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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

DM-PhyClus: a Bayesian phylogenetic algorithm for infectious disease transmission cluster inference.

Luc Villandré¹, Aurélie Labbe², Bluma Brenner³

¹Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1020 avenue des Pins Ouest, Montreal, H3A 1A2, QC, Canada. luc.villandre@mail.mcgill.ca.

BMC Bioinformatics

|September 16, 2018

Summary

A new Dirichlet-Multinomial Phylogenetic Clustering (DM-PhyClus) algorithm improves transmission cluster detection by identifying rapid transmission chains without arbitrary cutpoints. This method offers more interpretable and reliable phylogenetic clusters for public health strategies.

Keywords:

Bayesian inference Clustering HIV-1 Markov Chain Monte Carlo Phylogenetics

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Area of Science:

Phylogenetics
Computational Biology
Epidemiology

Background:

Conventional phylogenetic clustering uses arbitrary cutpoints, leading to inconsistent confidence measures.
Existing methods often struggle with clear interpretation of transmission clusters.

Purpose of the Study:

Introduce a novel Bayesian phylogenetic clustering algorithm, DM-PhyClus (Dirichlet-Multinomial Phylogenetic Clustering).
Develop a method for identifying transmission chains without arbitrary distance or confidence thresholds.

Main Methods:

Developed the DM-PhyClus algorithm, a Bayesian approach for phylogenetic clustering.
Utilized simulations to compare DM-PhyClus against conventional and distance-based methods.
Applied DM-PhyClus to real HIV-1 sequence data.

Main Results:

DM-PhyClus demonstrated superior mean cluster recovery compared to conventional and Gap procedures in simulations.
Analysis of HIV-1 sequences yielded clusters consistent with previous detailed investigations.
The algorithm successfully identified transmission clusters without requiring ad hoc cutpoints.

Conclusions:

DM-PhyClus facilitates transmission cluster detection by providing sensible inference and eliminating the need for cutpoints.
Reliable transmission cluster estimates are crucial for controlling infectious diseases like HIV-1.
DM-PhyClus can enhance public health strategies through improved cluster detection.