Evidence map›Paper›PMID 42399474›Full record

ArticleNature methods2026

Efficient evidence-based genome annotation with EviAnn.

Aleksey V Zimin, Daniela Puiu, Mihaela Pertea, James A Yorke, Steven L Salzberg

Abstract read
PubMed Publisher
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 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
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  4. Chromosome-Scale Genome of Zoonotic EyewormAnimals : an open access journal from MDPI · 2026
    Article
  5. Article
  6. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Aleksey V ZiminDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. alekseyz@jhu.edu.ORCID http://orcid.org/0000-0001-5091-3092
Daniela PuiuDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-2386-9265
Mihaela PerteaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-0762-8637
James A YorkeDepartment of Mathematics, University of Maryland, College Park, MD, USA.
Steven L SalzbergDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-8859-7432

Funding

Computational Methods for Genome Assembly, Transcript Assembly, and Variant DiscoveryR01HG006677 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2011 to 2025
$10.7M
Computational Methods for Microbial and Microbiome Sequence AnalysisR35GM130151 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Steven L. Salzberg · 2019 to 2026
$2.9M
Comprehensive Human Expressed Sequences in Brain (CHESS-BRAIN) and their roles in neuropsychiatric illnessR01MH123567 · NIMH · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2021 to 2025
$2.8M
National Science Foundation (NSF) IOS-2432298U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01HG006677U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01MH123567U.S. Department of Health & Human Services | National Institutes of Health (NIH) R35GM130151U.S. Department of Health & Human Services | National Institutes of Health (NIH) R35-GM156470
6 · The paper itself

Abstract

Machine-learning-based ab initio gene finders have long been central to eukaryotic genome annotation, largely because gene expression data were historically expensive and limited. Modern sequencing technologies have transformed this landscape, making transcript evidence abundant and reliable, while proteins from many well-annotated genomes provide additional rich sources of evidence. Here we address the underutilization of these data by existing annotation tools by developing EviAnn (Evidence-based Annotator), an evidence-based eukaryotic gene annotation system. EviAnn takes a strongly data-driven approach, building exon-intron structures of protein coding and noncoding genes directly from transcript alignments or protein homology rather than relying primarily on ab initio predictions. Using identical input data, EviAnn consistently outperforms leading packages such as BRAKER3, MAKER2 and FINDER, while utilizing considerably less computer time. A mammalian genome can be annotated in under an hour on a single multicore server. EviAnn is open-source software available via GitHub at https://github.com/alekseyzimin/EviAnn_release and through Bioconda as 'eviann'.

Indexed as

GenomeGenomicsMolecular Sequence AnnotationSoftwareAnimalsHumansIntrons

Identifiers

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.