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

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 acros...

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Detalles Bibliográficos
Autores principales: Mularoni, Loris, Sabarinathan, Radhakrishnan, Deu-Pons, Jordi, Gonzalez-Perez, Abel, López-Bigas, Núria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4910259/
https://www.ncbi.nlm.nih.gov/pubmed/27311963
http://dx.doi.org/10.1186/s13059-016-0994-0
Descripción
Sumario: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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0994-0) contains supplementary material, which is available to authorized users.

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