Rapid identification of human-infecting viruses.
Identifieur interne : 000464 ( PubMed/Corpus ); précédent : 000463; suivant : 000465Rapid identification of human-infecting viruses.
Auteurs : Zheng Zhang ; Zena Cai ; Zhiying Tan ; Congyu Lu ; Taijiao Jiang ; Gaihua Zhang ; Yousong PengSource :
- Transboundary and emerging diseases [ 1865-1682 ] ; 2019.
English descriptors
- KwdEn :
- MESH :
- chemical , blood : DNA, Viral.
- genetics : Viruses.
- virology : Blood.
- Animals, Cluster Analysis, Genome, Viral, Humans, Machine Learning, Metagenomics.
Abstract
Viruses have caused much mortality and morbidity to humans and pose a serious threat to global public health. The virome with the potential of human infection is still far from complete. Novel viruses have been discovered at an unprecedented pace as the rapid development of viral metagenomics. However, there is still a lack of methodology for rapidly identifying novel viruses with the potential of human infection. This study built several machine learning models to discriminate human-infecting viruses from other viruses based on the frequency of k-mers in the viral genomic sequences. The k-nearest neighbor (KNN) model can predict the human-infecting viruses with an accuracy of over 90%. The performance of this KNN model built on the short contigs (≥1 kb) is comparable to those built on the viral genomes. We used a reported human blood virome to further validate this KNN model with an accuracy of over 80% based on very short raw reads (150 bp). Our work demonstrates a conceptual and generic protocol for the discovery of novel human-infecting viruses in viral metagenomics studies.
DOI: 10.1111/tbed.13314
PubMed: 31373773
Links to Exploration step
pubmed:31373773Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Rapid identification of human-infecting viruses.</title><author><name sortKey="Zhang, Zheng" sort="Zhang, Zheng" uniqKey="Zhang Z" first="Zheng" last="Zhang">Zheng Zhang</name><affiliation><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Cai, Zena" sort="Cai, Zena" uniqKey="Cai Z" first="Zena" last="Cai">Zena Cai</name><affiliation><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Tan, Zhiying" sort="Tan, Zhiying" uniqKey="Tan Z" first="Zhiying" last="Tan">Zhiying Tan</name><affiliation><nlm:affiliation>College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Lu, Congyu" sort="Lu, Congyu" uniqKey="Lu C" first="Congyu" last="Lu">Congyu Lu</name><affiliation><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Jiang, Taijiao" sort="Jiang, Taijiao" uniqKey="Jiang T" first="Taijiao" last="Jiang">Taijiao Jiang</name><affiliation><nlm:affiliation>Suzhou Institute of Systems Medicine, Suzhou, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Gaihua" sort="Zhang, Gaihua" uniqKey="Zhang G" first="Gaihua" last="Zhang">Gaihua Zhang</name><affiliation><nlm:affiliation>College of Life Sciences, Hunan Normal University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Peng, Yousong" sort="Peng, Yousong" uniqKey="Peng Y" first="Yousong" last="Peng">Yousong Peng</name><affiliation><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:31373773</idno><idno type="pmid">31373773</idno><idno type="doi">10.1111/tbed.13314</idno><idno type="wicri:Area/PubMed/Corpus">000464</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000464</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Rapid identification of human-infecting viruses.</title><author><name sortKey="Zhang, Zheng" sort="Zhang, Zheng" uniqKey="Zhang Z" first="Zheng" last="Zhang">Zheng Zhang</name><affiliation><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Cai, Zena" sort="Cai, Zena" uniqKey="Cai Z" first="Zena" last="Cai">Zena Cai</name><affiliation><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Tan, Zhiying" sort="Tan, Zhiying" uniqKey="Tan Z" first="Zhiying" last="Tan">Zhiying Tan</name><affiliation><nlm:affiliation>College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Lu, Congyu" sort="Lu, Congyu" uniqKey="Lu C" first="Congyu" last="Lu">Congyu Lu</name><affiliation><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Jiang, Taijiao" sort="Jiang, Taijiao" uniqKey="Jiang T" first="Taijiao" last="Jiang">Taijiao Jiang</name><affiliation><nlm:affiliation>Suzhou Institute of Systems Medicine, Suzhou, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Gaihua" sort="Zhang, Gaihua" uniqKey="Zhang G" first="Gaihua" last="Zhang">Gaihua Zhang</name><affiliation><nlm:affiliation>College of Life Sciences, Hunan Normal University, Changsha, China.</nlm:affiliation></affiliation></author><author><name sortKey="Peng, Yousong" sort="Peng, Yousong" uniqKey="Peng Y" first="Yousong" last="Peng">Yousong Peng</name><affiliation><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Transboundary and emerging diseases</title><idno type="eISSN">1865-1682</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Blood (virology)</term><term>Cluster Analysis</term><term>DNA, Viral (blood)</term><term>Genome, Viral</term><term>Humans</term><term>Machine Learning</term><term>Metagenomics</term><term>Viruses (genetics)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="blood" xml:lang="en"><term>DNA, Viral</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Viruses</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Blood</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Cluster Analysis</term><term>Genome, Viral</term><term>Humans</term><term>Machine Learning</term><term>Metagenomics</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Viruses have caused much mortality and morbidity to humans and pose a serious threat to global public health. The virome with the potential of human infection is still far from complete. Novel viruses have been