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Five Slides About
EGAN
Jesse Paquette
UCSF Helen Diller Family
Comprehensive Cancer Center
[email protected]
The exploratory assay workflow
The biologist needs
additional information to
progress from from B to C.
A biologist often starts with this perspective of highthroughput technologies. Collect the data and discovery
• Example queries
will naturally follow…
– “How are these genes
–
related?”
The experienced biologist/statistician/bioinformatician is
“How do the results compare
familiar with methods of getting from A to B; but generation
to
• Our aCGH experiment?
of a computational result (commonly a gene list, or
• Our SNP GWA data?
“signature”) at point B is not true biological discovery…
• Results published by
Soandso et al. (2008)?”
–
–
“Which genes have a pvalue of < 0.05 across
multiple experiments and
are also S/T kinases?”
“Is there any literature that
will help?”
EGAN: Exploratory Gene Association Networks
• Software that runs on a biologist’s computer
– No additional hardware/web server/database necessary
• Internal database of diverse knowledge about genes
– Data updates are automatically downloaded
– Easily customized with alternative/supplemental/proprietary data
• Provides a venue for integration of results from multiple
diverse *omics experiments
– Expression microarray, aCGH, SNP, MS/MS, etc.
– Downstream of statistical analysis/clustering
– Enrichment statistics
• Built to accelerate the progression from experiment result
to discovery
–
–
–
–
Leverage the organic intelligence of the biologist
Point-and-click interface
Spreadsheet and graph-based display of information
Guide the user to pertinent journal articles
EGAN: Exploratory Gene Association
Networks
Searchable
Sortable
Links to literature
Links to web resources
Enrichment statistics
Familiar, spreadsheet-like tables
Graph-based visualization
Customizable data
Analysis of multiple experiments in EGAN
1) Select genes by
spreadsheet-like tables
or by dialog
Exp.1
Low-power experiment. Relax
the p-value cutoff to include
more genes.
3) Calculate enrichments and
construct annotation hypergraph
EGAN immediately identifies 6
pertinent articles (click edge to
locate in PubMed)
Exp.2
4) Follow links to literature and internet
resources
5) Export to Excel-ready file and/or PDF
2) Show selected nodes on graph
6) Repeat!
EGAN adoption
• At UCSF
– Albertson Lab
– Cleaver Lab
– Giacomini Lab
– Gray Lab
– Hodgson Lab
– Kreutz Lab
– McCormick Lab
– McMahon Lab
– Olshen Lab
– Prostate SPORE
• EGAN manuscript is under review at Bioinformatics