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Updated: Feb 17, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals.

Mathieu Fourment¹, Brian C Claywell², Vu Dinh²

¹ithree institute, University of Technology Sydney, Ultimo, NSW 2007, Australia.

Systematic Biology

|November 30, 2017

Summary

Online phylogenetic sequential Monte Carlo (OPSMC) algorithms enable faster phylogenetic analysis of infectious disease surveillance data. These methods efficiently update evolutionary estimates as new sequence data arrives, improving real-time outbreak tracking.

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Area of Science:

Computational Biology
Epidemiology
Evolutionary Biology

Background:

Modern infectious disease surveillance generates continuous sequence data requiring timely phylogenetic analysis.
Existing Bayesian phylogenetic inference tools struggle to incorporate new sequences rapidly, limiting real-time evolutionary insights.

Purpose of the Study:

To introduce and evaluate online phylogenetic sequential Monte Carlo (OPSMC) algorithms for real-time phylogenetic inference.
To address the limitations of current software in handling continuously arriving sequence data.

Main Methods:

Development and assessment of several OPSMC algorithms for Bayesian phylogenetic inference.
Comparison of OPSMC performance against traditional Markov Chain Monte Carlo (MCMC) methods (MrBayes).
Introduction of "guided" proposals and "heating" techniques to improve OPSMC efficiency and accuracy.

Main Results:

OPSMC significantly reduces computation time for phylogenetic posteriors compared to MCMC.
Guided proposals improve OPSMC performance by better matching proposal density to the posterior.
Heating the proposal density resolves pathological behaviors observed in simpler guided proposals.

Conclusions:

OPSMC offers a computationally efficient alternative for phylogenetic inference in infectious disease surveillance.
The developed OPSMC algorithms provide accurate and timely evolutionary insights from streaming sequence data.
OPSMC enhances the utility of phylogenetic analysis for dynamic, real-time outbreak investigations.