discovered at an unprecedented pace as the rapid development of viral metagenomics. However, there is still a lack of methodology for rapidly identifying novel viruses with the potential of human infection. This study built several machine learning models to discriminate human-infecting viruses from other viruses based on the frequency of k-mers in the viral genomic sequences. The k-nearest neighbor (KNN) model can predict the human-infecting viruses with an accuracy of over 90%. The performance of this KNN model built on the short contigs (≥1 kb) is comparable to those built on the viral genomes. We used a reported human blood virome to further validate this KNN model with an accuracy of over 80% based on very short raw reads (150 bp). Our work demonstrates a conceptual and generic protocol for the discovery of novel human-infecting viruses in viral metagenomics studies.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">31373773</PMID><DateCompleted><Year>2020</Year><Month>02</Month><Day>13</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1865-1682</ISSN><JournalIssue CitedMedium="Internet"><Volume>66</Volume><Issue>6</Issue><PubDate><Year>2019</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Transboundary and emerging diseases</Title><ISOAbbreviation>Transbound Emerg Dis</ISOAbbreviation></Journal><ArticleTitle>Rapid identification of human-infecting viruses.</ArticleTitle><Pagination><MedlinePgn>2517-2522</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/tbed.13314</ELocationID><Abstract><AbstractText>Viruses have caused much mortality and morbidity to humans and pose a serious threat to global public health. The virome with the potential of human infection is still far from complete. Novel viruses have been discovered at an unprecedented pace as the rapid development of viral metagenomics. However, there is still a lack of methodology for rapidly identifying novel viruses with the potential of human infection. This study built several machine learning models to discriminate human-infecting viruses from other viruses based on the frequency of k-mers in the viral genomic sequences. The k-nearest neighbor (KNN) model can predict the human-infecting viruses with an accuracy of over 90%. The performance of this KNN model built on the short contigs (≥1 kb) is comparable to those built on the viral genomes. We used a reported human blood virome to further validate this KNN model with an accuracy of over 80% based on very short raw reads (150 bp). Our work demonstrates a conceptual and generic protocol for the discovery of novel human-infecting viruses in viral metagenomics studies.</AbstractText><CopyrightInformation>© 2019 Blackwell Verlag GmbH.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Zheng</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>College of Biology, Hunan University, Changsha, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cai</LastName><ForeName>Zena</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>College of Biology, Hunan University, Changsha, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tan</LastName><ForeName>Zhiying</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lu</LastName><ForeName>Congyu</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>College of Biology, Hunan University, Changsha, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Taijiao</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Suzhou Institute of Systems Medicine, Suzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Gaihua</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>College of Life Sciences, Hunan Normal University, Changsha, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Peng</LastName><ForeName>Yousong</ForeName><Initials>Y</Initials><Identifier Source="ORCID">https://orcid.org/0000-0002-5482-9506</Identifier><AffiliationInfo><Affiliation>College of Biology, Hunan University, Changsha, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>2016YFD0500300</GrantID><Agency>National Key Plan for Scientific Research and Development of China</Agency><Country></Country></Grant><Grant><GrantID>2018JJ3039</GrantID><Agency>Hunan Provincial Natural Science Foundation of China</Agency><Country></Country></Grant><Grant><GrantID>kq1801040</GrantID><Agency>Changsha Science and Technology Bureau</Agency><Country></Country></Grant><Grant><GrantID>31500126</GrantID><Agency>National Natural Science Foundation of China</Agency><Country></Country></Grant><Grant><GrantID>31671371</GrantID><Agency>National Natural Science Foundation of China</Agency><Country></Country></Grant><Grant><GrantID>2016-I2M-1-005</GrantID><Agency>Chinese Academy of Medical Sciences</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>08</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>Germany</Country><MedlineTA>Transbound Emerg Dis</MedlineTA><NlmUniqueID>101319538</NlmUniqueID><ISSNLinking>1865-1674</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D004279">DNA, Viral</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001769" MajorTopicYN="N">Blood</DescriptorName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004279" MajorTopicYN="N">DNA, Viral</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016679" MajorTopicYN="Y">Genome, Viral</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="N">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D056186" MajorTopicYN="N">Metagenomics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014780" MajorTopicYN="N">Viruses</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">human-infecting virus</Keyword><Keyword MajorTopicYN="N">machine learning</Keyword><Keyword MajorTopicYN="N">viral metagenomics</Keyword><Keyword 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