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

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 hund...

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Autores principales: Guo, Yu Amanda, Chang, Mei Mei, Skanderup, Anders Jacobsen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275039/
https://www.ncbi.nlm.nih.gov/pubmed/32550006
http://dx.doi.org/10.1038/s41525-020-0133-4
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author Guo, Yu Amanda
Chang, Mei Mei
Skanderup, Anders Jacobsen
author_facet Guo, Yu Amanda
Chang, Mei Mei
Skanderup, Anders Jacobsen
author_sort Guo, Yu Amanda
collection PubMed
description 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.
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spelling pubmed-72750392020-06-16 MutSpot: detection of non-coding mutation hotspots in cancer genomes Guo, Yu Amanda Chang, Mei Mei Skanderup, Anders Jacobsen NPJ Genom Med Brief Communication 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. Nature Publishing Group UK 2020-06-05 /pmc/articles/PMC7275039/ /pubmed/32550006 http://dx.doi.org/10.1038/s41525-020-0133-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Brief Communication
Guo, Yu Amanda
Chang, Mei Mei
Skanderup, Anders Jacobsen
MutSpot: detection of non-coding mutation hotspots in cancer genomes
title MutSpot: detection of non-coding mutation hotspots in cancer genomes
title_full MutSpot: detection of non-coding mutation hotspots in cancer genomes
title_fullStr MutSpot: detection of non-coding mutation hotspots in cancer genomes
title_full_unstemmed MutSpot: detection of non-coding mutation hotspots in cancer genomes
title_short MutSpot: detection of non-coding mutation hotspots in cancer genomes
title_sort mutspot: detection of non-coding mutation hotspots in cancer genomes
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275039/
https://www.ncbi.nlm.nih.gov/pubmed/32550006
http://dx.doi.org/10.1038/s41525-020-0133-4
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