Computational approaches for discovery of mutational signatures in cancer
Abstract The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the caus...
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| Veröffentlicht in: | Briefings in bioinformatics 2019-01, Vol.20 (1), p.77-88 |
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| creator | Baez-Ortega, Adrian Gori, Kevin |
| description | Abstract
The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field. |
| doi_str_mv | 10.1093/bib/bbx082 |
| format | Article |
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The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field.</description><identifier>ISSN: 1467-5463</identifier><identifier>ISSN: 1477-4054</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbx082</identifier><identifier>PMID: 28968631</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Bayes Theorem ; Cancer ; Computational Biology ; Computer applications ; Deoxyribonucleic acid ; DNA ; DNA fingerprinting ; DNA, Neoplasm - genetics ; genome ; Genome, Human ; Genomes ; High-Throughput Nucleotide Sequencing - statistics & numerical data ; Humans ; Mathematical models ; Models, Genetic ; Models, Statistical ; mutagens ; Mutation ; neoplasms ; Neoplasms - genetics ; Sequence Analysis, DNA - statistics & numerical data ; Signatures ; Software ; somatic mutation</subject><ispartof>Briefings in bioinformatics, 2019-01, Vol.20 (1), p.77-88</ispartof><rights>The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2017</rights><rights>The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c535t-520b6ce166c01812870ae99335bc5ab201831cc767af6d3d2ac97b7c09c3610f3</citedby><cites>FETCH-LOGICAL-c535t-520b6ce166c01812870ae99335bc5ab201831cc767af6d3d2ac97b7c09c3610f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357558/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357558/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,1598,27901,27902,53766,53768</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bib/bbx082$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28968631$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Baez-Ortega, Adrian</creatorcontrib><creatorcontrib>Gori, Kevin</creatorcontrib><title>Computational approaches for discovery of mutational signatures in cancer</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract
The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field.</description><subject>Bayes Theorem</subject><subject>Cancer</subject><subject>Computational Biology</subject><subject>Computer applications</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA fingerprinting</subject><subject>DNA, Neoplasm - genetics</subject><subject>genome</subject><subject>Genome, Human</subject><subject>Genomes</subject><subject>High-Throughput Nucleotide Sequencing - statistics & numerical data</subject><subject>Humans</subject><subject>Mathematical models</subject><subject>Models, Genetic</subject><subject>Models, Statistical</subject><subject>mutagens</subject><subject>Mutation</subject><subject>neoplasms</subject><subject>Neoplasms - genetics</subject><subject>Sequence Analysis, DNA - statistics & numerical data</subject><subject>Signatures</subject><subject>Software</subject><subject>somatic mutation</subject><issn>1467-5463</issn><issn>1477-4054</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90VtLwzAYBuAgiofpjT9ACiKIUP2SNEl7I8jwBANv9DokWbpV2qYm7XD_3ozpPFx4lZA8vHzJi9AxhksMBb3Slb7S-h1ysoX2cSZEmgHLtld7LlKWcbqHDkJ4BSAgcryL9khe8JxTvI8ex67phl71lWtVnaiu806ZuQ1J6XwyrYJxC-uXiSuT5puFataqfvCRVW1iVGusP0Q7paqDPfpcR-jl7vZ5_JBOnu4fxzeT1DDK-pQR0NxYzLkBnGOSC1C2KChl2jClSTyk2BjBhSr5lE6JMoXQwkBhKMdQ0hG6Xud2g27s1Ni296qWna8a5ZfSqUr-vmmruZy5heSUCcbyGHD-GeDd22BDL5v4TFvXqrVuCJJkGfACGKaRnv6hr27w8QdWihSAAWAVeLFWxrsQvC03w2CQq4ZkbEiuG4r45Of4G_pVSQRna-CG7r-gD7oAmjE</recordid><startdate>20190118</startdate><enddate>20190118</enddate><creator>Baez-Ortega, Adrian</creator><creator>Gori, Kevin</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope></search><sort><creationdate>20190118</creationdate><title>Computational approaches for discovery of mutational signatures in cancer</title><author>Baez-Ortega, Adrian ; Gori, Kevin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c535t-520b6ce166c01812870ae99335bc5ab201831cc767af6d3d2ac97b7c09c3610f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Bayes Theorem</topic><topic>Cancer</topic><topic>Computational Biology</topic><topic>Computer applications</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA fingerprinting</topic><topic>DNA, Neoplasm - genetics</topic><topic>genome</topic><topic>Genome, Human</topic><topic>Genomes</topic><topic>High-Throughput Nucleotide Sequencing - statistics & numerical data</topic><topic>Humans</topic><topic>Mathematical models</topic><topic>Models, Genetic</topic><topic>Models, Statistical</topic><topic>mutagens</topic><topic>Mutation</topic><topic>neoplasms</topic><topic>Neoplasms - genetics</topic><topic>Sequence Analysis, DNA - statistics & numerical data</topic><topic>Signatures</topic><topic>Software</topic><topic>somatic mutation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baez-Ortega, Adrian</creatorcontrib><creatorcontrib>Gori, Kevin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Baez-Ortega, Adrian</au><au>Gori, Kevin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computational approaches for discovery of mutational signatures in cancer</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2019-01-18</date><risdate>2019</risdate><volume>20</volume><issue>1</issue><spage>77</spage><epage>88</epage><pages>77-88</pages><issn>1467-5463</issn><issn>1477-4054</issn><eissn>1477-4054</eissn><abstract>Abstract
The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>28968631</pmid><doi>10.1093/bib/bbx082</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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| source | Oxford Journals Open Access Collection |
| subjects | Bayes Theorem Cancer Computational Biology Computer applications Deoxyribonucleic acid DNA DNA fingerprinting DNA, Neoplasm - genetics genome Genome, Human Genomes High-Throughput Nucleotide Sequencing - statistics & numerical data Humans Mathematical models Models, Genetic Models, Statistical mutagens Mutation neoplasms Neoplasms - genetics Sequence Analysis, DNA - statistics & numerical data Signatures Software somatic mutation |
| title | Computational approaches for discovery of mutational signatures in cancer |
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