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SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models
Open accessPreprint — published as 10.1186/s13059-024-03298-4 · Paper page
Abstract
Accurate inference of orthologous genes constitutes a prerequisite for comparative and evolutionary genomics. SonicParanoid is one of the fastest tools for orthology inference; however, its scalability and accuracy have been hampered by time-consuming all-versus-all alignments and the existence of proteins with complex domain architectures. Here, we present a substantial update of Sonicparanoid, where a gradient boosting predictor halves the execution time and a language model doubles the recall. Application to empirical large-scale and standardized benchmark datasets showed that SonicParanoid2 is up to 18X faster than comparable methods and also the most accurate. SonicParanoid2 is available at https://gitlab.com/salvo981/sonicparanoid2
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Metadata
- Authors
- Cosentino, S., & Iwasaki, W.
- Affiliations
- The University of Tokyo (JP)
- Institute for Quantitative Biosciences, The University of Tokyo (JP)
- Corresponding author
- Iwasaki, Wataru
- Venue
- Genome biology
- Publisher
- openRxiv
- Published
- 2023-05-15
- Volume
- 25
- Issue
- 1
- Pages
- 195-195
- ISSN
- 1465-6906 (ISSN-L)
- PMC
- PMC11270883
- OpenAlex
- W4400987661
- Cited by
- 50
- References
- 1
- Citations by year
- 2026: 19 · 2025: 23 · 2024: 7 · 2023: 1
- FWCI
- 6.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Ministry of Education, Culture, Sports, Science and Technology (JPMXD1521474594, 22H04925, 16H06279, CREST)
- Japan Society for the Promotion of Science (19H05688, 16H06279, 22H04925, CREST)
- Core Research for Evolutional Science and Technology (JPMJCR19S2)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- orthology inference, ortholog prediction, gradient boosting, language models, protein domain architecture, comparative genomics, evolutionary genomics, benchmark dataset
- MeSH terms
- Machine Learning, Humans, Software, Sequence Alignment, Genomics
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