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
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<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>
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