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

Genome Biol. 2016 Jun 16;17(1):128. doi: 10.1186/s13059-016-0994-0.


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.

Keywords: Cancer drivers; Local functional mutations bias; Non-coding drivers; Non-coding regions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carcinogenesis / genetics*
  • Computational Biology*
  • Genome, Human
  • Humans
  • Mutation
  • Neoplasms / genetics*
  • Open Reading Frames / genetics
  • Promoter Regions, Genetic
  • RNA, Long Noncoding / genetics
  • Software*


  • RNA, Long Noncoding