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Interpreting Gene Lists from -omics Studies

Workshop Details

Date: July 15-16, 2010
Location: Downtown Toronto, ON
Lead Faculty (2010): Gary Bader & Quaid Morris
Registration Fee for Applications received before June 14, 2010: $500 + GST
Registration Fee for Applications received after June 14, 2010: $700 + GST
Apply now!



Target Audience
This workshop is geared towards biologists working with 'Omics data' (e.g. gene expression, protein expression, molecular interactions) from Eukaryotic systems who are interested in interpreting large gene lists resulting from their experiments.

Prerequisite: A gene list + 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 2-3 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 tools available for annotating a gene list, predicting gene function and evaluating regulatory networks. Beginning with a gene list, participants will gain practical experience and skills as detailed in the course outline below. Register now as space is limited to 30 participants.


Course Outline
Each module contains approximately 1.5 hours lecture, 30 minute break and a lab.

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

  • Ice breaking session for participants (promote networking)
  • 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)
  • Finding information about genes on a list
    • - BioMart, Dragon, TIGR, IMAGEne, etc.
  • Definition of gene function
    • - Different aspects - Issues: annotation transfer and multi-functional genes - Gene Ontology and evidence codes

Break (30 minutes)

  • Practical aspects of working with gene lists (Lab)
    • - Working with gene identifiers: Gene and protein IDs, ID mapping and translation, gene names, caveats and issues (gene name ambiguity, problems reaching 100% coverage due to version issues)
  • Module Assignment: Annotate a list of genes with functional annotation from GO, 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)
    • - DAVID, GoMiner, BINGO
  • Gene Set Enrichment Analysis (GSEA)
  • Statistics e.g. hypergeometric test, Fisher's Exact Test, GSEA

Break (30 minutes)

  • Multiple testing correction: Bonferroni, Benjamini-Hochberg FDR
  • Filtering by GO evidence codes
  • Running software tools on your gene list (Lab)
  • Module Assignment

Dinner

Open Lab Time

  • Laboratory assignment & worked examples using your own data

Day 2

Module 3: Pathway and network analysis of gene lists (Chair: Gary Bader)

  • Basic network concepts (Nature Biotechnology Primer)
  • Where pathway and network information comes from
  • Cytoscape (Nature Protocols Cytoscape paper)
  • Active Modules
  • GenMAPP - viewing genes in a pathway context
  • Functional association networks and gene function prediction
    • - Functional relationships, similarity space - Homology based prediction - Guilt-by-association: STRING, BioPIXIE, GeneMANIA
  • Module Assignment: Run active modules on a set of yeast genes

Pre-Readings
* Module 1:
-BMC Bioinformatics paper: http://www.ncbi.nlm.nih.gov/pubmed/15214961
* Module 2:
-Nature Reviews Cancer paper: http://www.ncbi.nlm.nih.gov/pubmed/17167517
-Nature Reviews Genetics paper: http://www.ncbi.nlm.nih.gov/pubmed/18475267
-Genome Biology paper: http://www.ncbi.nlm.nih.gov/pubmed/18613945
* Module 3:
-Nature Protocols Cytoscape paper - http://www.ncbi.nlm.nih.gov/pubmed/17947979
-Pathway Information for Systems Biology - http://www.ncbi.nlm.nih.gov/pubmed/15763557