Phylogenetic tree construction using Markov chain Monte Carlo

We describe a Bayesian method based on Markov chain simulation to study the phylogenetic relationship in a group of DNA sequences. Under simple models of mutational events, our method produces a Markov chain whose stationary distribution is the conditional distribution of the phylogeny given the obs erved sequences. Our algorithm strikes a reasonable balance between the des ire to move globally through the space of phylogenies and the need to make computationally feasible moves in areas of high probability. Because phylog enetic information is described by a tree, we have created new diagnostics to handle this type of data structure. An important byproduct of the Markov chain Monte Carlo phylogeny building technique is that it provides estimat es and corresponding measures of variability for any aspect of the phylogen y under study.