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キーワード:
Amino acid model, Bayesian analysis, Bayesian model averaging, BEAST, Gamma rate heterogeneity, Phylogenetics, Protein model, Site model, Statistical phylogenetics, Substitution model
要旨:
Background. Bayesian analyses offer many benefits for phylogenetic, and have been popular for analysis of amino acid alignments. It is necessary to specify a substitution and site model for such analyses, and often an ad hoc, or likelihood based method is employed for choosing these models that are typically of no interest to the analysis overall. Methods. We present a method called OBAMA that averages over substitution models and site models, thus letting the data inform model choices and taking model uncertainty into account. It uses trans-dimensional Markov Chain Monte Carlo (MCMC) proposals to switch between various empirical substitution models for amino acids such as Dayhoff, WAG, and JTT. Furthermore, it switches base frequencies from these substitution models or use base frequencies estimated based on the alignment. Finally, it switches between using gamma rate heterogeneity or not, and between using a proportion of invariable sites or not. Results. We show that the model performs well in a simulation study. By using appropriate priors, we demonstrate both proportion of invariable sites and the shape parameter for gamma rate heterogeneity can be estimated. The OBAMA method allows taking in account model uncertainty, thus reducing bias in phylogenetic estimates. The method is implemented in the OBAMA package in BEAST 2, which is open source licensed under LGPL and allows joint tree inference under a wide range of models. © Copyright 2020 Bouckaert.
