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]
