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Updated: Jul 2, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Empirical profile mixture models for phylogenetic reconstruction.

Le Si Quang¹, Olivier Gascuel, Nicolas Lartillot

¹Méthodes et Algorithmes pour la Bioinformatique, LIRMM, CNRS-UM2, Montpellier Cedex 5, France.

Bioinformatics (Oxford, England)

|August 23, 2008

Summary

This study introduces an expectation-maximization algorithm for estimating amino acid profile mixtures, enhancing phylogenetic reconstructions. The new method offers a better statistical fit than existing models, especially for saturated data in phylogenetics.

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Area of Science:

Computational Biology
Evolutionary Biology
Bioinformatics

Background:

Phylogenetic reconstructions can be improved by accounting for site-specific amino acid replacement patterns using profile mixture models.
Existing profile mixture models are limited to Bayesian frameworks and perform best on large alignments.

Purpose of the Study:

To develop an expectation-maximization algorithm for estimating amino acid profile mixtures within a maximum likelihood (ML) framework.
To make profile mixture models more accessible and applicable to a wider range of phylogenetic analyses.

Main Methods:

Developed an expectation-maximization (EM) algorithm for estimating amino acid profile mixtures.
Applied the EM algorithm to learn profiles from the HSSP database.

Main Results:

The developed EM algorithm effectively estimates amino acid profile mixtures.
A set of 20 learned profiles provided a superior statistical fit compared to existing empirical matrices (WAG, JTT).
The method shows particular improvement on saturated sequence alignments.

Conclusions:

The new ML-based approach for amino acid profile mixtures enhances phylogenetic reconstruction robustness.
This method offers a statistically superior alternative to current empirical matrices, especially for challenging, saturated data.
The developed algorithm expands the utility of profile mixture models in phylogenetics.