Evidence map›Paper›PMID 40824055›Full record

ReviewMicrobiology and molecular biology reviews : MMBR2025

Computational function prediction of bacteria and phage proteins.

Susanna R Grigson, George Bouras, Bas E Dutilh, Robert D Olson, Robert A Edwards

Abstract readReview
In one paragraph

Review in Microbiology and molecular biology reviews : MMBR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. LAMBDA: A Prophage Detection Benchmark for Genomic Language Models.bioRxiv : the preprint server for biology · 2026
    Article
  8. Review
  9. Review
  10. Review
  11. 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

5 authors.

Susanna R GrigsonFlinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.ORCID 0000-0003-4738-3451
George BourasAdelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.ORCID 0000-0002-5885-4186
Bas E DutilhInstitute of Biodiversity, Ecology, and Evolution; Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany.ORCID 0000-0003-2329-7890
Robert D OlsonConsortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, USA.
Robert A EdwardsFlinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.ORCID 0000-0001-8383-8949

Funding

Computational and Experimental Resources for Virome Analysis in Inflammatory Bowel Disease (CERVAID)RC2DK116713 · NIDDK · WASHINGTON UNIVERSITY · PI WANG, DAVID · 2019 to 2023
$8.9M
Australian Research Council DP220102915Australian Research Council DP250103825Australian Research Council FL250100019Deutsche Forschungsgemeinschaft 390713860European Research Council 865694NIDDK NIH HHS RC2 DK116713NIDDK NIH HHS RC2DK116713
6 · The paper itself

Abstract

SUMMARYUnderstanding protein functions is crucial for interpreting microbial life; however, reliable function annotation remains a major challenge in computational biology. Despite significant advances in bioinformatics methods, ~30% of all bacterial and ~65% of all bacteriophage (phage) protein sequences cannot be confidently annotated. In this review, we examine state-of-the-art bioinformatics tools and methodologies for annotating bacterial and phage proteins, particularly those of unknown or poorly characterized function. We describe the process of identifying protein-coding regions and the systems to classify protein functionalities. Additionally, we explore a range of protein annotation methods, from traditional homology-based methods to cutting-edge machine learning models. In doing so, we provide a toolbox for confidently annotating previously unknown bacterial and phage proteins, advancing the discovery of novel functions and our understanding of microbial systems.

Indexed as

BacteriaBacterial ProteinsBacteriophagesComputational BiologyViral ProteinsMachine LearningMolecular Sequence AnnotationBacterial ProteinsViral Proteinsbioinformaticsfunction predictionmachine learningmicrobial proteins

Identifiers

PMID40824055
PMCPMC12462290

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.