Leveraging machine learning for taxonomic classification of emerging astroviruses

• Published in: Frontiers in molecular biosciences Vol. 10; p. 1305506
• Main Authors: Alipour, Fatemeh, Holmes, Connor, Lu, Yang Young, Hill, Kathleen A, Kari, Lila
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 11-01-2024
• Subjects: alignment-free classification; Avastrovirus; family Astroviridae; machine learning; Mamastrovirus; Molecular Biosciences; viral classification and clustering; Avastrovirus; Mamastrovirus; genomic signature; alignment-free classification; machine learning; family Astroviridae; viral classification and clustering; k-mer frequency
• ISSN: 2296-889X; 2296-889X
• DOI: 10.3389/fmolb.2023.1305506

Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera,
and
However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence
-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (
) subgenus of the genus
, and a goose astrovirus (
) subgenus of the genus
. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.