Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study.
Identifieur interne : 000444 ( PubMed/Curation ); précédent : 000443; suivant : 000445Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study.
Auteurs : Peng Jiang [République populaire de Chine] ; Yaofei Hu [République populaire de Chine] ; Yiqi Wang [République populaire de Chine] ; Jin Zhang [République populaire de Chine] ; Qinghong Zhu [République populaire de Chine] ; Lin Bai [République populaire de Chine] ; Qiang Tong [République populaire de Chine] ; Tao Li [République populaire de Chine] ; Liang Zhao [République populaire de Chine]Source :
- Frontiers in genetics [ 1664-8021 ] ; 2019.
Abstract
Ventricular septal defect (VSD) is a fatal congenital heart disease showing severe consequence in affected infants. Early diagnosis plays an important role, particularly through genetic variants. Existing panel-based approaches of variants mining suffer from shortage of large panels, costly sequencing, and missing rare variants. Although a trio-based method alleviates these limitations to some extent, it is agnostic to novel mutations and computational intensive. Considering these limitations, we are studying a novel variants mining algorithm from trio-based sequencing data and apply it on a VSD trio to identify associated mutations. Our approach starts with irrelevant k-mer filtering from sequences of a trio via a newly conceived coupled Bloom Filter, then corrects sequencing errors by using a statistical approach and extends kept k-mers into long sequences. These extended sequences are used as input for variants needed. Later, the obtained variants are comprehensively analyzed against existing databases to mine VSD-related mutations. Experiments show that our trio-based algorithm narrows down candidate coding genes and lncRNAs by about 10- and 5-folds comparing with single sequence-based approaches, respectively. Meanwhile, our algorithm is 10 times faster and 2 magnitudes memory-frugal compared with existing state-of-the-art approach. By applying our approach to a VSD trio, we fish out an unreported gene-CD80, a combination of two genes-MYBPC3 and TRDN and a lncRNA-NONHSAT096266.2, which are highly likely to be VSD-related.
DOI: 10.3389/fgene.2019.00670
PubMed: 31440271
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Medicine, Shiyan</wicri:regionArea></affiliation></author><author><name sortKey="Zhao, Liang" sort="Zhao, Liang" uniqKey="Zhao L" first="Liang" last="Zhao">Liang Zhao</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:31440271</idno><idno type="pmid">31440271</idno><idno type="doi">10.3389/fgene.2019.00670</idno><idno type="wicri:Area/PubMed/Corpus">000444</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000444</idno><idno type="wicri:Area/PubMed/Curation">000444</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000444</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study.</title><author><name sortKey="Jiang, Peng" sort="Jiang, Peng" uniqKey="Jiang P" first="Peng" last="Jiang">Peng Jiang</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author><author><name sortKey="Hu, Yaofei" sort="Hu, Yaofei" uniqKey="Hu Y" first="Yaofei" last="Hu">Yaofei Hu</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author><author><name sortKey="Wang, Yiqi" sort="Wang, Yiqi" uniqKey="Wang Y" first="Yiqi" last="Wang">Yiqi Wang</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author><author><name sortKey="Zhang, Jin" sort="Zhang, Jin" uniqKey="Zhang J" first="Jin" last="Zhang">Jin Zhang</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author><author><name sortKey="Zhu, Qinghong" sort="Zhu, Qinghong" uniqKey="Zhu Q" first="Qinghong" last="Zhu">Qinghong Zhu</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author><author><name sortKey="Bai, Lin" sort="Bai, Lin" uniqKey="Bai L" first="Lin" last="Bai">Lin Bai</name><affiliation wicri:level="1"><nlm:affiliation>School of Computing and Electronic Information, Guangxi University, Nanning, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Computing and Electronic Information, Guangxi University, Nanning</wicri:regionArea></affiliation></author><author><name sortKey="Tong, Qiang" sort="Tong, Qiang" uniqKey="Tong Q" first="Qiang" last="Tong">Qiang Tong</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author><author><name sortKey="Li, Tao" sort="Li, Tao" uniqKey="Li T" first="Tao" last="Li">Tao Li</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author><author><name sortKey="Zhao, Liang" sort="Zhao, Liang" uniqKey="Zhao L" first="Liang" last="Zhao">Liang Zhao</name><affiliation wicri:level="1"><nlm:affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in genetics</title><idno type="ISSN">1664-8021</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">Ventricular septal defect (VSD) is a fatal congenital heart disease showing severe consequence in affected infants. Early diagnosis plays an important role, particularly through genetic variants. Existing panel-based approaches of variants mining suffer from shortage of large