biostudies-literatureLochovsky LNHGRI NIH HHSNational Institutes of Health1031-1033https://www.ebi.ac.uk/biostudies/studies/S-EPMC5860157biostudies-literatureUnknown34(6)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 permutatiBioinformatics (Oxford, England)MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool.PMC5860157R01 HG0081265U41HG007000-04Gerstein MZhang JLochovsky LfalseMOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool.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 permutati2018-01-01T00:00:00Z2018 Mar2025-04-05T15:10:54.548Z2019-03-26T23:44:11ZS-EPMC58601572912116910.1093/bioinformatics/btx700