Evidence map›Paper›PMID 42706541›Full record

ArticleGenome biology2026

Large-scale benchmarking of prokaryotic annotation tools across thousands of species.

Mateusz Jundzill, Martin Hölzer, Serghei Mangul, Mike Marquet, Ralf Ehricht, Mara Lohde, Riccardo Spott, Oliwia Makarewicz, Mathias W Pletz, Christian Brandt

Abstract read
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Mateusz JundzillInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, 07747, Germany. mateusz.jundzill@med.uni-jena.de.ORCID https://orcid.org/0009-0000-7264-9165
Martin HölzerBioinformatics and Translational Research, Genome Competence Center, Robert Koch Institute, Berlin, 13353, Germany.
Serghei MangulDepartment of Biological and Morphofunctional Sciences, College of Medicine and Biological Sciences, Stefan Cel Mare University of Suceava, Suceava, 720229, Romania.
Mike MarquetInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, 07747, Germany.
Ralf EhrichtLeibniz Institute of Photonic Technology (IPHT), Jena, 07745, Germany.
Mara LohdeInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, 07747, Germany.
Riccardo SpottInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, 07747, Germany.
Oliwia MakarewiczInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, 07747, Germany.
Mathias W PletzInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, 07747, Germany.
Christian BrandtInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, 07747, Germany.

Funding

German Research Foundation and the Open Access Publication Fund of the Thueringer Universitaets und Landesbibliothek Jena [Projekt-Nr.: 512648189]Innovative molecular and biochemical assays for rapid diagnostics, drug development and new therapy concepts (LPI-BT5) FKZ:13N15720Ministry of Research, Innovation and Digitization under Romania's National Recovery and Resilience Plan - Funded by EU - NextGenerationEU program, project "Artificial intelligence-powered personalized health and genomics libraries for the analysis of long-term effects in COVID-19 patients (AI-PHGL-COVID) 760073/23.05.2023, code 285/30.11.2022, within Pillar III, Component C9, Investment 81University Hospital Jena Young Researchers funding program IZKF Förderkennzeichen: MSP24
6 · The paper itself

Abstract

backgroundGenome annotation is an important step in deriving functional meaning from prokaryotic sequencing data, yet systematic evaluations guiding tool selection are lacking. We present the first large-scale investigation of four prominent open-source annotation tools (Prokka, Bakta, EggNOG-mapper, and PGAP) across 156,033 diverse genomes. This includes Escherichia coli strains for baseline performance, thousands of archaea and bacteria genomes, as well as frameshifted and metagenome-assembled genomes.

resultsBakta excels in annotating high-quality bacterial genomes, while PGAP was better for archaeal genomes and challenging bacterial assemblies, including metagenome-assembled, fragmented, or contaminated samples. For Gene Ontology annotation, PGAP consistently provides broader term coverage, whereas EggNOG-mapper offers more terms per feature.

conclusionsOur findings highlight tool-specific strengths crucial for selecting optimal solutions based on genome quality, taxonomy, and origin (e.g. MAGs). This study provides an evidence-based guide for users and informs future tool development.

Indexed as

Genome, ArchaealGenome, BacterialMolecular Sequence AnnotationSoftwareArchaeaBenchmarkingComputational BiologyGene OntologyMetagenomeArchaeaBacteriaBenchmarkBioinformaticsComputational biologyFunctional annotationGene function predictionGene ontologyGenome annotationMetagenome-assembled genome

Identifiers

PMID42706541
PMCPMC13548610

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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.