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A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Syst. Biol (2004)
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BibTeX
@MISC{Pagel04aphylogenetic,
author = {Mark Pagel and Andrew Meade},
title = {A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Syst. Biol},
year = {2004}
}
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Abstract
Abstract.—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. [Bayesian inference; MCMC; mixture model; phylogeny; rate-heterogeneity; secondary structure; sequence evolution] The conventional likelihood-based approach to infer-ring phylogenetic trees from aligned gene-sequence or other data is to apply a single substitutional model to
Keyphrases
character-state data phylogenetic mixture model gene sequence mixture model data set phylogenetic tree sequence evolution conventional likelihood-based approach alignment evolve different site homogenous process special case conventional partitioning phylogenetic inference rate-variability model protein-coding gene homogeneous model infer-ring phylogenetic tree site pattern-heterogeneity qualitative variability easy-to-use computer program prior knowledge model simplifies bayesian inference present study bayesian markov-chain monte carlo framework secondary structure general likelihood-based mixture model gene-sequence data distinct way single substitutional model
