{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lochovsky L"],"funding":["NHGRI NIH HHS","National Institutes of Health"],"pagination":["1031-1033"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5860157"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["34(6)"],"pubmed_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"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool."],"pmcid":["PMC5860157"],"funding_grant_id":["R01 HG008126","5U41HG007000-04"],"pubmed_authors":["Gerstein M","Zhang J","Lochovsky L"],"additional_accession":[]},"is_claimable":false,"name":"MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool.","description":"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","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Mar","modification":"2025-04-05T15:10:54.548Z","creation":"2019-03-26T23:44:11Z"},"accession":"S-EPMC5860157","cross_references":{"pubmed":["29121169"],"doi":["10.1093/bioinformatics/btx700"]}}