Pathway and Network Analysis of -omics Data (2017)

Course Objectives

A poster announcing this workshop can be found here

The CBW has developed a 2.5-day course covering the bioinformatics concepts and tools available for interpreting a gene list using pathway and network information. The workshop focuses on the principles and concepts required for analyzing and conducting pathway and network analysis on a gene list from any organism, although focus will be on human and model eukaryotic organisms.

Participants will gain practical experience and skills to be able to:

  • Get more information about a gene list;
  • Discover what pathways are enriched in a gene list (and use it for hypothesis generation);
  • Find out how a set of genes is connected by e.g. protein interactions and identify pathways, systems and modules within this network;
  • Predict gene function and extend a gene list;
  • Identify master regulators, such as transcription factors, active in the experiment.

We will develop a unified analysis flow chart throughout the course that students will be able to follow after the workshop to conduct their own analysis.

Target Audience

This workshop is intended for biologists working with 'Omics data' (e.g. gene expression, protein expression, molecular interactions, large-scale genetic screens and other omics data) from human and model eukaryotic organisms who are interested in interpreting large gene lists resulting from their experiments. Concepts will be applicable to omics data from non-eukaryotic organisms, but software and demonstrations will not cover them.

Prerequisite: Your own laptop computer. Minimum requirements: 1024x768 screen resolution, 1.5GHz CPU, 1GB RAM, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements). If you do not have access to your own computer, you may loan one from the CBW. Please contact for more information.

Course Outline

Day 1

Module 1: Introduction to Pathway and Network Analysis (2017) (Instructor: Gary Bader)

  • Where do gene lists come from and what are they useful for?
  • Pathway and network analysis overview
  • Presenting a workflow of concepts and tools from gene list to pathway analysis
  • Provide examples of multiple paths through the workflow that will be covered in the workshop
  • Sources of pathway and network information: GO biological process, network databases, pathway databases. Examples of pros and cons of each type of information
  • General issues: gene identifiers, data normalization

Module 2: Finding Over-represented Pathways in Gene Lists (2017) (Instructor: Quaid MorrisInstructor: Veronique Voisin)

  • Statistics for detecting over-representation e.g. hypergeometric test, GSEA
  • Multiple testing correction: Bonferroni, Benjamini-Hochberg FDR
  • Filtering Gene Ontology e.g. using evidence codes

Lab Practical: Performing over-representation analysis

  • Workflow of tools and steps
  • g:Profiler tool for over-representation analysis
  • Gene Set Enrichment Analysis (GSEA) and Enrichment Maps software tool
  • Running gene enrichment tools on your gene list

Module 3: Network Visualization and Analysis with Cytoscape (2017) (Instructor: Gary BaderInstructor: Veronique Voisin)

  • Introduction to Cytoscape
  • Cytoscape demo

Lab Practical: Tutorials on Cytoscape

  • Layouts
  • Labels
  • Enrichment maps

Integrated Assignment Part #1: Enrichment analysis using GSEA and g:Profiler (Veronique Voisin)

Day 2

Module 4: More depth on Pathway and Network Analysis (2017) (Instructor: Lincoln SteinInstructor: Robin Haw)

  • Basic network concepts
  • Types of pathway and network information
  • Network and pathway databases
  • More examples of pathway and network analysis methods
  • Reactome analysis tools: network clustering and paradigm

Lab Practical: Reactome

  • Workflow of tools and steps
  • Reactome FI

Module 5: Gene Function Prediction (2017) (Instructor: Quaid MorrisInstructor: Veronique Voisin)

  • Functional association networks and gene function prediction
  • Functional relationships, similarity space
  • Guilt-by-association concept
  • GeneMANIA and STRING tools

Lab Practical:

  • Workflow of tools and steps
  • Using GeneMANIA to assess gene and gene list function

Evening Integrated Assignment Part #2: Reactome FI and GeneMANIA

Day 3

Module 6: Regulatory Network Analysis (2017) (Instructor: Michael HoffmanInstructor: Veronique Voisin)

  • Overview of transcription and transcriptional regulation
  • Data sources for regulatory data - ChIP-seq, DNAse-seq, methylation data
  • Using epigenomics data
  • Finding transcription factor binding sites

Lab Practical: Using iRegulon

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