Empirical profile mixture models for phylogenetic reconstruction |
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Authors: | Quang Le Si Gascuel Olivier Lartillot Nicolas |
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Institution: | Méthodes et Algorithmes pour la Bioinformatique, LIRMM, CNRS-UM2, Montpellier Cedex 5, France. |
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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. |
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