galba

galba predicts protein-coding gene structures in novel eukaryotic genomes using high-quality proteins from closely related species as references.


Key Features:

  • Protein-to-genome alignment (miniprot): Uses miniprot, a rapid protein-to-genome aligner, to map reference proteins to target genomes.
  • Ab initio gene prediction (AUGUSTUS): Uses AUGUSTUS to infer gene structures informed by protein alignments.
  • Reference-driven annotation: Leverages high-quality proteins from closely related species as evidence when transcriptome data are unavailable.
  • Automated pipeline: Integrates alignment and prediction steps into an automated workflow for genome-wide annotation.
  • Scalability: Demonstrated performance on large vertebrate genomes.
  • Cross-taxa applicability: Applied to multiple eukaryotic groups including insects, vertebrates, and land plants.

Scientific Applications:

  • Genome annotation without transcriptomes: Predicts protein-coding genes in newly sequenced eukaryotic genomes when RNA-seq or transcriptome data are lacking.
  • Vertebrate genome annotation: Annotates large vertebrate genomes using reference proteins and combined miniprot+AUGUSTUS prediction.
  • Cross-kingdom eukaryotic annotation: Supports gene structure prediction across taxa such as insects and land plants using reference-driven evidence.

Methodology:

Align reference proteins to the target genome using miniprot and use the resulting alignments as evidence for gene structure prediction with AUGUSTUS within an automated pipeline.

Topics

Details

License:
Artistic-1.0
Tool Type:
command-line tool
Operating Systems:
Linux
Programming Languages:
Perl, Bash
Added:
11/16/2022
Last Updated:
11/24/2024

Operations

Publications

Brůna T, Li H, Guhlin J, Honsel D, Herbold S, Stanke M, Nenasheva N, Ebel M, Gabriel L, Hoff KJ. Galba: genome annotation with miniprot and AUGUSTUS. BMC Bioinformatics. 2023;24(1). doi:10.1186/s12859-023-05449-z. PMID:37653395. PMCID:PMC10472564.

PMID: 37653395
Funding: - National Institutes of Health: R01HG010040 - Deutsche Forschungsgemeinschaft: 277249973, 391397397 - Government of Mecklenburg-Vorpommern: Project Data Competency