Bayesian phylogenetic inference under a statistical insertion-deletion model

Abstract

A central problem in computational biology is the inference of phylogeny given a set of DNA or protein sequences. Currently, this problem is tackled stepwise, with phylogenetic reconstruction dependent on an initial multiple sequence alignment step. However these two steps are fundamentally interdependent. Whether the main interest is in sequence alignment or phylogeny, a major goal of computational biology is the co-estimation of both. Here we present a first step towards this goal by developing an extension of the Felsenstein peeling algorithm. Given an alignment, our extension analytically integrates out both substitution and insertion-deletion events within a proper statistical model. This new algorithm provides a solution to two important problems in computational biology. Firstly, indel events become informative for phylogenetic reconstruction, and secondly phylogenetic uncertainty can be included in the estimation of insertion-deletion parameters. We illustrate the practicality of this algorithm within a Bayesian Markov chain Monte Carlo framework by demonstrating it on a non-trivial analysis of a multiple alignment of ten globin protein sequences. © Springer-Verlag Berlin Heidelberg 2003.