MixtureFinder: Estimating DNA Mixture Models for Phylogenetic Analyses.

Abstract

In phylogenetic studies, both partitioned models and mixture models are used to account for heterogeneity in molecular evolution among the sites of DNA sequence alignments. Partitioned models require the user to specify the grouping of sites into subsets, and then assume that each subset of sites can be modeled by a single common process. Mixture models do not require users to prespecify subsets of sites, and instead calculate the likelihood of every site under every model, while co-estimating the model weights and parameters. While much research has gone into the optimization of partitioned models by merging user-specified subsets, there has been less attention paid to the optimization of mixture models for DNA sequence alignments. In this study, we first ask whether a key assumption of partitioned models-that each user-specified subset can be modeled by a single common process-is supported by the data. Having shown that this is not the case, we then design, implement, test, and apply an algorithm, MixtureFinder, to select the optimum number of classes for a mixture model of Q-matrices for the standard models of DNA sequence evolution. We show this algorithm performs well on simulated and empirical datasets and suggest that it may be useful for future empirical studies. MixtureFinder is available in IQ-TREE2, and a tutorial for using MixtureFinder can be found here: http://www.iqtree.org/doc/Complex-Models#mixture-models.