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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