Evidence map›Paper›PMID 38866550›Full record

ArticleGenome research2024

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA.

Lars Gabriel, Tomáš Brůna, Katharina J Hoff, Matthis Ebel, Alexandre Lomsadze, Mark Borodovsky, Mario Stanke

Abstract read
In one paragraph

Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 556 papers.

0numbers the graph read from it
0cells of the map it votes in
556citing 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

556 citing papers in PubMed.

  1. Article
  2. DetectNAR genomics and bioinformatics · 2026
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  14. Review
  15. Genome analysis of the glycosphingolipid-producing green alga tetraselmis sp. NKG400013.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026
    Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Lars GabrielInstitute of Mathematics and Computer Science, University of Greifswald, 17489 Greifswald, Germany.
Tomáš BrůnaU.S. Department of Energy, Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
Katharina J HoffInstitute of Mathematics and Computer Science, University of Greifswald, 17489 Greifswald, Germany; katharina.hoff@uni-greifswald.de alexandre.lomsadze@bme.gatech.edu.
Matthis EbelInstitute of Mathematics and Computer Science, University of Greifswald, 17489 Greifswald, Germany.
Alexandre LomsadzeWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA; katharina.hoff@uni-greifswald.de alexandre.lomsadze@bme.gatech.edu.ORCID 0000-0003-0491-1648
Mark Borodovsky *Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Mario Stanke *Institute of Mathematics and Computer Science, University of Greifswald, 17489 Greifswald, Germany.ORCID 0000-0001-8696-0384

Funding

NIGMS Administrative Supplements to Support Undergraduate Summer ResearchR01GM128145 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI BORODOVSKY, MARK · 2018 to 2021
$1.1M
NIGMS NIH HHS R01 GM128145
6 · The paper itself

Abstract

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

Indexed as

Molecular Sequence AnnotationSoftwareAlgorithmsAnimalsComputational BiologyGenomeGenomicsHumansRNA-SeqTranscriptome

Identifiers

PMID38866550
PMCPMC11216308

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.