MSA: reproducible mutational signature attribution with confidence based on simulations

Abstract Background Mutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage. De novo extraction of signatures is commonly performed using Non-Negative Matrix Factorisation meth...

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Autor principal:	Sergey Senkin
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	MSA Mutational signatures NNLS Parametric bootstrap Nextflow Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d101f8dfde724132b54e2266d9c6cf30
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spelling	oai:doaj.org-article:d101f8dfde724132b54e2266d9c6cf302021-11-07T12:22:16ZMSA: reproducible mutational signature attribution with confidence based on simulations10.1186/s12859-021-04450-81471-2105https://doaj.org/article/d101f8dfde724132b54e2266d9c6cf302021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04450-8https://doaj.org/toc/1471-2105Abstract Background Mutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage. De novo extraction of signatures is commonly performed using Non-Negative Matrix Factorisation methods, however, accurate attribution of these signatures to individual samples is a distinct problem requiring uncertainty estimation, particularly in noisy scenarios or when the acting signatures have similar shapes. Whilst many packages for signature attribution exist, a few provide accuracy measures, and most are not easily reproducible nor scalable in high-performance computing environments. Results We present Mutational Signature Attribution (MSA), a reproducible pipeline designed to assign signatures of different mutation types on a single-sample basis, using Non-Negative Least Squares method with optimisation based on configurable simulations. Parametric bootstrap is proposed as a way to measure statistical uncertainties of signature attribution. Supported mutation types include single and doublet base substitutions, indels and structural variants. Results are validated using simulations with reference COSMIC signatures, as well as randomly generated signatures. Conclusions MSA is a tool for optimised mutational signature attribution based on simulations, providing confidence intervals using parametric bootstrap. It comprises a set of Python scripts unified in a single Nextflow pipeline with containerisation for cross-platform reproducibility and scalability in high-performance computing environments. The tool is publicly available from https://gitlab.com/s.senkin/MSA .Sergey SenkinBMCarticleMSAMutational signaturesNNLSParametric bootstrapNextflowComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MSA Mutational signatures NNLS Parametric bootstrap Nextflow Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	MSA Mutational signatures NNLS Parametric bootstrap Nextflow Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Sergey Senkin MSA: reproducible mutational signature attribution with confidence based on simulations
description	Abstract Background Mutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage. De novo extraction of signatures is commonly performed using Non-Negative Matrix Factorisation methods, however, accurate attribution of these signatures to individual samples is a distinct problem requiring uncertainty estimation, particularly in noisy scenarios or when the acting signatures have similar shapes. Whilst many packages for signature attribution exist, a few provide accuracy measures, and most are not easily reproducible nor scalable in high-performance computing environments. Results We present Mutational Signature Attribution (MSA), a reproducible pipeline designed to assign signatures of different mutation types on a single-sample basis, using Non-Negative Least Squares method with optimisation based on configurable simulations. Parametric bootstrap is proposed as a way to measure statistical uncertainties of signature attribution. Supported mutation types include single and doublet base substitutions, indels and structural variants. Results are validated using simulations with reference COSMIC signatures, as well as randomly generated signatures. Conclusions MSA is a tool for optimised mutational signature attribution based on simulations, providing confidence intervals using parametric bootstrap. It comprises a set of Python scripts unified in a single Nextflow pipeline with containerisation for cross-platform reproducibility and scalability in high-performance computing environments. The tool is publicly available from https://gitlab.com/s.senkin/MSA .
format	article
author	Sergey Senkin
author_facet	Sergey Senkin
author_sort	Sergey Senkin
title	MSA: reproducible mutational signature attribution with confidence based on simulations
title_short	MSA: reproducible mutational signature attribution with confidence based on simulations
title_full	MSA: reproducible mutational signature attribution with confidence based on simulations
title_fullStr	MSA: reproducible mutational signature attribution with confidence based on simulations
title_full_unstemmed	MSA: reproducible mutational signature attribution with confidence based on simulations
title_sort	msa: reproducible mutational signature attribution with confidence based on simulations
publisher	BMC
publishDate	2021
url	https://doaj.org/article/d101f8dfde724132b54e2266d9c6cf30
work_keys_str_mv	AT sergeysenkin msareproduciblemutationalsignatureattributionwithconfidencebasedonsimulations
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MSA: reproducible mutational signature attribution with confidence based on simulations

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