Evidence map›Paper›PMID 41286201›Full record

ArticleNature methods2026

Helixer: ab initio prediction of primary eukaryotic gene models combining deep learning and a hidden Markov model.

Felix Holst, Anthony M Bolger, Felicitas Kindel, Christopher Günther, Janina Maß, Sebastian Triesch, Niklas Kiel, Nima Saadat, Oliver Ebenhöh, Björn Usadel and 4 more

Abstract read
In one paragraph

Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.

0numbers the graph read from it
0cells of the map it votes in
90citing 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

90 citing papers in PubMed.

  1. Article
  2. Chromosome-scale genome assembly and genomic prediction of essential oil compounds in Atractylodes lancea for genomics-assisted breeding.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026
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  11. PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  12. Article
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  14. Article
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  20. Article

30 more citing papers are in PubMed but not listed here.

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

14 authors.

Felix Holst *Institute of Plant Biochemistry, Heinrich Heine University, Düsseldorf, Germany.
Anthony M Bolger *IBG-4 Bioinformatics, Forschungszentrum Jülich, Jülich, Germany.
Felicitas Kindel *Institute of Plant Biochemistry, Heinrich Heine University, Düsseldorf, Germany.ORCID http://orcid.org/0009-0000-6149-0224
Christopher GüntherInstitute of Plant Biochemistry, Heinrich Heine University, Düsseldorf, Germany.
Janina MaßInstitute of Quantitative and Theoretical Biology, Heinrich Heine University, Düsseldorf, Germany.ORCID http://orcid.org/0000-0002-0752-7732
Sebastian TrieschInstitute of Plant Biochemistry, Heinrich Heine University, Düsseldorf, Germany.ORCID http://orcid.org/0000-0001-7796-4631
Niklas KielInstitute of Plant Biochemistry, Heinrich Heine University, Düsseldorf, Germany.
Nima SaadatInstitute of Quantitative and Theoretical Biology, Heinrich Heine University, Düsseldorf, Germany.
Oliver EbenhöhInstitute of Quantitative and Theoretical Biology, Heinrich Heine University, Düsseldorf, Germany.ORCID http://orcid.org/0000-0002-7229-7398
Björn UsadelIBG-4 Bioinformatics, Forschungszentrum Jülich, Jülich, Germany.ORCID http://orcid.org/0000-0003-0921-8041
Rainer SchwackeIBG-4 Bioinformatics, Forschungszentrum Jülich, Jülich, Germany.
Andreas P M WeberInstitute of Plant Biochemistry, Heinrich Heine University, Düsseldorf, Germany.ORCID http://orcid.org/0000-0003-0970-4672
Marie E Bolger *IBG-4 Bioinformatics, Forschungszentrum Jülich, Jülich, Germany. m.bolger@fz-juelich.de.ORCID http://orcid.org/0000-0001-6335-1578
Alisandra K DentonInstitute of Plant Biochemistry, Heinrich Heine University, Düsseldorf, Germany.ORCID http://orcid.org/0000-0002-1132-0224

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) 497667402Deutsche Forschungsgemeinschaft (German Research Foundation) EXC-2048/1-Project ID: 390686111
6 · The paper itself

Abstract

The accurate identification of genes is vital for understanding biological function, yet this remains challenging across many newly sequenced or less-studied species. Here we present Helixer, an artificial intelligence-based tool for ab initio gene prediction that delivers highly accurate gene models across fungal, plant, vertebrate and invertebrate genomes. Unlike traditional methods, Helixer operates without requiring additional experimental data such as RNA sequencing, making it broadly applicable to diverse species. We show that Helixer's pretrained models achieve accuracy on par with or exceeding current tools, producing gene annotations that closely match expert-curated references across multiple evaluation metrics. Its design enables immediate use on genomes without retraining, providing an efficient, accessible solution for genome annotation in both research and applied settings. The tool is available as an open-source software for local installation via GitHub. An online web interface is also available as well as through the Galaxy ToolShed.

Indexed as

Computational BiologyDeep LearningEukaryotaModels, GeneticSoftwareAnimalsGenomicsHidden Markov ModelsMarkov ChainsMolecular Sequence Annotation

Identifiers

PMID41286201
PMCPMC13076211

What OpenQuestion holds

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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.