Joint Bayesian estimation of alignment and phylogeny

I describe a novel model and algorithm for simultaneously estimating multiple molecular sequence alignments and the phylogenetic trees that relate the sequences. Unlike current techniques that base phylogeny estimates on a single estimate of the alignment, I take alignment uncertainty into account by considering all possible alignments. This sidesteps the problem in which alignments created by progressive alignment are biased toward the guide tree used to generate them. Joint estimation also allows us to use the evidence in shared insertion/deletions (indels) to group sister taxa in the phylogeny. The indel model I present makes use of affine gap penalties and thus allows indels of multiple letters. I describe novel MCMC transition kernels allowing us to sample from the joint posterior distribution. I describe a method for summarizing this uncertainty in a single plot. I apply the joint estimation framework to improve the resolution of phylogeny estimate for recently diverged disease strains by incorporating indel information. I introduce an improved indel a novel transition kernel that improves computational efficiency by proposing non-local topology rearrangements and by block sampling alignment and topology parameters. I also extend my original indel model to increase biological realism by placing indels preferentially on longer branches. Finally, I examine the effects of codon-based substitution models on alignment quality and phylogenetic inference.