1 / 6

Five Slides About EGAN

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

lani
Download Presentation

Five Slides About EGAN

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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?” The exploratory assay workflow 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… 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…

  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. • Downstreamof 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 Searchable Sortable Links to literature Links to web resources Enrichment statistics Familiar, spreadsheet-like tables Graph-based visualization Customizable data

  5. Analysis of multiple experiments in EGAN 3) Calculate enrichments and construct annotation hypergraph 1) Select genes by spreadsheet-like tables or by dialog Low-power experiment. Relax the p-value cutoff to include more genes. 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 2) Show selected nodes on graph 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

More Related