COSINE: non-seeding method for mapping long noisy sequences
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Auteurs : Pegah Tootoonchi Afshar [États-Unis] ; Wing Hung Wong [États-Unis]Source :
- Nucleic Acids Research [ 0305-1048 ] ; 2017.
Abstract
Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors. COSINE computes the context similarity of two stretches of nucleobases given the similarity over distributions of their short
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DOI: 10.1093/nar/gkx511
PubMed: 28586438
PubMed Central: 5737678
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">COSINE: non-seeding method for mapping long noisy sequences</title><author><name sortKey="Afshar, Pegah Tootoonchi" sort="Afshar, Pegah Tootoonchi" uniqKey="Afshar P" first="Pegah Tootoonchi" last="Afshar">Pegah Tootoonchi Afshar</name><affiliation wicri:level="1"><nlm:aff id="AFF1">Department of Electrical Engineering, School of Engineering, Stanford University, Stanford, CA 94305, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, School of Engineering, Stanford University, Stanford, CA 94305</wicri:regionArea></affiliation></author><author><name sortKey="Wong, Wing Hung" sort="Wong, Wing Hung" uniqKey="Wong W" first="Wing Hung" last="Wong">Wing Hung Wong</name><affiliation wicri:level="1"><nlm:aff id="AFF2">Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">28586438</idno><idno type="pmc">5737678</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737678</idno><idno type="RBID">PMC:5737678</idno><idno type="doi">10.1093/nar/gkx511</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">000F60</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000F60</idno><idno type="wicri:Area/Pmc/Curation">000F60</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000F60</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">COSINE: non-seeding method for mapping long noisy sequences</title><author><name sortKey="Afshar, Pegah Tootoonchi" sort="Afshar, Pegah Tootoonchi" uniqKey="Afshar P" first="Pegah Tootoonchi" last="Afshar">Pegah Tootoonchi Afshar</name><affiliation wicri:level="1"><nlm:aff id="AFF1">Department of Electrical Engineering, School of Engineering, Stanford University, Stanford, CA 94305, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, School of Engineering, Stanford University, Stanford, CA 94305</wicri:regionArea></affiliation></author><author><name sortKey="Wong, Wing Hung" sort="Wong, Wing Hung" uniqKey="Wong W" first="Wing Hung" last="Wong">Wing Hung Wong</name><affiliation wicri:level="1"><nlm:aff id="AFF2">Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305</wicri:regionArea></affiliation></author></analytic><series><title level="j">Nucleic Acids Research</title><idno type="ISSN">0305-1048</idno><idno type="eISSN">1362-4962</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Abstract</title><p>Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors. COSINE computes the context similarity of two stretches of nucleobases given the similarity over distributions of their short <italic>k</italic>-mers (<italic>k</italic> = 3–4) along the sequences. The results on simulated and real data show that COSINE achieves high sensitivity and specificity under a wide range of read accuracies. When the error rate is high, COSINE can offer substantial advantages over existing alignment methods.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Zhang, J" uniqKey="Zhang J">J. Zhang</name></author><author><name sortKey="Chiodini, R" uniqKey="Chiodini R">R. Chiodini</name></author><author><name sortKey="Badr, A" uniqKey="Badr A">A. Badr</name></author><author><name sortKey="Zhangd, G" uniqKey="Zhangd G">G. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Nucleic Acids Res</journal-id><journal-id journal-id-type="iso-abbrev">Nucleic Acids Res</journal-id><journal-id journal-id-type="publisher-id">nar</journal-id><journal-title-group><journal-title>Nucleic Acids Research</journal-title></journal-title-group><issn pub-type="ppub">0305-1048</issn><issn pub-type="epub">1362-4962</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">28586438</article-id><article-id pub-id-type="pmc">5737678</article-id><article-id pub-id-type="doi">10.1093/nar/gkx511</article-id><article-id pub-id-type="publisher-id">gkx511</article-id><article-categories><subj-group subj-group-type="heading"><subject>Methods Online</subject></subj-group></article-categories><title-group><article-title>COSINE: non-seeding method for mapping long noisy sequences</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Afshar</surname><given-names>Pegah Tootoonchi</given-names></name><xref ref-type="aff" rid="AFF1">1</xref></contrib><contrib contrib-type="author"><name><surname>Wong</surname><given-names>Wing Hung</given-names></name><pmc-comment>whwong@stanford.edu </pmc-comment>
<xref ref-type="aff" rid="AFF2">2</xref><xref ref-type="corresp" rid="COR1"></xref></contrib></contrib-group><aff id="AFF1"><label>1</label>Department of Electrical Engineering, School of Engineering, Stanford University, Stanford, CA 94305, USA</aff><aff id="AFF2"><label>2</label>Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA</aff><author-notes><corresp id="COR1"><label>*</label>To whom correspondence should be addressed. Tel: +1 650 725 2915; Fax: +1 650 725 8977; Email: <email>whwong@stanford.edu</email></corresp></author-notes><pub-date pub-type="ppub"><day>21</day><month>8</month><year>2017</year></pub-date><pub-date pub-type="epub" iso-8601-date="2017-06-06"><day>06</day><month>6</month><year>2017</year></pub-date><pub-date pub-type="pmc-release"><day>06</day><month>6</month><year>2017</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>45</volume><issue>14</issue><fpage>e132</fpage><lpage>e132</lpage><history><date date-type="accepted"><day>04</day><month>6</month><year>2017</year></date><date date-type="rev-recd"><day>16</day><month>5</month><year>2017</year></date><date date-type="received"><day>28</day><month>8</month><year>2016</year></date></history><permissions><copyright-statement>© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.</copyright-statement><copyright-year>2017</copyright-year><license license-type="cc-by-nc" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<uri xlink:href="http://creativecommons.org/licenses/by-nc/4.0/">http://creativecommons.org/licenses/by-nc/4.0/</uri>), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact <email>journals.permissions@oup.com</email></license-p></license></permissions><self-uri xlink:href="gkx511.pdf"></self-uri><abstract><title>Abstract</title><p>Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors. COSINE computes the context similarity of two stretches of nucleobases given the similarity over distributions of their short <italic>k</italic>-mers (<italic>k</italic> = 3–4) along the sequences. The results on simulated and real data show that COSINE achieves high sensitivity and specificity under a wide range of read accuracies. When the error rate is high, COSINE can offer substantial advantages over existing alignment methods.</p></abstract><counts><page-count count="13"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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