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Knowledge-based Analysis of Genome-scale Data

Knowledge-based Analysis of Genome-scale Data. How to Understand Gene Sets ?. Gene products function together in dynamic groups A key task is to understand why a set of gene products are grouped together in a condition, exploiting all existing knowledge about: The genes (all of them)

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Knowledge-based Analysis of Genome-scale Data

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  1. Knowledge-based Analysis ofGenome-scale Data

  2. How to Understand Gene Sets? • Gene products function together in dynamic groups • A key task is to understand why a set of gene products are grouped together in a condition, exploiting all existing knowledge about: • The genes (all of them) • Their relationships (|genes|2) • The condition(s) under study.

  3. 1,170 peer-reviewed gene-related databases in 2009 Nucleic Acids Research database issue

  4. Exponential growth in the biomedical literature 1,000 genomes project will create 1,400GB next year http://1000genomes.org

  5. How to stay ahead? • Have to take advantage of information gained in different disciplines • Relaxin 1 & βblockers • Originally characterized in 1926 as pregnancy related

  6. Gene-by-gene • Lots of gene centric information sources: • PubMed / GeneRIFs • Entrez Gene / UniProt • GeneCards • OMIM (with associated human phenotypes) • But these can be overwhelming even for a single gene, let alone for a list of hundreds. • Try scanning these for information about human PPARD, a moderately well-studied gene.

  7. Mapping to Pathways • Searching a pathway database (KEGG, Reactome, WikiPathways) with multiple genes • “Painting” expression dataonto staticpathways, e.g.GenMAPP

  8. Mapping to PPI networks • Greater coverage than pathways, but harder to interpret (e.g. GenePro Cytoscape plugin)

  9. Tools to find commonalities • GO term enrichment • Identifies annotations of all genes in a cluster that appear more often than expected for a random set of genes of the same size, e.g. Onto-Express • DAVID gene functional classification enrichment (GO, PIR, KEGG, Interpro, etc.)

  10. Gene Set Enrichment Analysis • Start with predefined sets of related genes, then test expression data for over-representation of each group • Not alwayseasy to definegood sets;chromosomalregions workwell in cancer

  11. 3R Approach • Integrated approach to creating knowledge-based resources and using them for analysis • Reading: Extracting information from the literature and curated databases • Reasoning: Integrating, extending, evaluating and aligning knowledge with data • Reporting: Interactive visualizations and queries that facilitate explanation and hypothesis generation

  12. Information integration • Peer-reviewed gene-centric databases contain: • Annotations to function, location, process, disease, etc. ontologies • Linkages to many sorts of experimental and derived data(GWAS, expression, structure, pathways, population frequencies) • Linkages to publications that report evidence relevant to them • Many can be integrated into a single, unified network using gene and/or publication identifiers. • Identifier cross-reference lists increasingly reliable • Increasing coordination and standardization among providers • Some challenges remain, e.g. what is a “gene”? • PRO might help, but not there yet.

  13. Reading • The best source of knowledge is the literature • OpenDMAP is significant progress in concept recognition in biomedical text • Even simple-minded approaches are powerful • Gene co-occurrence widely used • Thresholded co-occurrence fraction is better

  14. OpenDMAP extracts typed relations from the literature • Concept recognition tool • Connect ontological terms to literature instances • Built on Protégé knowledge representation system • Language patterns associated with concepts and slots • Patterns can contain text literals, other concepts, constraints (conceptual or syntactic), ordering information, or outputs of other processing. • Linked to many text analysis engines via UIMA • Best performance in BioCreative II IPS task • >500,000 instances of three predicates (with arguments) extracted from Medline Abstracts • [Hunter, et al., 2008] http://bionlp.sourceforge.net

  15. Reasoning in knowledge networks Ddc; MGI:94876 [Bada & Hunter, 2006] catechols(CHEBI:33566) catecholamines(CHEBI:33567) adrenaline (CHEBI:33568) noradrenaline(CHEBI:33569) Cadps; MGI:1350922 GO:0042423 GO:0050432 CHEBI:33567 MGI:94876 MGI:1350922 Reliability = 0.009740

  16. Inferred interactions • Dramatically increase coverage… • But at the cost of lower reliability • We apply new method toassess reliabilitywithout an explicit goldstandard • [Leach, et al., 2007;Gabow, et al., 2008] Top 1,000 Craniofacial genes(1,000,000 possible edges)

