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PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis

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dc.contributor.author Ng, Sam
dc.contributor.author Collisson, Eric A.
dc.contributor.author Sokolov, Artem
dc.contributor.author Goldstein, Theodore
dc.contributor.author González-Pérez, Abel
dc.contributor.author López Bigas, Núria
dc.contributor.author Benz, Christopher
dc.contributor.author Haussler, David
dc.contributor.author Stuart, Joshua M.
dc.date.accessioned 2015-12-07T17:23:14Z
dc.date.available 2015-12-07T17:23:14Z
dc.date.issued 2012
dc.identifier.citation Ng S, Collisson EA, Sokolov A, Goldstein T, Gonzalez-Perez A, Lopez-Bigas N et al. PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis. Bioinformatics. 2012. 28(18): i640-i646. DOI 10.1093/bioinformatics/bts402
dc.identifier.issn 1367-4803
dc.identifier.uri http://hdl.handle.net/10230/25345
dc.description.abstract MOTIVATION: A current challenge in understanding cancer processes is to pinpoint which mutations influence the onset and progression of disease. Toward this goal, we describe a method called PARADIGM-SHIFT that can predict whether a mutational event is neutral, gain-or loss-of-function in a tumor sample. The method uses a belief-propagation algorithm to infer gene activity from gene expression and copy number data in the context of a set of pathway interactions. RESULTS: The method was found to be both sensitive and specific on a set of positive and negative controls for multiple cancers for which pathway information was available. Application to the Cancer Genome Atlas glioblastoma, ovarian and lung squamous cancer datasets revealed several novel mutations with predicted high impact including several genes mutated at low frequency suggesting the approach will be complementary to current approaches that rely on the prevalence of events to reach statistical significance. AVAILABILITY: All source code is available at the github repository http:github.org/paradigmshift. CONTACT: jstuart@soe.ucsc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Oxford University Press
dc.rights © 2012 Ng S et al. This is an Open Access article distributed under the terms of a Creative Commons Attribution License
dc.rights.uri http://creativecommons.org/licenses/by/3.0
dc.subject.other Expressió gènica
dc.title PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/bts402
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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