them for your research. A team of experts from NCBI will be presenting these sessions over two separate days. NOTE: No sessions on June 7. This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form. Please register only if you intend to attend the workshop. Locations: Live Workshop - NIH Bethesda - Bldg 10

• FAES Room 4 (June 6th) and
• FAES Room 3 (June 8th)

Remote Simulcast - NCI-Frederick - Scientific Library Training Room, Bldg 549 For more information on the Frederick simulcast, please contact:   Tracie Frederick, Technology Informationist, Scientific Library NCI at Frederick Phone: 301-846-1094 Email: frederickt@mail.nih.gov

WORKSHOP AGENDA

Day 1 - Monday, June 6, 2016

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

Instructor: Majda Valjavec-Gratian, Ph.D.

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

12:30 - 1:30 pm Lunch Break

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

Instructor: Peter Cooper, Ph.D.

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

Day 2 - Wednesday, June 8, 2016

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

Instructor: Peter Cooper, Ph.D

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

12:30 - 1:30 pm Lunch Break

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

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

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

 

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

Instructor: Ben Busby, Ph.D.

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

 

 

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

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

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

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

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

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

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

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

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

• Note for 64 bit Windows users – please download the *64* bit Java version, as Oracle still downloads the 32-bit version by default.
• The *64* bit version is at: http://javadl.oracle.com/webapps/download/AutoDL?BundleId=211999

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

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

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

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

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

Time and Location:

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

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

Workshop Agenda

Day 1: 9:30 am – 12:30 pm

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

Break

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

Lunch Break

Day 1: 1:30 pm – 4:30 pm

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

Break

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

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

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

 

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

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

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

Workshop Agenda

Day 1 – October 3, 2016 (Monday)

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

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

Introduction

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

Data Analysis

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

Statistical Analysis

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

11:15 – 11:30 am BREAK

Visualization and Clustering

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

Validation and Downstream Analysis

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

12:30 – 1:30 pm LUNCH BREAK

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

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

Lecture and Hands-on session for GEO2R

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

2:45 - 3:00 pm – BREAK

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

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

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

Day 2 – October 4, 2016 (Tuesday)

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

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

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

12:30 – 1:30 pm LUNCH BREAK      

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

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

 

ABOUT THE COURSE

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

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

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

R Installation

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

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

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

Bioconductor and Bioconductor Package Installation

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

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

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

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

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

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

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

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

Workshop Agenda

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

Introduction to R

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

12:30 - 1:00 pm   LUNCH BREAK

Afternoon Session - 1:00 - 4:00 pm

Introduction to Bioconductor

• Installing Bioconductor
• An Overview of Bioconductor Packages
• Fundamental Packages
  • Biobase: the Foundation
  • Biostrings: A Representation of Biological Sequences
  • BSgenome: A Representation of Complete Genomic Sequences
  • GenomicRanges: Manipulation of Genomic Intervals
  • GenomicFeatures: Manipulation of Genomic Features
  • GenomicAlgnments: Manipulation of Short Genomic Alignments
• Two Fundamental Structures to Contain Experiment Data
  • The ExpressionSet for Array Data
    • Constructing an ExpressionSet
    • Analyzing an ExpressionSet
  • The SummarizedExperiment for NGS Sequence Data
    • Constructing a SummarizedExperiment
    • Analyzing a SummarizedExperiment

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

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

• An overview of the tools available - their strength and weaknesses and where to find them
• An overview of the different classes of browsers
• An discussion of the relevant file types accepted by most browsers
• Details on how to navigate the UCSC Genome Browser
• How to integrate your own or publically available data into browsers
• How to capture and share specific views of data
• How to get more detailed views of your data with tools like IGB and IGV and how to integrate them into other tools
• and more....