1 / 58

Divining Systems Biology Knowledge from High-throughput Experiments Using EGAN

Divining Systems Biology Knowledge from High-throughput Experiments Using EGAN. Jesse Paquette ISMB 2010 Biostatistics and Computational Biology Core Helen Diller Family Comprehensive Cancer Center University of California, San Francisco (AKA BCBC HDFCCC UCSF). High-throughput experiments.

vinson
Télécharger la présentation

Divining Systems Biology Knowledge from High-throughput Experiments Using 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. Divining Systems Biology Knowledge from High-throughput Experiments Using EGAN Jesse Paquette ISMB 2010 Biostatistics and Computational Biology Core Helen Diller Family Comprehensive Cancer Center University of California, San Francisco (AKA BCBC HDFCCC UCSF)

  2. High-throughput experiments • This talk applies to • Expression microarrays • aCGH • SNP/CNV arrays • MS/MS Proteomics • DNA methylation • ChIP-Seq • RNA-Seq • In-silico experiments • If parts of the output can be mapped to gene IDs • You can use EGAN

  3. What do you hope to accomplish? Collect data Process data Differential analysis Publish! Clustersand/orgene lists Produce insight about the underlying biology New papers! New testable hypotheses New grants! Drug targets!

  4. Leverage organic intelligence Clustersand/orgene lists Summarize Produce insight about the underlying biology Visualize Contextualize New testable hypotheses

  5. Producing insight from clusters and gene lists • Summarize: find enriched pathways (and other gene sets) • Hypergeometric over-representation • DAVID • Global trends • GSEA • Visualize: gene relationships in a graph • Protein-protein interactions • Cytoscape • Network module discovery • Ingenuity IPA • Literature co-occurrence • PubGene • Contextualize: pertinent literature • PubMed • Google • iHOP

  6. EGAN: Exploratory Gene Association Networks • Methods: state-of-the-art analysis of clusters and gene lists • Hypergeometric enrichment of gene sets • Global statistical trends of gene sets • Hypergraph visualization (via Cytoscape libraries) • Literature identification • Network module discovery • User Interface: responds quickly to new queries from the biologist • Sandbox-style functionality • Dynamic adjustment of p-value cutoffs • Point-and-click interface • All data in-memory for immediate access • Links to external websites • Modular: integrates as a flexible plug-and-play cog • All data is customizable • Proprietary data can be restricted to the client location • Java runs on almost every OS (PC, Mac, LINUX) • Can be configured and launched from a different application (e.g. GenePattern) • Analyses can be scripted for automation

  7. Gene sets • A gene set is a a set of semantically related genes • e.g. Wnt signaling pathway • EGAN contains a database of gene sets • > 100k gene sets by default • KEGG, Reactome, NCI-Nature, Gene Ontology, MeSH, Conserved Domain, Cytoband, miRNA targets • You can easily add your own • Simple file format • Download from MSigDB (Broad Institute)

  8. Gene-gene relationships • EGAN also contains • Protein-protein interactions (PPI) • Literature co-occurrence • Chromosomal adjacency • Kinase-target relationships • Other possibilities • Sequence homology • Expression correlation

  9. Example with microarray and aCGH results • Mirzoeva et al. (2009) Cancer Research • UCSF-LBL collaboration • Analysis of breast cancer cell lines • Basal vs. luminal • Discoveries in this presentation • miRNA regulator of subtype (mir-200) • Annexin (ANXA1) as potential regulator of ER, glucocorticoid and EGFR signaling

  10. Gene list - higher expression in basal cell lines

  11. Gene set/pathway enrichment

  12. Importing gene lists from publications

  13. Combining expression with aCGH

More Related