BayesCAT: Bayesian Co-estimation of Alignment and Tree

TL;DR

BayesCAT introduces a Bayesian joint estimation method for phylogeny and sequence alignment, accounting for alignment uncertainty and indel events, leading to potentially more accurate evolutionary inferences.

Contribution

It develops a novel joint Bayesian model with an MCMC approach that co-estimates phylogeny and alignment, including a detailed indel history, improving over traditional sequential methods.

Findings

Joint estimation improves phylogeny accuracy.

Incorporating indel history enhances evolutionary insights.

BayesCAT outperforms traditional methods on simulated and real data.

Abstract

Traditionally, phylogeny and sequence alignment are estimated separately: first estimate a multiple sequence alignment and then infer a phylogeny based on the sequence alignment estimated in the previous step. However, uncertainty in the alignment estimation is ignored, resulting, possibly, in overstated certainty in phylogeny estimates. We develop a joint model for co-estimating phylogeny and sequence alignment which improves estimates from the traditional approach by accounting for uncertainty in the alignment in phylogenetic inferences. Our insertion and deletion (indel) model allows arbitrary-length overlapping indel events and a general distribution for indel fragment size. We employ a Bayesian approach using MCMC to estimate the joint posterior distribution of a phylogenetic tree and a multiple sequence alignment. Our approach has a tree and a complete history of indel events mapped onto the tree as the state space of the Markov Chain while alternative previous approaches have a tree and an alignment. A large state space containing a complete history of indel events makes our MCMC approach more challenging, but it enables us to infer more information about the indel process. The performances of this joint method and traditional sequential methods are compared using simulated data as well as real data. Software named BayesCAT (Bayesian Co-estimation of Alignment and Tree) is available at https://github.com/heejungshim/BayesCAT.