Evidence map›Paper›PMID 42370331›Full record

ArticleBioinformatics advances2026

vClassifier: a toolkit for high-resolution phylogenetic classification of prokaryotic viruses.

Kun Zhou, James C Kosmopoulos, Karthik Anantharaman

Abstract read
In one paragraph

Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kun ZhouState Key Laboratory of Marine Geology, Tongji University, Shanghai, 200092, China.ORCID https://orcid.org/0009-0006-0572-9361
James C KosmopoulosDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, United States.
Karthik AnantharamanDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID42370331
PMCPMC13303284

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.