A framework for mutational signature analysis based on DNA shape parameters
The mutation risk of a DNA locus depends on its oligonucleotide context. In turn, mutability of oligonucleotides varies across individuals, due to exposure to mutagenic agents or due to variable efficiency and/or accuracy of DNA repair. Such variability is captured by mutational signatures, a mathem...
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description | The mutation risk of a DNA locus depends on its oligonucleotide context. In turn, mutability of oligonucleotides varies across individuals, due to exposure to mutagenic agents or due to variable efficiency and/or accuracy of DNA repair. Such variability is captured by mutational signatures, a mathematical construct obtained by a deconvolution of mutation frequency spectra across individuals. There is a need to enhance methods for inferring mutational signatures to make better use of sparse mutation data (e.g., resulting from exome sequencing of cancers), to facilitate insight into underlying biological mechanisms, and to provide more accurate mutation rate baselines for inferring positive and negative selection. We propose a conceptualization of mutational signatures that represents oligonucleotides via descriptors of DNA conformation: base pair, base pair step, and minor groove width parameters. We demonstrate how such DNA structural parameters can accurately predict mutation occurrence due to DNA repair failures or due to exposure to diverse mutagens such as radiation, chemical exposure, and the APOBEC cytosine deaminase enzymes. Furthermore, the mutation frequency of DNA oligomers classed by structural features can accurately capture systematic variability in mutagenesis of >1,000 tumors originating from diverse human tissues. A nonnegative matrix factorization was applied to mutation spectra stratified by DNA structural features, thereby extracting novel mutational signatures. Moreover, many of the known trinucleotide signatures were associated with an additional spectrum in the DNA structural descriptor space, which may aid interpretation and provide mechanistic insight. Overall, we suggest that the power of DNA sequence motif-based mutational signature analysis can be enhanced by drawing on DNA shape features. |
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In turn, mutability of oligonucleotides varies across individuals, due to exposure to mutagenic agents or due to variable efficiency and/or accuracy of DNA repair. Such variability is captured by mutational signatures, a mathematical construct obtained by a deconvolution of mutation frequency spectra across individuals. There is a need to enhance methods for inferring mutational signatures to make better use of sparse mutation data (e.g., resulting from exome sequencing of cancers), to facilitate insight into underlying biological mechanisms, and to provide more accurate mutation rate baselines for inferring positive and negative selection. We propose a conceptualization of mutational signatures that represents oligonucleotides via descriptors of DNA conformation: base pair, base pair step, and minor groove width parameters. We demonstrate how such DNA structural parameters can accurately predict mutation occurrence due to DNA repair failures or due to exposure to diverse mutagens such as radiation, chemical exposure, and the APOBEC cytosine deaminase enzymes. Furthermore, the mutation frequency of DNA oligomers classed by structural features can accurately capture systematic variability in mutagenesis of >1,000 tumors originating from diverse human tissues. A nonnegative matrix factorization was applied to mutation spectra stratified by DNA structural features, thereby extracting novel mutational signatures. Moreover, many of the known trinucleotide signatures were associated with an additional spectrum in the DNA structural descriptor space, which may aid interpretation and provide mechanistic insight. Overall, we suggest that the power of DNA sequence motif-based mutational signature analysis can be enhanced by drawing on DNA shape features.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0262495</identifier><identifier>PMID: 35015788</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>APOBEC Deaminases - metabolism ; Auroral kilometric radiation ; Biology and Life Sciences ; Cancer ; Conformation ; Cytosine ; Cytosine deaminase ; Data science ; Deoxyribonucleic acid ; DNA ; DNA - chemistry ; DNA - genetics ; DNA Damage ; DNA Mutational Analysis - methods ; DNA Repair ; Exposure ; Feature extraction ; Frequency spectrum ; Gene mutations ; Genome, Human ; Genomes ; Grooves ; Health aspects ; Human tissues ; Humans ; Mathematical analysis ; Medicine and Health Sciences ; Molecular structure ; Mutagenesis ; Mutagens ; Mutation ; Mutation rates ; Negative selection ; Neoplasms - genetics ; Neoplasms - pathology ; Nucleic Acid Conformation ; Nucleotide sequence ; Oligonucleotides ; Parameters ; Physiological aspects ; Radiation ; Repair ; Sequences ; Signature analysis ; Signatures ; Structure ; Transcriptome ; Tumors ; Variability</subject><ispartof>PloS one, 2022-01, Vol.17 (1), p.e0262495</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Karolak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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In turn, mutability of oligonucleotides varies across individuals, due to exposure to mutagenic agents or due to variable efficiency and/or accuracy of DNA repair. Such variability is captured by mutational signatures, a mathematical construct obtained by a deconvolution of mutation frequency spectra across individuals. There is a need to enhance methods for inferring mutational signatures to make better use of sparse mutation data (e.g., resulting from exome sequencing of cancers), to facilitate insight into underlying biological mechanisms, and to provide more accurate mutation rate baselines for inferring positive and negative selection. We propose a conceptualization of mutational signatures that represents oligonucleotides via descriptors of DNA conformation: base pair, base pair step, and minor groove width parameters. We demonstrate how such DNA structural parameters can accurately predict mutation occurrence due to DNA repair failures or due to exposure to diverse mutagens such as radiation, chemical exposure, and the APOBEC cytosine deaminase enzymes. Furthermore, the mutation frequency of DNA oligomers classed by structural features can accurately capture systematic variability in mutagenesis of >1,000 tumors originating from diverse human tissues. A nonnegative matrix factorization was applied to mutation spectra stratified by DNA structural features, thereby extracting novel mutational signatures. Moreover, many of the known trinucleotide signatures were associated with an additional spectrum in the DNA structural descriptor space, which may aid interpretation and provide mechanistic insight. Overall, we suggest that the power of DNA sequence motif-based mutational signature analysis can be enhanced by drawing on DNA shape features.