Measuring the relative contribution to predictive power of modern nucleotide substitution modeling approaches.

Abstract

Traditional approaches to probabilistic phylogenetic inference have relied on information-theoretic criteria to select among a relatively small set of substitution models. These model selection criteria have recently been called into question when applied to richer models, including models that invoke mixtures of nucleotide frequency profiles. At the nucleotide level, we are therefore left without a clear picture of mixture models' contribution to overall predictive power relative to other modeling approaches. Here, we utilize a Bayesian cross-validation method to directly measure the predictive performance of a wide range of nucleotide substitution models. We compare the relative contributions of free nucleotide exchangeability parameters, gamma-distributed rates across sites, and mixtures of nucleotide frequencies with both finite and infinite mixture frameworks. We find that the most important contributor to a model's predictive power is the use of a sufficiently rich mixture of nucleotide frequencies. These results suggest that mixture models should be given greater consideration in nucleotide-level phylogenetic inference.