OBAMA: OBAMA for Bayesian amino-acid model averaging.

Remco R Bouckaert

doi:10.7717/peerj.9460

Abstract

BackgroundBayesian 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.MethodsWe 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.ResultsWe 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.

Highlights

To perform a Bayesian phylogenetic analysis with amino acid alignments one needs to define a site model
For each of these cases, we simulated data under the model parameters distinguishing 8 cases: any combination of with/without estimated frequencies, with/without gamma rate heterogeneity and with/without a proportion of invariable sites, providing a total of 800 alignments over 16 taxa with 200 amino acids. For each of these 800 alignments, an Markov Chain Monte Carlo (MCMC) analysis was done under the OBAMA site model, Yule tree prior and uncorrelated relaxed clock model, all with the same priors and hyper priors as used to sample the data
Site models matter To determine the effectiveness of the OBAMA model, we investigated an amino acid alignment, M200 from TreeBase1 (Simmons et al, 2002)

Summary

Introduction

To perform a Bayesian phylogenetic analysis with amino acid alignments one needs to define a site model. We present a method called OBAMA that averages over substitution models and site models, 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. It switches base frequencies from these substitution models or use base frequencies estimated based on the alignment. 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

Methods

Results

Discussion

Conclusion