Key result
Whole-genome sequencing establishes distinct nucleotide divergence thresholds for classifying VSV species, genotypes, and subtypes.
Why the study?
Accurate classification of vesicular stomatitis virus is critical for epidemiological surveillance, prompting an update to VSV taxonomic organization using whole-genome sequence criteria.
Answers are grounded in this paper and cite their sources.
Quantitative benchmarks for nucleotide divergence provide a precise framework for VSV classification, enhancing genomic surveillance and tracking of viral evolution.
Vesicular stomatitis virus (VSV) causes clinical disease in livestock that mimics Foot-and-Mouth Disease, necessitating its status as a reportable pathogen to the World Organization for Animal Health. Given the importance of accurate classification for epidemiological surveillance, this study aims to update VSV taxonomic organization using whole-genome sequence criteria to better monitor disease dynamics. Using phylogenetic analysis and the Species Demarcation Tool (SDT), we analyzed pairwise identity across publicly available sequences, constructing a rooted maximum likelihood tree to cluster strains based on robust genetic identity scores. The results demonstrate that nucleotide divergences exceeding 30% define distinct species within the Vesiculovirus genus. Within a species, divergences between 10% and 30% successfully delineate genotypes, while differences between 6% and 10% identify specific subtypes. These quantitative benchmarks provide a precise framework for viral classification, significantly enhancing genomic surveillance and the ability to track the evolution and transmission of VSV genotypes and subtypes across diverse geographic regions.
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León et al. (2026) studied Vesicular stomatitis virus (VSV). Whole-genome sequence phylogenetic analysis was evaluated on Taxonomic classification thresholds based on nucleotide divergence. Whole-genome sequence analysis established that nucleotide divergences >30% define VSV species, 10-30% delineate genotypes, and 6-10% identify specific subtypes.
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