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

Loris Mularoni1, Radhakrishnan Sabarinathan1, Jordi Deu-Pons1

  • 1Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003, Barcelona, Catalonia, Spain.

Genome Biology
|June 18, 2016
PubMed
Summary
This summary is machine-generated.

Identifying cancer-driving mutations in both coding and non-coding tumor DNA is challenging. OncodriveFML analyzes mutation patterns to detect positive selection, revealing driver mutations in various genomic regions across cancers.

Keywords:
Cancer driversLocal functional mutations biasNon-coding driversNon-coding regions

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Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Distinguishing driver mutations from background somatic mutations in tumor genomes is a significant challenge in cancer research.
  • This challenge is particularly pronounced for non-coding mutations, where identifying their role in tumorigenesis remains largely unsolved.

Purpose of the Study:

  • To introduce OncodriveFML, a novel computational method for analyzing somatic mutation patterns.
  • To identify driver mutations in both coding and non-coding genomic regions by detecting signals of positive selection.
  • To demonstrate the method's utility in identifying driver mutations across various cancer-related genomic elements.

Main Methods:

  • OncodriveFML analyzes the distribution and patterns of somatic mutations across multiple tumor genomes.
  • The method assesses genomic regions for signals of positive selection, indicative of a role in cancer development.
  • The approach is applied to both protein-coding and non-coding genomic regions, including promoters, UTRs, and intronic splice sites.

Main Results:

  • The study presents OncodriveFML as a tool for uncovering driver mutations.
  • The method successfully identified driver mutations in protein-coding genes, promoters, untranslated regions, intronic splice regions, and lncRNAs.
  • The utility of OncodriveFML was demonstrated across several types of malignancies.

Conclusions:

  • OncodriveFML provides a robust method for identifying driver mutations in cancer genomes.
  • The approach is effective in detecting positive selection signals in both coding and non-coding regions.
  • This method aids in understanding the genetic basis of tumorigenesis by pinpointing critical mutations.