Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants
Identifieur interne : 001231 ( Pmc/Curation ); précédent : 001230; suivant : 001232Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants
Auteurs : Daniel S. Standage [États-Unis] ; C. Titus Brown [États-Unis] ; Fereydoun Hormozdiari [États-Unis]Source :
- iScience [ 2589-0042 ] ; 2019.
Abstract
Url:
DOI: 10.1016/j.isci.2019.07.032
PubMed: 31377530
PubMed Central: 6682328
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Variants</title>
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<affiliation wicri:level="1"><nlm:aff id="aff4">Biochemistry and Molecular Medicine, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA</nlm:aff>
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<wicri:regionArea>Biochemistry and Molecular Medicine, University of California, Davis, 1 Shields Avenue, Davis, CA 95616</wicri:regionArea>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Kevlar: A Mapping-Free Framework for Accurate Discovery of <italic>De Novo</italic>
Variants</title>
<author><name sortKey="Standage, Daniel S" sort="Standage, Daniel S" uniqKey="Standage D" first="Daniel S." last="Standage">Daniel S. Standage</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Population Health and Reproduction, University of California, Davis, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Population Health and Reproduction, University of California, Davis</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Brown, C Titus" sort="Brown, C Titus" uniqKey="Brown C" first="C. Titus" last="Brown">C. Titus Brown</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Population Health and Reproduction, University of California, Davis, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Population Health and Reproduction, University of California, Davis</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2">Genome Center, University of California, Davis, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Genome Center, University of California, Davis</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Hormozdiari, Fereydoun" sort="Hormozdiari, Fereydoun" uniqKey="Hormozdiari F" first="Fereydoun" last="Hormozdiari">Fereydoun Hormozdiari</name>
<affiliation wicri:level="1"><nlm:aff id="aff2">Genome Center, University of California, Davis, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Genome Center, University of California, Davis</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff3">MIND Institute, University of California, Davis, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>MIND Institute, University of California, Davis</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff4">Biochemistry and Molecular Medicine, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Biochemistry and Molecular Medicine, University of California, Davis, 1 Shields Avenue, Davis, CA 95616</wicri:regionArea>
</affiliation>
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</analytic>
<series><title level="j">iScience</title>
<idno type="eISSN">2589-0042</idno>
<imprint><date when="2019">2019</date>
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<front><div type="abstract" xml:lang="en"><title>Summary</title>
<p><italic>De novo</italic>
genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and <italic>de novo</italic>
variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for <italic>de novo</italic>
variant prediction. Kevlar is a mapping-free method for <italic>de novo</italic>
variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance <italic>k</italic>
-mers unique to the individual of interest. Reads containing these <italic>k</italic>
-mers are partitioned into disjoint sets by shared <italic>k</italic>
-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both <italic>de novo</italic>
single-nucleotide variants and indels with high accuracy.</p>
</div>
</front>
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</TEI>
<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">iScience</journal-id>
<journal-id journal-id-type="iso-abbrev">iScience</journal-id>
<journal-title-group><journal-title>iScience</journal-title>
</journal-title-group>
<issn pub-type="epub">2589-0042</issn>
<publisher><publisher-name>Elsevier</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">31377530</article-id>
<article-id pub-id-type="pmc">6682328</article-id>
<article-id pub-id-type="publisher-id">S2589-0042(19)30259-7</article-id>
<article-id pub-id-type="doi">10.1016/j.isci.2019.07.032</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Kevlar: A Mapping-Free Framework for Accurate Discovery of <italic>De Novo</italic>
Variants</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Standage</surname>
<given-names>Daniel S.</given-names>
</name>
