Abstract
Analysis of microarray, SNPs, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high-fidelity annotated knowledge base of protein interactions, pathways, and functional ontologies. This knowledge base has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here we present MetaDiscovery, an integrated platform for functional data analysis which is being developed at GeneGo for the past 8 years. On the content side, MetaDiscovery encompasses a comprehensive database of protein interactions of different types, pathways, network models and 10 functional ontologies covering human, mouse, and rat proteins. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical “interactome” tool for identification of over- and under-connected proteins in the data set, and a network module made up of network generation algorithms and filters. The suite also features MetaSearch, an application for combinatorial search of the database content, as well as a Java-based tool called MapEditor for drawing and editing custom pathway maps. Applications of MetaDiscovery include identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds, and clinical applications (analysis of large cohorts of patients and translational and personalized medicine).
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References
Salwinski L, Eisenberg D. Computational methods of analysis of protein–protein interactions. Curr Opin Struct Biol, 2003, 13, 377–382
Kemmeren P, et al. Protein interaction verification and functional annotation by integrated analysis of genome-scale data. Mol Cell, 2002, 9, 1133–1143
Subramaniam A, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 2005, 102(43),15545–15550
Klebanov L, Glazko G, Salzman P, Yakovlev A, Xiao Y. A multivariate extension of the gene set enrichment analysis. J Bioinform Comput Biol, 2007, 5(5), 1139–1153
Yook SH, Oltvai ZN, Barabási AL. Functional and topological characterization of protein interaction networks. Proteomics, 2004, 4(4), 928–942
Barabasi AL, Oltvai Z. Network biology: understanding the cell’s functional organization. Nat Rev Gen, 2004, 5, 101
Bader SKS, Gavin AC. Interaction networks for systems biology. FEBS Lett, 2008, 9, 582(8), 1220–1224.
Yip KY, Yu H, Kim PM, Schultz M, Gerstein M. The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks. Bioinformatics, 2006, 22(23), 2968–2970
Yu H, Zhu X, Greenbaum D, Karro J, Gerstein M. ToPNet–an application for interactive analysis of expression data and biological networks. Bioinformatics, 2004, 20(9), 1470–1471
Broder A, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, Stata R, Tomkins A, Wiener J. Graph structure in the web. Computer Networks, 2000, 33, 309
Wood LA, et al. The genomic landscapes of human breast and colorectal cancers. Science, 2007, 16, 318, 1108–1113
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003, 13(11), 2498–2504
Galperin MY, Cochrane GR. Nucleic acids research annual database issue and the NAR online molecular biology database collection in 2009. Nucleic Acids Res, 2009 Jan, 37 (Database issue), D1-4. Epub 2008 Nov 25.
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Nikolsky, Y., Kirillov, E., Zuev, R., Rakhmatulin, E., Nikolskaya, T. (2009). Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated “Knowledge-Based” Platform. In: Nikolsky, Y., Bryant, J. (eds) Protein Networks and Pathway Analysis. Methods in Molecular Biology, vol 563. Humana Press. https://doi.org/10.1007/978-1-60761-175-2_10
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DOI: https://doi.org/10.1007/978-1-60761-175-2_10
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