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

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 permutati

SUBMITTER: Lochovsky L 

PROVIDER: S-EPMC5860157 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature