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PSAURON: a tool for assessing protein annotation across a broad range of species
Open accessPreprint — published as 10.1093/nargab/lqae189 · Paper page
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 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 general-purpose method to evaluate gene annotation. PSAURON is open source and freely available at https://github.com/salzberg-lab/PSAURON . One-Sentence Summary: PSAURON is a machine learning-based tool for rapid assessment of protein coding gene annotation.
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Metadata
- 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
- openRxiv
- Published
- 2024-05-18
- Volume
- 7
- Issue
- 1
- Pages
- lqae189-lqae189
- ISSN
- 2631-9268 (ISSN-L)
- PMC
- PMC11704789
- OpenAlex
- W4406151775
- Cited by
- 29
- References
- 1
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 1
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- 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
- National Human Genome Research Institute (R01HG006677)
- National Institute of General Medical Sciences (R35GM130151)
- 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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