OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations

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dc.contributor.author	Mularoni, Loris
dc.contributor.author	Sabarinathan, Radhakrishnan
dc.contributor.author	Déu Pons, Jordi
dc.contributor.author	González-Pérez, Abel
dc.contributor.author	López Bigas, Núria
dc.date	2016
dc.identifier.citation	Mularoni L, Sabarinathan R, Déu Pons J, González-Pérez A, López Bigas N. OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations. Genome Biology. 2016; 17:128. DOI: 10.1186/s13059-016-0994-0
dc.identifier.citation	1474-760X
dc.identifier.citation	http://dx.doi.org/10.1186/s13059-016-0994-0
dc.identifier.uri	http://hdl.handle.net/10230/27626
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	BioMed Central
dc.relation	Genome Biology. 2016; 17:128
dc.relation	info:eu-repo/grantAgreement/EC/FP7/600394
dc.relation	info:eu-repo/grantAgreement/ES/1PE/SAF2015-66084-R
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.rights	http://creativecommons.org/licenses/by/4.0/
dc.subject	Cancer drivers
dc.subject	Non-coding regions
dc.subject	Local functional mutations bias
dc.subject	Non-coding drivers
dc.title	OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.description.abstract	Distinguishing the driver mutations from somatic mutations in a tumor genome is one of the major challenges of cancer research. This challenge is more acute and far from solved for non-coding mutations. Here we present OncodriveFML, a method designed to analyze the pattern of somatic mutations across tumors in both coding and non-coding genomic regions to identify signals of positive selection, and therefore, their involvement in tumorigenesis. We describe the method and illustrate its usefulness to identify protein-coding genes, promoters, untranslated regions, intronic splice regions, and lncRNAs-containing driver mutations in several malignancies.
dc.description.abstract	We acknowledge funding from the Spanish Ministry of Economy and Competitiveness (SAF2015-66084-R, MINECO/FEDER, UE), the Marató de TV3 Foundation, and the Spanish National Institute of Bioinformatics (INB). R.S. is supported by an EMBO Long-Term Fellowship (ALTF 568-2014) co-funded by the European Commission (EMBOCOFUND2012, GA-2012-600394) support from Marie Curie Actions. A.G.-P. is supported by a Ramón y Cajal contract (RYC-2013-14554).

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