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

Recent years saw the development of methods to detect signals of positive selection in the pattern of somatic mutations in genes across cohorts of tumors, and the discovery of hundreds of driver genes. The next major challenge in tumor genomics is the identification of non-coding regions which may also drive tumorigenesis. We present OncodriveFML, a method that estimates the accumulated functional impact bias of somatic mutations in any genomic region of interest based on a local simulation of the mutational process affecting it. It may be applied to all genomic elements to detect likely drivers amongst them. OncodriveFML can discover signals of positive selection when only a small fraction of the genome, like a panel of genes, has been sequenced.

For more details: https://bitbucket.org/bbglab/oncodrivefml