MutSpot: detection of non-coding mutation hotspots in cancer genomes

2019 · doi:10.1101/740944
preprint OA: closed
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Abstract

Summary Recurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes. Availability and implementation MutSpot is implemented as an R package and is available at https://github.com/skandlab/MutSpot/ Supplementary information Supplementary data are available at https://github.com/skandlab/MutSpot/
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Abstract Summary Recurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes. Availability and implementation MutSpot is implemented as an R package and is available at https://github.com/skandlab/MutSpot/ Supplementary information Supplementary data are available at https://github.com/skandlab/MutSpot/

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