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SonicParanoid
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SonicParanoid is an algorithm for the de-novo prediction of orthologous genes among multiple species.[1] It borrows the main idea from InParanoid[2] with substantial changes to the algorithm that drastically reduce the time required for the analysis. Additionally, SonicParanoid generates groups of orthologous genes shared among the input proteomes using single-linkage hierarchical clustering or markov clustering. The latest iteration of SonicParanoid uses machine learning to substantially reduce execution times, and language models to infer orthologs at the domain level.[3]
References
[edit]- ^ Cosentino, Salvatore; Iwasaki, Wataru (2019). "SonicParanoid: fast, accurate and easy orthology inference". Bioinformatics. 35 (1): 149–151. doi:10.1093/bioinformatics/bty631. PMC 6298048. PMID 30032301.
- ^ Remm, Maido; Christian E.V. Storm; Erik L.L. Sonnhammer (2001). "Automatic clustering of orthologs and in-paralogs from pairwise species comparisons". Journal of Molecular Biology. 314 (5): 1041–1052. CiteSeerX 10.1.1.328.6724. doi:10.1006/jmbi.2000.5197. PMID 11743721.
- ^ Cosentino, Salvatore; Sriswasdi, Sira; Iwasaki, Wataru (25 July 2024). "SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models". Genome Biology. 25 (1): 195. doi:10.1186/s13059-024-03298-4. PMC 11270883. PMID 39054525.
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