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• W2796130155 abstract "Abstract Motivation When gene duplication occurs, one of the copies may become free of selective pressure and evolve at an accelerated pace. This has important consequences on the prediction of orthology relationships, since two orthologous genes separated by divergence after duplication may differ in both sequence and function. In this work, we make the distinction between the primary orthologs, which have not been affected by accelerated mutation rates on their evolutionary path, and the secondary orthologs, which have. Similarity-based prediction methods will tend to miss secondary orthologs, whereas phylogeny-based methods cannot separate primary and secondary orthologs. However, both types of orthology have applications in important areas such as gene function prediction and phylogenetic reconstruction, motivating the need for methods that can distinguish the two types. Results We formalize the notion of divergence after duplication and provide a theoretical basis for the inference of primary and secondary orthologs. We then put these ideas to practice with the Hybrid Prediction of Paralogs and Orthologs (HyPPO) framework, which combines ideas from both similarity and phylogeny approaches. We apply our method to simulated and empirical datasets and show that we achieve superior accuracy in predicting primary orthologs, secondary orthologs and paralogs. Availability and implementation HyPPO is a modular framework with a core developed in Python and is provided with a variety of C++ modules. The source code is available at https://github.com/manuellafond/HyPPO. Supplementary information Supplementary data are available at Bioinformatics online." @default.
• W2796130155 created "2018-04-13" @default.
• W2796130155 creator A5016796629 @default.
• W2796130155 creator A5032949143 @default.
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• W2796130155 date "2018-06-27" @default.
• W2796130155 modified "2023-10-12" @default.
• W2796130155 title "Accurate prediction of orthologs in the presence of divergence after duplication" @default.
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• W2796130155 doi "https://doi.org/10.1093/bioinformatics/bty242" @default.
• W2796130155 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6022570" @default.
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• W2796130155 hasPublicationYear "2018" @default.
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