Classes & Events

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