  17. 3R Knowledge Networks • Combine diverse sources… • Databases of interactions • Information extracted from the literature (CF or DMAP) • Inference of interactions • … Into a unified knowledge summary network: • Every link gets a reliability value • Combine multiple links for one pair into a single summary • More sources  more reliable • Better sources  more reliable • “Noisy Or” versus “Linear Opinion Pool” • Summaries allow for effective use of noisy inferences • [Leach PhD thesis 2007; Leach et al., 2007]

  18. Knowledge-based analysis of experimental data • High-throughput studies generate their own interaction networks tied to fiducials • E.g. Gene correlation coefficients in expression data • Combine with background knowledge by: • Averaging (highlights already known linkages) • Hanisch (ISMB 2002) method (emphasizes data linkages not yet well supported by the literature) • Report highest scoring data + knowledge linkages, color coding for scores of average, Hanisch or both.

  19. The Hanalyzer: 3R proof of concept • [Leach, Tipney, et al., PLoS Comp Bio 2009] http://hanalyzer.sourceforge.org See video demo by searching YouTube for “Hanalyzer” • Knowledge network built for mouse • NLP only CF and DMAP for three relationships from PubMed abstracts • Simple reasoning (co-annotation, including ontology cross-products) • Visualization of combined knowledge / data network via Cytoscape + new plugins

  20. First application: Craniofacial Development • NICHD-funded study (Rich Spritz; Trevor Williams) focused on cleft lip & palate • Well designed gene expression array experiment: • Craniofacial development in normal mice (control) • Three tissues (Maxillary prominence, Fronto-nasal prominence, Mandible) • Five time points (every 12 hours from E10.5) • Seven biological replicates per condition (well powered) • >1,000 genes differentially expressed among at least 2 of the 15 conditions (FDR<0.01)

  21. The Whole Network Craniofacial dataset, covering all genes on the Affy mouse chip. Graph of top 1000 edges using AVE or HANISCH (1734 in total). Edges identified by both. Focus on mid-size subnetwork

  22. Link calculations for MyoD1 MyoG Shared interprodomains: IPR:11598… Premod_M interaction: Mod074699 R = 0.0438 R = 0.1005 Inferred link through shared GO/ChEBI: ChEBI:16991 Shared GObiological processes: GO:6139… Co-occurrencein abstracts:PMID:16407395… DMAP transportrelation R = 0.01 R = 0.0172 Shared GOcell component: GO:5667… R = 0.1034 R = 0.0105 R = 0.0190 Shared GOmolecular functions: GO:3705… R = 0.0284 Shared knockoutphenotypes:MP:5374 … R = 0.018 Correlation inexpression data:Pdata = 0.4808

  23. Strong data and background knowledge facilitate explanations • Goal is abductive inference: why are these genes doing this? • Specifically, why the increase in mandible before the increase in maxilla, and not at all in the frontonasal prominence? AVE edges Both edges Skeletal muscle structural components Skeletal muscle contractile components Proteins of no common family

  24. Exploring the knowledge network See the YouTubeHanalyzer demo fora better sense of the process

  25. Scientist + aide + literature  explanation: tongue development AVE edges Both edges Skeletal muscle structural components Skeletal muscle contractile components Proteins of no common family The delayed onset, at E12.5, of the same group of proteins during mastication muscle development. Myoblast differentiation and proliferation continues until E15 at which point the tongue muscle is completely formed. Myogenic cells invade the tongue primodia ~E11

  26. HANISCH edges AVE edges Both edges inferred synapse signaling proteins Inferred myogenic proteins Proteins of no common family Proteins in the previous AVE based sub-network On to Discovery • Add the strong data, weak background knowledge (Hanisch) edges to the previous network, bringing in new genes. • Four of these genes not previously implicated in facial muscle development (1 almost completely unannotated)

  27. Biological validation Transverse, E12.5 Sagittal, E11.5 More rostral More caudal Apobec2 E430002G05Rik Hoxa2 Zim1

  28. Using ontologies for explanation:What is the role of CAV3 in muscle?

  29. Genes ~ Bad Guys?Pirolli & Card, Int’l Conf. on Intelligence Analysis, 2005

  30. Bio-Jigsaw Based on Stasko, et al.’s [2007] Jigsaw visual analytics system

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