<email>daniel.standage@nbacc.dhs.gov</email>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="fn1" ref-type="fn">5</xref>
<xref rid="cor1" ref-type="corresp">∗</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Brown</surname>
<given-names>C. Titus</given-names>
</name>
<email>ctbrown@ucdavis.edu</email>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="cor2" ref-type="corresp">∗∗</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Hormozdiari</surname>
<given-names>Fereydoun</given-names>
</name>
<email>fhormozd@ucdavis.edu</email>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="aff4" ref-type="aff">4</xref>
<xref rid="fn2" ref-type="fn">6</xref>
<xref rid="cor3" ref-type="corresp">∗∗∗</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
Population Health and Reproduction, University of California, Davis, USA</aff>
<aff id="aff2"><label>2</label>
Genome Center, University of California, Davis, USA</aff>
<aff id="aff3"><label>3</label>
MIND Institute, University of California, Davis, USA</aff>
<aff id="aff4"><label>4</label>
Biochemistry and Molecular Medicine, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA</aff>
<author-notes><corresp id="cor1"><label>∗</label>
Corresponding author <email>daniel.standage@nbacc.dhs.gov</email>
</corresp>
<corresp id="cor2"><label>∗∗</label>
Corresponding author <email>ctbrown@ucdavis.edu</email>
</corresp>
<corresp id="cor3"><label>∗∗∗</label>
Corresponding author <email>fhormozd@ucdavis.edu</email>
</corresp>
<fn id="fn1"><label>5</label>
<p id="ntpara0010">Present address: National Biodefense Analysis and Countermeasures Center, Fort Detrick, MD 21702, USA</p>
</fn>
<fn id="fn2"><label>6</label>
<p id="ntpara0015">Lead Contact</p>
</fn>
</author-notes>
<pub-date pub-type="pmc-release"><day>23</day>
<month>7</month>
<year>2019</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="collection"><day>30</day>
<month>8</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub"><day>23</day>
<month>7</month>
<year>2019</year>
</pub-date>
<volume>18</volume>
<fpage>28</fpage>
<lpage>36</lpage>
<history><date date-type="received"><day>11</day>
<month>2</month>
<year>2019</year>
</date>
<date date-type="rev-recd"><day>24</day>
<month>6</month>
<year>2019</year>
</date>
<date date-type="accepted"><day>19</day>
<month>7</month>
<year>2019</year>
</date>
</history>
<permissions><copyright-statement>© 2019 The Authors</copyright-statement>
<copyright-year>2019</copyright-year>
<license license-type="CC BY-NC-ND" xlink:href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><license-p>This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).</license-p>
</license>
</permissions>
<abstract id="abs0010"><title>Summary</title>
<p><italic>De novo</italic>
genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and <italic>de novo</italic>
variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for <italic>de novo</italic>
variant prediction. Kevlar is a mapping-free method for <italic>de novo</italic>
variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance <italic>k</italic>
-mers unique to the individual of interest. Reads containing these <italic>k</italic>
-mers are partitioned into disjoint sets by shared <italic>k</italic>
-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both <italic>de novo</italic>
single-nucleotide variants and indels with high accuracy.</p>
</abstract>
<abstract abstract-type="graphical" id="abs0015"><title>Graphical Abstract</title>
<fig id="undfig1" position="anchor"><graphic xlink:href="fx1"></graphic>
</fig>
</abstract>
<abstract abstract-type="author-highlights" id="abs0020"><title>Highlights</title>
<p><list list-type="simple" id="ulist0010"><list-item id="u0010"><label>•</label>
<p id="p0010">Method for discovery of <italic>de novo</italic>
variants without mapping reads to a reference genome</p>
</list-item>
<list-item id="u0015"><label>•</label>
<p id="p0015">Novel probabilistic score for ranking variant predictions as confidently <italic>de novo</italic>
</p>
</list-item>
<list-item id="u0020"><label>•</label>
<p id="p0020">Predicts <italic>de novo</italic>
SNVs, indels, and structural variants with high accuracy</p>
</list-item>
<list-item id="u0025"><label>•</label>
<p id="p0025">Higher accuracy than competing methods for predicting long (>100 bp) variants</p>
</list-item>
</list>
</p>
</abstract>
<abstract abstract-type="teaser" id="abs0025"><p>Bioinformatics; Biological Sciences; Genetics</p>
</abstract>
<kwd-group id="kwrds0010"><title>Subject Areas</title>
<kwd>Bioinformatics</kwd>
<kwd>Biological Sciences</kwd>
<kwd>Genetics</kwd>
</kwd-group>
</article-meta>
<notes><p id="misc0010">Published: August 30, 2019</p>
</notes>
</front>
</pmc>
</record>
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