Abstract

BackgroundAnalysis of somatic mutations from tumor whole exomes has fueled discovery of novel cancer driver genes. However, ~ 98% of the genome is non-coding and includes regulatory elements whose normal cellular functions can be disrupted by mutation. Whole genome sequencing (WGS), on the other hand, allows for identification of non-coding somatic variation and expanded estimation of background mutation rates, yet fewer computational tools exist for specific interrogation of this space.ResultsWe present MutEnricher, a flexible toolset for investigating somatic mutation enrichment in both coding and non-coding genomic regions from WGS data. MutEnricher contains two distinct modules for these purposes that provide customizable options for calculating sample- and feature-specific background mutation rates. Additionally, both MutEnricher modules calculate feature-level and local, or “hotspot,” somatic mutation enrichment statistics.ConclusionsMutEnricher is a flexible software package for investigating somatic mutation enrichment that is implemented in Python, is freely available, can be efficiently parallelized, and is highly configurable to researcher's specific needs. MutEnricher is available online at https://github.com/asoltis/MutEnricher.

Highlights

  • Analysis of somatic mutations from tumor whole exomes has fueled discovery of novel cancer driver genes

  • MutEnricher is composed of two distinct analysis modules: 1) coding, which identifies genes harboring recurrent non-silent somatic mutations and 2) noncoding, which identifies enrichment of somatic variation in user-defined non-coding genomic regions

  • We observed strong overlap among genes called statistically significant by MutEnricher’s burden testing strategy with those called by MutSigCV (100% median/~ 76% mean overlap, dataset-wise overlap significance all < 2.8e-4 by hypergeometric test, Supplementary Table 2A)

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Summary

Results

We present MutEnricher, a flexible toolset for investigating somatic mutation enrichment in both coding and non-coding genomic regions from WGS data. MutEnricher contains two distinct modules for these purposes that provide customizable options for calculating sample- and feature-specific background mutation rates. Both MutEnricher modules calculate feature-level and local, or “hotspot,” somatic mutation enrichment statistics

Conclusions
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