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Bioinformatics. 2008 Oct 15;24(20):2317-23. doi: 10.1093/bioinformatics/btn445. Epub 2008 Aug 21.

Empirical profile mixture models for phylogenetic reconstruction.

Author information

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

Abstract

MOTIVATION:

Previous studies have shown that accounting for site-specific amino acid replacement patterns using mixtures of stationary probability profiles offers a promising approach for improving the robustness of phylogenetic reconstructions in the presence of saturation. However, such profile mixture models were introduced only in a Bayesian context, and are not yet available in a maximum likelihood (ML) framework. In addition, these mixture models only perform well on large alignments, from which they can reliably learn the shapes of profiles, and their associated weights.

RESULTS:

In this work, we introduce an expectation-maximization algorithm for estimating amino acid profile mixtures from alignment databases. We apply it, learning on the HSSP database, and observe that a set of 20 profiles is enough to provide a better statistical fit than currently available empirical matrices (WAG, JTT), in particular on saturated data.

PMID:
18718941
DOI:
10.1093/bioinformatics/btn445
[Indexed for MEDLINE]

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