Evidence map›Paper›PMID 38622738›Full record

ArticleGenome biology2024

Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes.

Ling-Yi Wu, Yasas Wijesekara, Gonçalo J Piedade, Nikolaos Pappas, Corina P D Brussaard, Bas E Dutilh

Abstract read
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

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  7. A genomic view of Earth's biomes.Nature reviews. Genetics · 2026
    Review
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  20. Viral metagenomic analysis of fecal samples fromFrontiers in cellular and infection microbiology · 2025
    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

6 authors.

Ling-Yi WuTheoretical Biology and Bioinformatics, Science4Life, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands.
Yasas WijesekaraInstitute of Bioinformatics, University Medicine Greifswald, Felix Hausdorff Str. 8, 17475, Greifswald, Germany.
Gonçalo J PiedadeDepartment Marine Microbiology and Biogeochemistry, NIOZ Royal Netherlands Institute for Sea Research, Den Burg, PO Box 59, Texel, 1790 AB, The Netherlands.
Nikolaos PappasTheoretical Biology and Bioinformatics, Science4Life, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands.
Corina P D BrussaardDepartment Marine Microbiology and Biogeochemistry, NIOZ Royal Netherlands Institute for Sea Research, Den Burg, PO Box 59, Texel, 1790 AB, The Netherlands.
Bas E DutilhTheoretical Biology and Bioinformatics, Science4Life, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands. bedutilh@gmail.com.ORCID http://orcid.org/0000-0003-2329-7890

Funding

Deutsche Forschungsgemeinschaft EXC 2051 - Project-ID 390713860European Research Council 865694H2020 European Research Council Consolidator grant 865694H2020 Marie Skłodowska-Curie Actions 955974Nederlandse Organisatie voor Wetenschappelijk Onderzoek grant ALWPP.2016.019
6 · The paper itself

Abstract

backgroundAs most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance.

resultsWe compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0-97%) and false positive rates (0-30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered.

conclusionsTogether, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

Indexed as

VirusesComputational BiologyDatabases, GeneticEcosystemMetagenomeMetagenomics

Identifiers

PMID38622738
PMCPMC11020464

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Registered trials

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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.