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Updated: Aug 31, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Scalable Bayesian phylogenetics.

Alexander A Fisher¹, Gabriel W Hassler², Xiang Ji³

¹Department of Statistical Science, Duke University, Durham, NC 27710, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

|August 22, 2022

Summary

This summary is machine-generated.

Bayesian phylogenetics accelerates genomic analysis with improved Markov chain Monte Carlo (MCMC) sampling methods. These techniques enhance computational efficiency for large phylogenetic trees, aiding pathogen evolution studies.

Keywords:

BEAST Bayesian phylogenetics Hamiltonian Monte Carlo adapative MCMC online inference scalable inference

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Last Updated: Aug 31, 2025

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Published on: July 11, 2025

Area of Science:

Computational Biology
Evolutionary Biology
Genomics

Background:

Traditional phylogenetic inference methods struggle with large genomic datasets.
Bayesian phylogenetics offers computational advantages for modern genomic sampling.

Purpose of the Study:

To review and conceptualize methods for improving Bayesian phylogenetic inference.
To demonstrate computational speed-ups using advanced Markov chain Monte Carlo (MCMC) sampling techniques.

Main Methods:

Summarizing the Bayesian phylogenetic framework.
Discussing methods to optimize MCMC sampling: faster likelihood calculations, reduced burn-in, and improved proposals.
Applying techniques to HIV and SARS-CoV-2 phylogenetic trees.

Main Results:

Demonstrated significant speed-up in phylogenetic analyses with large datasets.
Successfully applied advanced MCMC methods to 1536-tip and 1000-tip phylogenies.
Illustrated the practical benefits of state-of-the-art Bayesian phylogenetic approaches.

Conclusions:

Advanced MCMC sampling substantially improves computational efficiency in Bayesian phylogenetics.
These methods are crucial for analyzing large-scale genomic data in pathogen evolution.
Promising alternatives to MCMC for phylogenetic posterior approximation are also discussed.