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A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data

Pagel, M. and Meade, A. (2004) A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Systematic Biology, 53 (4). pp. 571-581. ISSN 1063-5157

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To link to this article DOI: 10.1080/10635150490468675

Abstract/Summary

We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate- variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences
ID Code:10573
Uncontrolled Keywords:Bayesian inference, MCMC, mixture model, phylogeny, rate-heterogeneity, secondary structure, sequence evolution, 12S RIBOSOMAL-RNA, SECONDARY STRUCTURE, DNA-SEQUENCES, NUCLEOTIDE, SUBSTITUTION, LIKELIHOOD APPROACH, EVOLUTION, INFERENCE, SITES, TREES

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