Classification of molecular sequence data using Bayesian phylogenetic mixture models

Rate variation among the sites of a molecular sequence is commonly found in applications of phylogenetic inference. Several approaches exist to account for this feature but they do not usually enable us to pinpoint the sites that evolve under one or another rate of evolution in a straightforward manner. In this paper we concentrate on phylogenetic mixture models as tools for site classification. Our method does not rely on prior knowledge of site membership to classes or even the number of classes. Furthermore, it does not require correlated sites to be next to one another in the sequence alignment, unlike some phylogenetic hidden Markov or change-point models. We present a simulation study to show that our approach is able to correctly classify the sites to evolutionary classes and we analyse the popular alignment of the mitochondrial DNA of primates. In both examples, all mixtures outperform commonly-used models of among-site rate variation and models that do not account for rate heterogeneity. Our method for site classification is directly relevant to the profiling of genes with unknown function, and its application may lead to the discovery of partitions not otherwise recognised in the alignment. In addition, we discuss computational aspects including Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden, AL5 2JQ, UK. Email: [email protected] Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK. Email: [email protected] Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK. Email: [email protected]