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A mutation-level covariate model for mutational signatures

Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that...

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Detalles Bibliográficos
Autores principales: Kahane, Itay, Leiserson, Mark D. M., Sharan, Roded
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270581/
https://www.ncbi.nlm.nih.gov/pubmed/37276234
http://dx.doi.org/10.1371/journal.pcbi.1011195
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author Kahane, Itay
Leiserson, Mark D. M.
Sharan, Roded
author_facet Kahane, Itay
Leiserson, Mark D. M.
Sharan, Roded
author_sort Kahane, Itay
collection PubMed
description Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information.
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spelling pubmed-102705812023-06-16 A mutation-level covariate model for mutational signatures Kahane, Itay Leiserson, Mark D. M. Sharan, Roded PLoS Comput Biol Research Article Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information. Public Library of Science 2023-06-05 /pmc/articles/PMC10270581/ /pubmed/37276234 http://dx.doi.org/10.1371/journal.pcbi.1011195 Text en © 2023 Kahane et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kahane, Itay
Leiserson, Mark D. M.
Sharan, Roded
A mutation-level covariate model for mutational signatures
title A mutation-level covariate model for mutational signatures
title_full A mutation-level covariate model for mutational signatures
title_fullStr A mutation-level covariate model for mutational signatures
title_full_unstemmed A mutation-level covariate model for mutational signatures
title_short A mutation-level covariate model for mutational signatures
title_sort mutation-level covariate model for mutational signatures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270581/
https://www.ncbi.nlm.nih.gov/pubmed/37276234
http://dx.doi.org/10.1371/journal.pcbi.1011195
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