Interpreting Gene Lists from -omics Studies

Workshop Details

Date: July 15-16, 2010
Location: Downtown Toronto, ON
Lead Faculty (2010): Gary Bader, Quaid Morris & Lincoln Stein
Registration Fee for Applications received before June 16, 2010: $500 + HST
Registration Fee for Applications received after June 16, 2010: $700 + HST
SOLD OUT

Target Audience
This workshop is geared towards 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 for a fee. Please contact course_info@bioinformatics.ca for more information.

Course Objectives
Many research programs often slow or stall after generating a gene list. The CBW has developed a 2-day course covering the bioinformatics concepts and tools available for annotating and determining functional enrichment of a gene list and analyzing networks. The workshop is focused on the principles and concepts required for analyzing and conducting pathway analysis on a gene list from any organism, although focus will be on human and model Eukaryotic organisms. Specifically, we will focus on 1) getting more information about a gene list, 2) finding out how a set of genes is connected, 3) discovering what's enriched in a gene list (and using it for hypothesis generation) and 4) extending or refining a gene list. An analysis flow chart will be developed throughout the course.

Course Outline

Day 1 - Gene Lists
Module 1: Introduction to gene lists (Chair: Gary Bader)

Ice breaking session for participants (promote networking)
Gene list analysis overview presenting a workflow of concepts and tools from gene list to pathway analysis
Where do gene lists come from?

- Common experimental methods that generate large gene lists (i.e. mRNA expression, protein expression (MudPIT), gene association studies, ChIP)

Working with gene function information

- Definition, sources (e.g. Gene Ontology) and issues (e.g. quality of annotation transfer, evidence codes, working with multi-functional genes)

Laboratory: Practical aspects of working with gene lists (Gary Bader)

Working with gene identifiers (IDs): Gene and protein IDs, ID mapping and translation, gene names, caveats and issues (gene name ambiguity, problems reaching 100% coverage due to version issues). Using tools such as BioMart and Synergizer. Finding information about genes on a list (e.g. BioMart).

Assignment: Annotate a list of genes with functional annotation from Gene Ontology, summarize annotation in a spreadsheet, convert gene IDs to Entrez Gene IDs.

Lunch

Module 2: Finding over-represented gene functions in gene lists (Chair: Quaid Morris)

Over-representation analysis (ORA)

- Use of DAVID tool

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
Advanced topics: orthology mapping, non model organisms

Laboratory (Daniele Merico)

Gene Set Enrichment Analysis (GSEA) and Enrichment Maps
DAVID website tool for over-representation analysis
GSEA software tool
Assignment:Running software tools on your gene list

Dinner

Open Lab Time

Laboratory assignment & worked examples using your own data. Teaching assistants will be available to answer questions and consult.

Day 2 - Networks

Module 3: Pathway and network analysis of gene lists (Chair: Lincoln Stein)

Introduction to pathway and network analysis
Basic network concepts
Types of pathway and network information

- Focus on protein interaction networks and pathways. Mention of gene regulatory networks.

Pathway Databases: Reactome
Pathway analysis of large scale genomics data sets, including cancer genomics
Introduction to network visualization (Gary)

- Visualizing omics data on a network or pathway

Advanced topic: Active modules in Cytoscape, application to breast cancer classification

Laboratory (Gary Bader)

Demo: Cytoscape (Gary)
Demo: Reactome - viewing genes in a pathway context (Lincoln)

Lecture & Laboratory: Gene Function Prediction (Quaid Morris)

Functional association networks and gene function prediction

- Functional relationships, similarity space

- Homology based prediction

- Guilt-by-association: GeneMANIA

Using GeneMania

Conclusions

Pre-Readings
Module 1:
- Potential pitfalls of working with large lists of gene identifiers: http://www.ncbi.nlm.nih.gov/pubmed/15214961

Module 2:
- Predicting gene function: http://www.ncbi.nlm.nih.gov/pubmed/17167517
- A race through the maze of genomic evidence: http://www.ncbi.nlm.nih.gov/pubmed/18613945
- A probabilistic view of gene function - http://www.ncbi.nlm.nih.gov/pubmed/15167932
-Use and misuse of the gene ontology annotations: http://www.ncbi.nlm.nih.gov/pubmed/18475267

Module 3:
- GeneMANIA: http://www.ncbi.nlm.nih.gov/pubmed/20576703
- How to visually interpret biological data using networks - http://www.ncbi.nlm.nih.gov/pubmed/19816451
- Integration of biological networks and gene expression data using Cytoscape - http://www.ncbi.nlm.nih.gov/pubmed/17947979
- Pathway Information for Systems Biology - http://www.ncbi.nlm.nih.gov/pubmed/15763557
- Reactome - http://genomebiology.com/2010/11/5/R53 (HTML) or http://genomebiology.com/content/pdf/gb-2010-11-5-r53.pdf (PDF)