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

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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.

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