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A Practical Guide to Phylogenetics for Nonexperts

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BayesCAT: Bayesian co-estimation of alignment and tree.

Heejung Shim¹, Bret Larget²

¹Department of Statistics, Purdue University, West Lafayette, Indiana, U.S.A.

|January 19, 2017

Summary

This study introduces a joint Bayesian model to simultaneously estimate phylogenetic trees and sequence alignments, improving accuracy by accounting for alignment uncertainties. The new method, BayesCAT, offers more robust phylogenetic inferences than traditional separate approaches.

Keywords:

Alignment Bayesian inference Insertion and deletion process MCMC Phylogenetics Phylogeny Tree

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Published on: August 16, 2017

Area of Science:

Computational Biology
Bioinformatics
Evolutionary Biology

Background:

Phylogenetic tree and sequence alignment estimations are traditionally performed sequentially.
This sequential approach ignores alignment uncertainties, potentially leading to overstated confidence in phylogenetic results.

Purpose of the Study:

To develop a joint model for co-estimating phylogenetic trees and multiple sequence alignments.
To improve phylogenetic inference accuracy by explicitly modeling and accounting for alignment uncertainty.

Main Methods:

Developed a joint Bayesian model using Markov Chain Monte Carlo (MCMC) for co-estimation.
Implemented an insertion and deletion (indel) model allowing arbitrary-length overlapping events and general fragment size distributions.
Defined the state space as a tree with a complete history of indel events mapped onto it.

Main Results:

The joint co-estimation method demonstrably improves phylogenetic inference compared to traditional sequential methods.
Performance was validated using both simulated and real biological data.
The developed software, BayesCAT, is publicly available for use.

Conclusions:

Jointly co-estimating phylogeny and sequence alignment provides more accurate and reliable phylogenetic inferences.
Accounting for indel uncertainty within a unified framework is crucial for robust evolutionary analyses.
The BayesCAT software facilitates the application of this advanced methodology in bioinformatics research.