panels, costly sequencing, and missing rare variants. Although a trio-based method alleviates these limitations to some extent, it is agnostic to novel mutations and computational intensive. Considering these limitations, we are studying a novel variants mining algorithm from trio-based sequencing data and apply it on a VSD trio to identify associated mutations. Our approach starts with irrelevant <i>k</i>-mer filtering from sequences of a trio <i>via</i> a newly conceived coupled Bloom Filter, then corrects sequencing errors by using a statistical approach and extends kept <i>k</i>-mers into long sequences. These extended sequences are used as input for variants needed. Later, the obtained variants are comprehensively analyzed against existing databases to mine VSD-related mutations. Experiments show that our trio-based algorithm narrows down candidate coding genes and lncRNAs by about 10- and 5-folds comparing with single sequence-based approaches, respectively. Meanwhile, our algorithm is 10 times faster and 2 magnitudes memory-frugal compared with existing state-of-the-art approach. By applying our approach to a VSD trio, we fish out an unreported gene-CD80, a combination of two genes-MYBPC3 and TRDN and a lncRNA-NONHSAT096266.2, which are highly likely to be VSD-related.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">31440271</PMID><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1664-8021</ISSN><JournalIssue CitedMedium="Print"><Volume>10</Volume><PubDate><Year>2019</Year></PubDate></JournalIssue><Title>Frontiers in genetics</Title><ISOAbbreviation>Front Genet</ISOAbbreviation></Journal><ArticleTitle>Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study.</ArticleTitle><Pagination><MedlinePgn>670</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fgene.2019.00670</ELocationID><Abstract><AbstractText>Ventricular septal defect (VSD) is a fatal congenital heart disease showing severe consequence in affected infants. Early diagnosis plays an important role, particularly through genetic variants. Existing panel-based approaches of variants mining suffer from shortage of large panels, costly sequencing, and missing rare variants. Although a trio-based method alleviates these limitations to some extent, it is agnostic to novel mutations and computational intensive. Considering these limitations, we are studying a novel variants mining algorithm from trio-based sequencing data and apply it on a VSD trio to identify associated mutations. Our approach starts with irrelevant <i>k</i>-mer filtering from sequences of a trio <i>via</i> a newly conceived coupled Bloom Filter, then corrects sequencing errors by using a statistical approach and extends kept <i>k</i>-mers into long sequences. These extended sequences are used as input for variants needed. Later, the obtained variants are comprehensively analyzed against existing databases to mine VSD-related mutations. Experiments show that our trio-based algorithm narrows down candidate coding genes and lncRNAs by about 10- and 5-folds comparing with single sequence-based approaches, respectively. Meanwhile, our algorithm is 10 times faster and 2 magnitudes memory-frugal compared with existing state-of-the-art approach. By applying our approach to a VSD trio, we fish out an unreported gene-CD80, a combination of two genes-MYBPC3 and TRDN and a lncRNA-NONHSAT096266.2, which are highly likely to be VSD-related.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Peng</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hu</LastName><ForeName>Yaofei</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yiqi</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Jin</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhu</LastName><ForeName>Qinghong</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bai</LastName><ForeName>Lin</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>School of Computing and Electronic Information, Guangxi University, Nanning, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tong</LastName><ForeName>Qiang</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Tao</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Liang</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of Computing and Electronic Information, Guangxi University, Nanning, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>08</Month><Day>08</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Front Genet</MedlineTA><NlmUniqueID>101560621</NlmUniqueID><ISSNLinking>1664-8021</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">association study</Keyword><Keyword MajorTopicYN="N">k-mer filtering</Keyword><Keyword MajorTopicYN="N">long non-coding RNA</Keyword><Keyword MajorTopicYN="N">trio-sequencing</Keyword><Keyword MajorTopicYN="N">variant calling</Keyword><Keyword MajorTopicYN="N">ventricular septal defect</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>12</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>06</Month><Day>27</Day></PubMedPubDate><PubMedPubDate 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