ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Malene Juul
  • ,
  • Tobias Madsen
  • ,
  • Qianyun Guo
  • ,
  • Johanna Bertl
  • ,
  • Asger Hobolth
  • Manolis Kellis, MIT - Massachusetts Institute of Technology, United States
  • Jakob Skou Pedersen

Motivation Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources. Results Here, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P-values. Availability and implementation ncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2 Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish
JournalBioinformatics
Volume35
Issue2
Pages (from-to)189-199
Number of pages11
ISSN1367-4803
DOIs
Publication statusPublished - 15 Jan 2019

    Research areas

  • EXCISION-REPAIR, EXPRESSION, FRAMEWORK, GENOME-WIDE ANALYSIS, RECURRENT, REGULATORY MUTATIONS, SOMATIC MUTATIONS, TERT PROMOTER MUTATIONS, VARIANTS

See relations at Aarhus University Citationformats

ID: 128633597