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MutationalPatterns: the one stop shop for the analysis of mutational processes
BACKGROUND: The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. RESULTS: Here, we present the next version of MutationalPat...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8845394/ https://www.ncbi.nlm.nih.gov/pubmed/35168570 http://dx.doi.org/10.1186/s12864-022-08357-3 |
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author | Manders, Freek Brandsma, Arianne M. de Kanter, Jurrian Verheul, Mark Oka, Rurika van Roosmalen, Markus J. van der Roest, Bastiaan van Hoeck, Arne Cuppen, Edwin van Boxtel, Ruben |
author_facet | Manders, Freek Brandsma, Arianne M. de Kanter, Jurrian Verheul, Mark Oka, Rurika van Roosmalen, Markus J. van der Roest, Bastiaan van Hoeck, Arne Cuppen, Edwin van Boxtel, Ruben |
author_sort | Manders, Freek |
collection | PubMed |
description | BACKGROUND: The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. RESULTS: Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. CONCLUSIONS: This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08357-3. |
format | Online Article Text |
id | pubmed-8845394 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88453942022-02-16 MutationalPatterns: the one stop shop for the analysis of mutational processes Manders, Freek Brandsma, Arianne M. de Kanter, Jurrian Verheul, Mark Oka, Rurika van Roosmalen, Markus J. van der Roest, Bastiaan van Hoeck, Arne Cuppen, Edwin van Boxtel, Ruben BMC Genomics Software BACKGROUND: The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. RESULTS: Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. CONCLUSIONS: This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08357-3. BioMed Central 2022-02-15 /pmc/articles/PMC8845394/ /pubmed/35168570 http://dx.doi.org/10.1186/s12864-022-08357-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Manders, Freek Brandsma, Arianne M. de Kanter, Jurrian Verheul, Mark Oka, Rurika van Roosmalen, Markus J. van der Roest, Bastiaan van Hoeck, Arne Cuppen, Edwin van Boxtel, Ruben MutationalPatterns: the one stop shop for the analysis of mutational processes |
title | MutationalPatterns: the one stop shop for the analysis of mutational processes |
title_full | MutationalPatterns: the one stop shop for the analysis of mutational processes |
title_fullStr | MutationalPatterns: the one stop shop for the analysis of mutational processes |
title_full_unstemmed | MutationalPatterns: the one stop shop for the analysis of mutational processes |
title_short | MutationalPatterns: the one stop shop for the analysis of mutational processes |
title_sort | mutationalpatterns: the one stop shop for the analysis of mutational processes |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8845394/ https://www.ncbi.nlm.nih.gov/pubmed/35168570 http://dx.doi.org/10.1186/s12864-022-08357-3 |
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