</description><subject>APOBEC Deaminases - metabolism</subject><subject>Auroral kilometric radiation</subject><subject>Biology and Life Sciences</subject><subject>Cancer</subject><subject>Conformation</subject><subject>Cytosine</subject><subject>Cytosine deaminase</subject><subject>Data science</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA - chemistry</subject><subject>DNA - genetics</subject><subject>DNA Damage</subject><subject>DNA Mutational Analysis - methods</subject><subject>DNA Repair</subject><subject>Exposure</subject><subject>Feature extraction</subject><subject>Frequency spectrum</subject><subject>Gene mutations</subject><subject>Genome, Human</subject><subject>Genomes</subject><subject>Grooves</subject><subject>Health aspects</subject><subject>Human tissues</subject><subject>Humans</subject><subject>Mathematical analysis</subject><subject>Medicine and Health Sciences</subject><subject>Molecular structure</subject><subject>Mutagenesis</subject><subject>Mutagens</subject><subject>Mutation</subject><subject>Mutation rates</subject><subject>Negative selection</subject><subject>Neoplasms - 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metabolism</topic><topic>Auroral kilometric radiation</topic><topic>Biology and Life Sciences</topic><topic>Cancer</topic><topic>Conformation</topic><topic>Cytosine</topic><topic>Cytosine deaminase</topic><topic>Data science</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA - chemistry</topic><topic>DNA - genetics</topic><topic>DNA Damage</topic><topic>DNA Mutational Analysis - methods</topic><topic>DNA Repair</topic><topic>Exposure</topic><topic>Feature extraction</topic><topic>Frequency spectrum</topic><topic>Gene mutations</topic><topic>Genome, Human</topic><topic>Genomes</topic><topic>Grooves</topic><topic>Health aspects</topic><topic>Human tissues</topic><topic>Humans</topic><topic>Mathematical analysis</topic><topic>Medicine and Health Sciences</topic><topic>Molecular structure</topic><topic>Mutagenesis</topic><topic>Mutagens</topic><topic>Mutation</topic><topic>Mutation rates</topic><topic>Negative selection</topic><topic>Neoplasms - genetics</topic><topic>Neoplasms - pathology</topic><topic>Nucleic Acid Conformation</topic><topic>Nucleotide sequence</topic><topic>Oligonucleotides</topic><topic>Parameters</topic><topic>Physiological aspects</topic><topic>Radiation</topic><topic>Repair</topic><topic>Sequences</topic><topic>Signature analysis</topic><topic>Signatures</topic><topic>Structure</topic><topic>Transcriptome</topic><topic>Tumors</topic><topic>Variability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karolak, Aleksandra</creatorcontrib><creatorcontrib>Levatić, Jurica</creatorcontrib><creatorcontrib>Supek, Fran</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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In turn, mutability of oligonucleotides varies across individuals, due to exposure to mutagenic agents or due to variable efficiency and/or accuracy of DNA repair. Such variability is captured by mutational signatures, a mathematical construct obtained by a deconvolution of mutation frequency spectra across individuals. There is a need to enhance methods for inferring mutational signatures to make better use of sparse mutation data (e.g., resulting from exome sequencing of cancers), to facilitate insight into underlying biological mechanisms, and to provide more accurate mutation rate baselines for inferring positive and negative selection. We propose a conceptualization of mutational signatures that represents oligonucleotides via descriptors of DNA conformation: base pair, base pair step, and minor groove width parameters. We demonstrate how such DNA structural parameters can accurately predict mutation occurrence due to DNA repair failures or due to exposure to diverse mutagens such as radiation, chemical exposure, and the APOBEC cytosine deaminase enzymes. Furthermore, the mutation frequency of DNA oligomers classed by structural features can accurately capture systematic variability in mutagenesis of >1,000 tumors originating from diverse human tissues. A nonnegative matrix factorization was applied to mutation spectra stratified by DNA structural features, thereby extracting novel mutational signatures. Moreover, many of the known trinucleotide signatures were associated with an additional spectrum in the DNA structural descriptor space, which may aid interpretation and provide mechanistic insight. Overall, we suggest that the power of DNA sequence motif-based mutational signature analysis can be enhanced by drawing on DNA shape features.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35015788</pmid><doi>10.1371/journal.pone.0262495</doi><tpages>e0262495</tpages><orcidid>https://orcid.org/0000-0002-4025-051X</orcidid><orcidid>https://orcid.org/0000-0002-7811-6711</orcidid><orcidid>https://orcid.org/0000-0001-8430-7138</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | APOBEC Deaminases - metabolism Auroral kilometric radiation Biology and Life Sciences Cancer Conformation Cytosine Cytosine deaminase Data science Deoxyribonucleic acid DNA DNA - chemistry DNA - genetics DNA Damage DNA Mutational Analysis - methods DNA Repair Exposure Feature extraction Frequency spectrum Gene mutations Genome, Human Genomes Grooves Health aspects Human tissues Humans Mathematical analysis Medicine and Health Sciences Molecular structure Mutagenesis Mutagens Mutation Mutation rates Negative selection Neoplasms - genetics Neoplasms - pathology Nucleic Acid Conformation Nucleotide sequence Oligonucleotides Parameters Physiological aspects Radiation Repair Sequences Signature analysis Signatures Structure Transcriptome Tumors Variability |
title | A framework for mutational signature analysis based on DNA shape parameters |
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