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• W4386949925 abstract "Molecular evolutionary rate variation is a key aspect of the evolution of many organisms that can be modeled using molecular clock models. For example, fixed local clocks revealed the role of episodic evolution in the emergence of SARS-CoV-2 variants of concern. Like all statistical models, however, the reliability of such inferences is contingent on an assessment of statistical evidence. We present a novel Bayesian phylogenetic approach for detecting episodic evolution. It consists of computing Bayes factors, as the ratio of posterior and prior odds of evolutionary rate increases, effectively quantifying support for the effect size. We conducted an extensive simulation study to illustrate the power of this method and benchmarked it to formal model comparison of a range of molecular clock models using (log) marginal likelihood estimation, and to inference under a random local clock model. Quantifying support for the effect size has higher sensitivity than formal model testing and is straight-forward to compute, because it only needs samples from the posterior and prior distribution. However, formal model testing has the advantage of accommodating a wide range molecular clock models. We also assessed the ability of an automated approach, known as the random local clock, where branches under episodic evolution may be detected without their a priori definition. In an empirical analysis of a data set of SARS-CoV-2 genomes, we find very strong evidence for episodic evolution. Our results provide guidelines and practical methods for Bayesian detection of episodic evolution, as well as avenues for further research into this phenomenon." @default.
• W4386949925 created "2023-09-23" @default.
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• W4386949925 date "2023-09-21" @default.
• W4386949925 modified "2023-10-11" @default.
• W4386949925 title "Detecting episodic evolution through Bayesian inference of molecular clock models" @default.
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• W4386949925 doi "https://doi.org/10.1093/molbev/msad212" @default.
• W4386949925 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37738550" @default.
• W4386949925 hasPublicationYear "2023" @default.
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