SonicParanoid is an algorithm for the de-novo prediction of
orthologous genes among multiple species.
It borrows the main idea from InParanoid
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
In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two ...
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.
References
External links
Source code on GitLabSonicParanoid DocumentationPython Package
Genetics databases
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