vClassifier: A Toolkit for High-Resolution Phylogenetic Classification of Prokaryotic Viruses
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ID: 315060
2026
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Abstract
Abstract Motivation As the most abundant and diverse biological entities, prokaryotic viruses play pivotal roles in ecological systems. Their taxonomic classification has been instrumental in elucidating their diversity and ecological functions. However, determination of viral taxonomy remains a considerable challenge. Recently developed approaches succeed in assignment of viral taxonomy at higher ranks, such as at the family level and above, but struggle at the subfamily level and below to the genus and species resolutions. Results We describe a phylogeny-informed methodology to provide species-level taxonomic assignments of viruses. We used single-copy marker genes relevant to specific taxa and reference phylogenetic trees for these groups which facilitates direct comparisons with the taxonomic framework of the International Committee on Taxonomy of Viruses (ICTV). Our method demonstrated significant congruence with the ICTV taxonomy, showing 84 − 91% alignment at the subfamily and genus levels. For species-level classification, our strategy was integrated with average nucleotide identity, yielding a high congruence rate of over 92% with the taxonomic data from the NCBI Virus database. This framework is implemented in vClassifier, a high-accuracy toolkit developed for standardized viral taxonomic assignment. Benchmarking comparisons revealed that vClassifier matches or surpasses other available tools regarding assignment rates. By achieving objectivity and high levels of consistency, vClassifier streamlines the taxonomic categorization of prokaryotic viral genomes. Accurate assignments at the subfamily, genus, and species levels will significantly refine the taxonomic resolution of viruses, fostering a deeper understanding of viral diversity in microbiomes and ecosystems. Availability and implementation vClassifier is publicly accessible via https://github.com/AnantharamanLab/vClassifier.
| Reference Key |
openalex_W7162499393
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|---|---|
| Authors | Kun Zhou, James C. Kosmopoulos, Karthik Anantharaman |
| Journal | Bioinformatics advances |
| Year | 2026 |
| DOI |
10.1093/bioadv/vbag149
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| URL | |
| Keywords | Keywords not found |
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