MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool

Bioinformatics. 2018 Mar 15;34(6):1031-1033. doi: 10.1093/bioinformatics/btx700.

Abstract

Summary: Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of ∼250.

Availability and implementation: MOAT is available at moat.gersteinlab.org.

Contact: mark@gersteinlab.org.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • DNA Mutational Analysis / methods
  • Genomics / methods
  • Humans
  • Mutation Rate*
  • Mutation*
  • Neoplasms / genetics*
  • Sequence Analysis, DNA / methods*
  • Software*