OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Mularoni, Loris; Sabarinathan, Radhakrishnan; Deu-Pons, Jordi; Gonzalez-Perez, Abel; López-Bigas, Núria
• Is Part Of: Genome biology. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-12
• Subjects: Cancer drivers -- Non-coding regions -- Local functional mutations bias -- Non-coding drivers; Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-016-0994-0
• Languages: English
• ISSNs: 1474-760X

Abstract

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.