Rapid identification of human-infecting viruses.
Identifieur interne : 000410 ( Main/Exploration ); précédent : 000409; suivant : 000411Rapid identification of human-infecting viruses.
Auteurs : Zheng Zhang [République populaire de Chine] ; Zena Cai [République populaire de Chine] ; Zhiying Tan [République populaire de Chine] ; Congyu Lu [République populaire de Chine] ; Taijiao Jiang [République populaire de Chine] ; Gaihua Zhang [République populaire de Chine] ; Yousong Peng [République populaire de Chine]Source :
- Transboundary and emerging diseases [ 1865-1682 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Virus.
- sang : ADN viral.
- virologie : Sang.
- Analyse de regroupements, Animaux, Apprentissage machine, Génome viral, Humains, Métagénomique.
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
Affiliations:
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Le document en format XML
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Changsha</wicri:regionArea><wicri:noRegion>Changsha</wicri:noRegion></affiliation></author><author><name sortKey="Cai, Zena" sort="Cai, Zena" uniqKey="Cai Z" first="Zena" last="Cai">Zena Cai</name><affiliation wicri:level="1"><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Biology, Hunan University, Changsha</wicri:regionArea><wicri:noRegion>Changsha</wicri:noRegion></affiliation></author><author><name sortKey="Tan, Zhiying" sort="Tan, Zhiying" uniqKey="Tan Z" first="Zhiying" last="Tan">Zhiying Tan</name><affiliation wicri:level="1"><nlm:affiliation>College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Computer Science and Electronic Engineering, Hunan University, Changsha</wicri:regionArea><wicri:noRegion>Changsha</wicri:noRegion></affiliation></author><author><name sortKey="Lu, Congyu" sort="Lu, Congyu" uniqKey="Lu C" first="Congyu" last="Lu">Congyu Lu</name><affiliation wicri:level="1"><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Biology, Hunan University, Changsha</wicri:regionArea><wicri:noRegion>Changsha</wicri:noRegion></affiliation></author><author><name sortKey="Jiang, Taijiao" sort="Jiang, Taijiao" uniqKey="Jiang T" first="Taijiao" last="Jiang">Taijiao Jiang</name><affiliation wicri:level="1"><nlm:affiliation>Suzhou Institute of Systems Medicine, Suzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Suzhou Institute of Systems Medicine, Suzhou</wicri:regionArea><wicri:noRegion>Suzhou</wicri:noRegion></affiliation></author><author><name sortKey="Zhang, Gaihua" sort="Zhang, Gaihua" uniqKey="Zhang G" first="Gaihua" last="Zhang">Gaihua Zhang</name><affiliation wicri:level="1"><nlm:affiliation>College of Life Sciences, Hunan Normal University, Changsha, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Life Sciences, Hunan Normal University, Changsha</wicri:regionArea><wicri:noRegion>Changsha</wicri:noRegion></affiliation></author><author><name sortKey="Peng, Yousong" sort="Peng, Yousong" uniqKey="Peng Y" first="Yousong" last="Peng">Yousong Peng</name><affiliation wicri:level="1"><nlm:affiliation>College of Biology, Hunan University, Changsha, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Biology, Hunan University, Changsha</wicri:regionArea><wicri:noRegion>Changsha</wicri:noRegion></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="KwdFr" xml:lang="fr"><term>ADN viral (sang)</term><term>Analyse de regroupements</term><term>Animaux</term><term>Apprentissage machine</term><term>Génome viral</term><term>Humains</term><term>Métagénomique</term><term>Sang (virologie)</term><term>Virus (génétique)</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="génétique" xml:lang="fr"><term>Virus</term></keywords><keywords scheme="MESH" qualifier="sang" xml:lang="fr"><term>ADN viral</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Sang</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><keywords scheme="MESH" xml:lang="fr"><term>Analyse de regroupements</term><term>Animaux</term><term>Apprentissage machine</term><term>Génome viral</term><term>Humains</term><term>Métagénomique</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><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Zhang, Zheng" sort="Zhang, Zheng" uniqKey="Zhang Z" first="Zheng" last="Zhang">Zheng Zhang</name></noRegion><name sortKey="Cai, Zena" sort="Cai, Zena" uniqKey="Cai Z" first="Zena" last="Cai">Zena Cai</name><name sortKey="Jiang, Taijiao" sort="Jiang, Taijiao" uniqKey="Jiang T" first="Taijiao" last="Jiang">Taijiao Jiang</name><name sortKey="Lu, Congyu" sort="Lu, Congyu" uniqKey="Lu C" first="Congyu" last="Lu">Congyu Lu</name><name sortKey="Peng, Yousong" sort="Peng, Yousong" uniqKey="Peng Y" first="Yousong" last="Peng">Yousong Peng</name><name sortKey="Tan, Zhiying" sort="Tan, Zhiying" uniqKey="Tan Z" first="Zhiying" last="Tan">Zhiying Tan</name><name sortKey="Zhang, Gaihua" sort="Zhang, Gaihua" uniqKey="Zhang G" first="Gaihua" last="Zhang">Gaihua Zhang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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