Comparison of algorithms for the detection of cancer drivers at subgene resolution

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• dc.contributor.author Porta-Pardo, Eduard
• dc.contributor.author Kamburov, Atanas
• dc.contributor.author Tamborero Noguera, David
• dc.contributor.author Pons, Tirso
• dc.contributor.author Grases, Daniela
• dc.contributor.author Valencia, Alfonso
• dc.contributor.author López Bigas, Núria
• dc.contributor.author Getz, Gad
• dc.contributor.author Godzik, Adam
• dc.date.accessioned 2018-11-13T09:56:54Z
• dc.date.available 2018-11-13T09:56:54Z
• dc.date.issued 2017
• dc.description.abstract Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.
• dc.description.sponsorship E.P.-P. and A.G. acknowledge the support from the Cancer Center grants P30 CA030199 (to our institute) and R35 GM118187 (A.G.). A.K. was supported by startup funds of G.G. and by a collaboration with Bayer AG. D.T. is supported by project SAF2015-74072-JIN, which is funded by the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). N.L.-B. acknowledges funding from the European Research Council (consolidator grant 682398). A.V. and T.P. acknowledge funding by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 305444 (RD-Connect)
• dc.format.mimetype application/pdf
• dc.identifier.citation Porta-Pardo E, Kamburov A, Tamborero D, Pons T, Grases D, Valencia A et al. Comparison of algorithms for the detection of cancer drivers at subgene resolution. Nat Methods. 2017 Aug;14(8):782-8. DOI: 10.1038/nmeth.4364
• dc.identifier.doi http://dx.doi.org/10.1038/nmeth.4364
• dc.identifier.issn 1548-7091
• dc.identifier.uri http://hdl.handle.net/10230/35737
• dc.language.iso eng
• dc.publisher Nature Publishing Group
• dc.relation.ispartof Nature Methods. 2017 Aug;14(8):782-8
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/682398
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/305444
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/SAF2015-74072-JIN
• dc.rights © Nature Publishing Group. http://dx.doi.org/10.1038/nmeth.4364
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.other Algorismes
• dc.subject.other Carcinogènesi
• dc.subject.other Cromosomes
• dc.subject.other Tumors
• dc.title Comparison of algorithms for the detection of cancer drivers at subgene resolution
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion