A Bayesian Compound Stochastic Process for Modeling Nonstationary and Nonhomogeneous Sequence Evolution

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 nonstationary and nonhomogeneous model accounting for compositional biases. Unlike previous nonstationary models, which are branchwise, that is, 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 data sets. In most cases, the stationarity assumption was rejected in favor of our nonstationary 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 2 previously developed nonstationary models. We show that the coupling between speciations and compositional shifts inherent to branchwise models may lead to an overparameterization, 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 data sets under investigation. Altogether, our results show that accounting for nonstationary sequence evolution may require more elaborate and more flexible models than those currently used.

On the Age of Eukaryotes: Evaluating Evidence from Fossils and Molecular Clocks

“…Although the harmonic mean estimator has been used for this purpose, it turns out to be extremely unstable . More reliable estimators are complex approximation methods such as thermodynamic integration ( path-sampling) (Ogata 1989;Philippe 2004, 2006;Blanquart and Lartillot 2006;Rodrigue et al 2006) and the stepping-stone method (Xie et al 2011) that have been implemented in programs like Phylobayes (Lartillot et al 2009) and BEAST (Baele et al 2012), respectively. Note that these methods are generally used for RMC model selection but can be difficult to implement for selecting among substitution models.…”

The Interaction between Base Compositional Heterogeneity and Among-Site Rate Variation in Models of Molecular Evolution

“…ere has been growing interest in nonstationary evolution and how it affects phylogenetic methods [10]. Several soware packages [20,27,28] have been developed that implement models that address this issue. While it is clear that this problem is real and problematic for some datasets [29], it is still unclear how this factor interacts with other evolutionary phenomena, as illustrated here.…”

Probabilistic models of eukaryotic evolution: time for integration

“…Instead, variation through time should ideally be modelled in terms of continuous-time processes. Some developments in this direction have already been proposed, either using compound Poisson processes [53,54], or multivariate Brownian processes [55]. These developments have not yet reached the stage where they could be routinely used for addressing large-scale phylogenetic reconstruction problems.…”