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MBE Advance Access published online on August 24, 2006
Molecular Biology and Evolution, doi:10.1093/molbev/msl091
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© The Author 2006. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Accepted August 3, 2006

Research Article

A Bayesian Compound Stochastic Process for Modelling Non-stationary and Non-homogeneous Sequence Evolution

Samuel Blanquart 1 * and Nicolas Lartillot 1

1 Projet Méthodes et Algorithmes pour la Bioinformatique, LIRMM-CNRS, Montpellier France

* To whom correspondence should be addressed.
Samuel Blanquart, E-mail: samuel.blanquart{at}lirmm.fr


  Abstract

Variations of nucleotidic composition affect phylogenetic inference conducted under stationary models of evolution. In particular, they may cause unrelated taxa sharing similar base composition to be grouped together in the resulting phylogeny. To address this problem we developed a non-stationary and non-homogeneous model accounting for compositional biases. Unlike previous non-stationary models, which are branchwise, i.e. assume that base composition only changes at the nodes of the tree, in our model, the process of compositional drift is totally uncoupled from the speciation events. In addition, the total number of events of compositional drift distributed across the tree is directly inferred from the data. We implemented the method in a Bayesian framework, relying on Markov Chain Monte Carlo algorithms, and applied it to several nucleotidic datasets. In most cases, the stationarity assumption was rejected in favor of our non-stationary model. In addition, we show that our method is able to resolve a well known artifact. By Bayes factor evaluation, we compared our model with two previously developed non-stationary models. We show that the coupling between speciations and compositional shifts inherent to branchwise models may lead to an over-parameterization, resulting in a lesser fit. In some cases, this leads to incorrect conclusions, concerning the nature of the compositional biases. In contrast, our compound model more flexibly adapts its effective number of parameters to the datasets under investigation. Altogether, our results show that accounting for non-stationary sequence evolution may require more elaborate and more flexible models than those currently used.

Keywords: phylogeny; MCMC; non-stationary; non-homogeneous; compositional bias; compound stochastic process.
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