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Parallel Algorithms For Phylogenetic Inference Under A Structured Coalescent Approximation.

Home
Parallel Algorithms For Phylogenetic Inference Under A Structured Coalescent Approximation.

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A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Parallel algorithms for phylogenetic inference under a structured coalescent approximation.

Yucai Shao¹, Marc A Suchard^1,2,3, Andrew Rambaut⁴

¹Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA 90095.

Proceedings of the National Academy of Sciences of the United States of America

|April 28, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an improved structured coalescent model for pathogen phylogeography. The enhanced algorithm significantly speeds up spatiotemporal transmission analysis, aiding epidemic preparedness.

Keywords:

Bayesian inference parallel computing phylogeography structured coalescent viral evolution

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Area of Science:

Computational Biology
Epidemiology
Evolutionary Biology

Background:

Accurate spatiotemporal transmission reconstruction is vital for epidemic preparedness.
Structured coalescent models are used for phylogeographic analysis.
Existing Bayesian structured coalescent approximation (BASTA) implementations face computational limitations with large datasets.

Purpose of the Study:

To develop a computationally efficient algorithm for structured coalescent likelihood calculation.
To enable large-scale phylogeographic analyses of pathogen evolution and spread.
To provide a scalable tool for real-time pathogen surveillance.

Main Methods:

Algorithmic restructuring of the structured coalescent likelihood.
Optimization of memory access and parallelization of computations.

Integration into BEAST X and BEAGLE software packages.

Main Results:

The restructured algorithm reduces coalescent likelihood computation by 7-8 fold.
Parallelization further enhances performance by 10-26 fold.
Enables large-scale analyses of dengue virus and H5N1 avian influenza.
Backward-in-time approximations provide conservative posterior estimates.

Conclusions:

The developed method significantly improves the scalability and speed of phylogeographic analyses.
This facilitates real-time surveillance of rapidly evolving pathogens.
The tool enhances epidemic preparedness and response capabilities.