1Program in Computational Biology and Bioinformatics2Department of Molecular Biophysics and Biochemistry3Department of Computer Science, Yale University, New Haven, CT 06520, USA
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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:email@example.comSupplementary information:Supplementary data are available at Bioinformatics online.