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MutSignatures: an R package for extraction and analysis of cancer mutational signatures
Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based com...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589488/ https://www.ncbi.nlm.nih.gov/pubmed/33106540 http://dx.doi.org/10.1038/s41598-020-75062-0 |
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author | Fantini, Damiano Vidimar, Vania Yu, Yanni Condello, Salvatore Meeks, Joshua J. |
author_facet | Fantini, Damiano Vidimar, Vania Yu, Yanni Condello, Salvatore Meeks, Joshua J. |
author_sort | Fantini, Damiano |
collection | PubMed |
description | Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. Specifically, mutSignatures accepts multiple types of input data, is compatible with non-human genomes, and supports the analysis of non-standard mutation types, such as tetra-nucleotide mutation types. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment. |
format | Online Article Text |
id | pubmed-7589488 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75894882020-10-28 MutSignatures: an R package for extraction and analysis of cancer mutational signatures Fantini, Damiano Vidimar, Vania Yu, Yanni Condello, Salvatore Meeks, Joshua J. Sci Rep Article Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. Specifically, mutSignatures accepts multiple types of input data, is compatible with non-human genomes, and supports the analysis of non-standard mutation types, such as tetra-nucleotide mutation types. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment. Nature Publishing Group UK 2020-10-26 /pmc/articles/PMC7589488/ /pubmed/33106540 http://dx.doi.org/10.1038/s41598-020-75062-0 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Fantini, Damiano Vidimar, Vania Yu, Yanni Condello, Salvatore Meeks, Joshua J. MutSignatures: an R package for extraction and analysis of cancer mutational signatures |
title | MutSignatures: an R package for extraction and analysis of cancer mutational signatures |
title_full | MutSignatures: an R package for extraction and analysis of cancer mutational signatures |
title_fullStr | MutSignatures: an R package for extraction and analysis of cancer mutational signatures |
title_full_unstemmed | MutSignatures: an R package for extraction and analysis of cancer mutational signatures |
title_short | MutSignatures: an R package for extraction and analysis of cancer mutational signatures |
title_sort | mutsignatures: an r package for extraction and analysis of cancer mutational signatures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589488/ https://www.ncbi.nlm.nih.gov/pubmed/33106540 http://dx.doi.org/10.1038/s41598-020-75062-0 |
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