Evidence map›Paper›PMID 41133271›Full record

ArticleNAR genomics and bioinformatics2025

Phage evolutionary relationships emerge from protein language model-based proteome representation.

Swapnesh Panigrahi, Mireille Ansaldi, Nicolas Ginet

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2025. 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

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

1 citing paper in PubMed.

  1. Review
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.

Swapnesh PanigrahiPhage cycle and bacterial metabolism team - Laboratoire de Chimie Bactérienne - UMR7283 CNRS/Aix-Marseille Université, Marseille 13009, France.
Mireille AnsaldiPhage cycle and bacterial metabolism team - Laboratoire de Chimie Bactérienne - UMR7283 CNRS/Aix-Marseille Université, Marseille 13009, France.
Nicolas GinetPhage cycle and bacterial metabolism team - Laboratoire de Chimie Bactérienne - UMR7283 CNRS/Aix-Marseille Université, Marseille 13009, France.ORCID https://orcid.org/0000-0002-5544-4376

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Viral taxonomy is a challenging task due to the propensity of viruses for recombination and the lack of universal gene markers. As a result, recent ICTV updates increasingly rely on multiple tools for taxonomic ranking, with a growing emphasis on proteome-based clustering approaches. At the same time, the rapid expansion of viral datasets presents new challenges in organizing, analysing, and discovering phage relationships at scale. To address these challenges, we introduce hierarchical viruses, a framework for comparative genomics of bacteriophages that leverages protein Language Model (pLM) embeddings to generate proteome-wide vector representations of phages. Clustering the vector representations of 24 362 phages from the curated INPHARED dataset reveals a multi-scale hierarchical organization of phages. This hierarchy aligns with current ICTV taxonomic rankings at the genus and subfamily levels, with an adjusted mutual information score greater than 0.9 for both, in the

Indexed as

BacteriophagesEvolution, MolecularProteomeViral ProteinsPhylogenyProteomeViral Proteins

Identifiers

PMID41133271
PMCPMC12541379

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.