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dc.contributor.author López Bigas, Núria
dc.contributor.author González-Pérez, Abel
dc.date.accessioned 2015-04-07T09:36:19Z
dc.date.available 2015-04-07T09:36:19Z
dc.date.issued 2012
dc.identifier.citation Gonzalez-Perez A, Lopez-Bigas N. Functional impact bias reveals cancer drivers. Nucleic Acids Research. 2012; 40(21): e169. DOI 10.1093/nar/gks743
dc.identifier.issn 0305-1048
dc.identifier.uri http://hdl.handle.net/10230/23344
dc.description.abstract Identifying cancer driver genes and pathways among all somatic mutations detected in a cohort of tumors is a key challenge in cancer genomics. Traditionally, this is done by prioritizing genes according to the recurrence of alterations that they bear. However, this approach has some known limitations, such as the difficulty to correctly estimate the background mutation rate, and the fact that it cannot identify lowly recurrently mutated driver genes. Here we present a novel approach, Oncodrive-fm, to detect candidate cancer drivers which does not rely on recurrence. First, we hypothesized that any bias toward the accumulation of variants with high functional impact observed in a gene or group of genes may be an indication of positive selection and can thus be used to detect candidate driver genes or gene modules. Next, we developed a method to measure this bias (FM bias) and applied it to three datasets of tumor somatic variants. As a proof of concept of our hypothesis we show that most of the highly recurrent and well-known cancer genes exhibit a clear FM bias. Moreover, this novel approach avoids some known limitations of recurrence-based approaches, and can successfully identify lowly recurrent candidate cancer drivers.
dc.description.sponsorship Spanish Ministry of Science and Technology [SAF2009-06954]; Spanish National Institute of Bioinformatics (INB). Funding for open access charge: Spanish Ministry of Science and Technology [SAF2009-06954]
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Oxford University Press
dc.relation.ispartof Nucleic Acids Research. 2012; 40(21): e169
dc.rights © Gonzalez-Perez A, Lopez-Bigas N [2012]. Published by Oxford University Press. This is an Open Access article distributed under the terms of a Creative Commons Attribution License
dc.rights.uri http://creativecommons.org/licenses/by-nc/3.0
dc.subject.other Genètica humana -- Variació
dc.subject.other Genòmica -- Mètodes estadístics
dc.title Functional impact bias reveals cancer drivers
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1093/nar/gks743
dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/SAF2009-06954
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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