Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.
Identifieur interne : 000463 ( PubMed/Curation ); précédent : 000462; suivant : 000464Kevlar: 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
De novo 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 de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-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 de novo single-nucleotide variants and indels with high accuracy.
DOI: 10.1016/j.isci.2019.07.032
PubMed: 31377530
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Standage</name><affiliation wicri:level="1"><nlm:affiliation>Population Health and Reproduction, University of California, Davis, USA. Electronic address: daniel.standage@nbacc.dhs.gov.</nlm:affiliation><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:affiliation>Population Health and Reproduction, University of California, Davis, USA; Genome Center, University of California, Davis, USA. Electronic address: ctbrown@ucdavis.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Population Health and Reproduction, University of California, Davis, USA; 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:affiliation>Genome Center, University of California, Davis, USA; MIND Institute, University of California, Davis, USA; Biochemistry and Molecular Medicine, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA. Electronic address: fhormozd@ucdavis.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Genome Center, University of California, Davis, USA; MIND Institute, University of California, Davis, USA; Biochemistry and Molecular Medicine, University of California, Davis, 1 Shields Avenue, Davis, CA 95616</wicri:regionArea></affiliation></author></analytic><series><title level="j">iScience</title><idno type="eISSN">2589-0042</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">De novo 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 de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-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 de novo single-nucleotide variants and indels with high accuracy.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">31377530</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>16</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2589-0042</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><PubDate><Year>2019</Year><Month>Aug</Month><Day>30</Day></PubDate></JournalIssue><Title>iScience</Title><ISOAbbreviation>iScience</ISOAbbreviation></Journal><ArticleTitle>Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.</ArticleTitle><Pagination><MedlinePgn>28-36</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S2589-0042(19)30259-7</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.isci.2019.07.032</ELocationID><Abstract><AbstractText>De novo 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 de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-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 de novo single-nucleotide variants and indels with high accuracy.</AbstractText><CopyrightInformation>Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Standage</LastName><ForeName>Daniel S</ForeName><Initials>DS</Initials><AffiliationInfo><Affiliation>Population Health and Reproduction, University of California, Davis, USA. Electronic address: daniel.standage@nbacc.dhs.gov.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Brown</LastName><ForeName>C Titus</ForeName><Initials>CT</Initials><AffiliationInfo><Affiliation>Population Health and Reproduction, University of California, Davis, USA; Genome Center, University of California, Davis, USA. 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