Evidence map›Paper›PMID 40708049›Full record

ArticleMicrobiome2025

Bidirectional subsethood of shared marker profiles enables accurate virus classification.

Christopher Riccardi, Yuqiu Wang, Shibu Yooseph, Fengzhu Sun

Abstract readVideo-Audio Media
PubMed Full text at PMC Publisher
In one paragraph

Article in Microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Christopher Riccardi *Quantitative and Computational Biology Department, University of Southern California, 1050 Childs Way, Los Angeles, 90089, CA, USA.
Yuqiu Wang *Quantitative and Computational Biology Department, University of Southern California, 1050 Childs Way, Los Angeles, 90089, CA, USA.
Shibu YoosephKravis Department of Integrated Sciences, Claremont McKenna College, Claremont, 91711, CA, USA.
Fengzhu SunQuantitative and Computational Biology Department, University of Southern California, 1050 Childs Way, Los Angeles, 90089, CA, USA. fsun@usc.edu.

Funding

National Science Foundation EF-2125142
6 · The paper itself

Abstract

backgroundDue to the impact of viral metagenomic sequencing, the official virus taxonomy is updated several times a year, with labels being renamed even substantially across releases. While this helps reveal newer aspects on the classification of viruses, existing bioinformatic methods for classification struggle to stay in sync with this ever-improving resource.

resultsWe developed a new computer program, named VIRGO, that is able to correctly predict virus families from metagenomic data with an F1 score above 0.9 using a novel viral sequence similarity metric proposed in this work. Moreover, it ensures compatibility with any version of the official taxonomy of viruses.

conclusionsVirgo is designed to easily incorporate newer releases of the official taxonomy, thus representing a valuable resource in the virology community while raising awareness to develop computational methods that evolve alongside manually curated resources. Video Abstract.

Indexed as

Computational BiologyMetagenomicsSoftwareVirusesGenome, ViralHumansPhylogenyBidirectional subsethoodClassification and taxonomyICTVMetagenomicsSoftwareVirology

Identifiers

PMID40708049
PMCPMC12291348

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.