Rapid identification of human-infecting viruses.
Transbound Emerg Dis
; 66(6): 2517-2522, 2019 Nov.
Article
en En
| MEDLINE
| ID: mdl-31373773
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Virus
/
Genoma Viral
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Transbound Emerg Dis
Asunto de la revista:
MEDICINA VETERINARIA
Año:
2019
Tipo del documento:
Article
País de afiliación:
China