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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<author><name sortKey="Afshar, Pegah Tootoonchi" sort="Afshar, Pegah Tootoonchi" uniqKey="Afshar P" first="Pegah Tootoonchi" last="Afshar">Pegah Tootoonchi Afshar</name>
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<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>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<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 . </pmc-comment>
<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>
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