1. Five Slides About EGAN Jesse Paquette UCSF Helen Diller Family Comprehensive Cancer Center Jesse.Paquette@cc.ucsf.edu

2. The exploratory assay workflow The experienced biologist/statistician/bioinformatician is familiar with methods of getting from A to B; but generation of a computational result (commonly a gene list, or “signature”) at point B is not true biological discovery… The biologist needs additional information to progress from from B to C. A biologist often starts with this perspective of high- throughput technologies. Collect the data and discovery will naturally follow… Example queries –“How are these genes related?” –“How do the results compare to Our aCGH experiment? Our SNP GWA data? Results published by Soandso et al. (2008)?” –“Which genes have a p- value of < 0.05 across multiple experiments and are also S/T kinases?” –“Is there any literature that will help?”

3. 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

4. EGAN: Exploratory Gene Association Networks Familiar, spreadsheet-like tables SearchableSortable Links to web resources Graph-based visualization Enrichment statistics Customizable data Links to literature

5. Analysis of multiple experiments in EGAN 1) Select genes by spreadsheet-like tables or by dialog Low-power experiment. Relax the p-value cutoff to include more genes. 2) Show selected nodes on graph 3) Calculate enrichments and construct annotation hypergraph EGAN immediately identifies 6 pertinent articles (click edge to locate in PubMed) Exp.1 Exp.2 4) Follow links to literature and internet resources 5) Export to Excel-ready file and/or PDF 6) Repeat!

6. 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