PSAURON: a tool for assessing protein annotation across a broad range of species
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Abstract
Evaluating the accuracy of protein-coding sequences in genome annotations is a challenging problem for which there is no broadly applicable solution. In this manuscript, we introduce PSAURON (Protein Sequence Assessment Using a Reference ORF Network), a novel software tool developed to help assess the quality of protein-coding gene annotations. Utilizing a machine learning model trained on a diverse dataset from over 1000 plant and animal genomes, PSAURON assigns a score to coding DNA or protein sequence that reflects the likelihood that the sequence is a genuine protein-coding region. PSAURON scores can be used for genome-wide protein annotation assessment as well as the rapid identification of potentially spurious annotated proteins. Validation against established benchmarks demonstrates PSAURON’s effectiveness and correlation with recognized measures of protein quality, highlighting its potential use as a widely applicable method to evaluate precision in gene annotation. PSAURON is open source and freely available at https://github.com/salzberg-lab/PSAURON.
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- Authors
- Sommer, M. J., Zimin, A. V., & Salzberg, S. L.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Sommer, Markus J
- Venue
- NAR Genomics and Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-01-07
- Volume
- 7
- Issue
- 1
- Pages
- lqae189-lqae189
- ISSN
- 2631-9268 (ISSN-L)
- PMC
- PMC11704789
- OpenAlex
- W4406151775
- Cited by
- 29
- References
- 30
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 1
- FWCI
- 3.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NAR Genomics and Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- National Institutes of Health (R01-HG006677, R35-GM130151)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein-coding region prediction, machine learning classifiers
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