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280
PAML: a program package for phylogenetic analysis by maximum likelihood
 COMPUT APPL BIOSCI 13:555–556
, 1997
"... PAML, currently in version 1.2, is a package of programs for phylogenetic analyses of DNA and protein sequences using the method of maximum likelihood (ML). The programs can be used for (i) maximum likelihood estimation of evolutionary parameters such as branch lengths in a phylogenetic tree, the tr ..."
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Cited by 1459 (17 self)
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PAML, currently in version 1.2, is a package of programs for phylogenetic analyses of DNA and protein sequences using the method of maximum likelihood (ML). The programs can be used for (i) maximum likelihood estimation of evolutionary parameters such as branch lengths in a phylogenetic tree, the transition/transversion rate ratio, the shape parameter of the gamma distribution for variable evolutionary rates at sites, and rate parameters for different genes; (ii) likelihood ratio test of hypotheses concerning sequence evolution, such as rate constancy and independence among sites and rate constancy among lineages (the molecular clock); (iii) calculation of substitution rates at sites and reconstruction of ancestral nucleotide or amino acid sequences; and (iv) phylogenetic tree reconstruction by maximum likelihood and Bayesian methods.
Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests
, 2004
"... Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the sel ..."
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Cited by 407 (8 self)
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Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (modelaveraged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AICbased model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus (genus Carabus) ground beetles described by Sota and Vogler (2001).
Bayesian phylogenetic inference via Markov chain Monte Carlo methods
 Biometrics
, 1999
"... SUMMARY. We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cop ..."
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Cited by 159 (6 self)
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SUMMARY. We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cophenetic matrix form suggests a simple and effective proposal distribution for selecting candidate trees close to the current tree in the chain. We illustrate the algorithm with restriction site data on 9 plant species, then extend to DNA sequences from 32 species of fish. The algorithm mixes well in both examples from random starting trees, generating reproducible estimates and credible sets for the path of evolution.
A likelihood approach to estimating phylogeny from discrete morphological character data
 Systematic Biology
, 2001
"... Abstract.—Evolutionary biologists have adopted simple likelihoodmodels for purposes of estimating ancestral states and evaluating character independence on specied phylogenies; however, for purposes of estimating phylogenies by using discrete morphological data, maximum parsimony remains the only o ..."
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Cited by 156 (0 self)
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Abstract.—Evolutionary biologists have adopted simple likelihoodmodels for purposes of estimating ancestral states and evaluating character independence on specied phylogenies; however, for purposes of estimating phylogenies by using discrete morphological data, maximum parsimony remains the only option. This paper explores the possibility of using standard, wellbehaved Markov models for estimating morphological phylogenies (including branch lengths) under the likelihood criterion. An importantmodication of standardMarkovmodels involvesmaking the likelihood conditional on characters being variable, because constant characters are absent in morphological data sets. Without this modication, branch lengths are often overestimated, resulting in potentially serious biases in tree topology selection. Several new avenues of research are opened by an explicitly modelbased approach to phylogenetic analysis of discrete morphological data, including combineddata likelihood analyses (morphologyC sequence data), likelihood ratio tests, and Bayesian analyses. [Discrete morphological character; Markov model; maximum likelihood; phylogeny.] The increased availability of nucleotide and protein sequences from a diversity of both organisms and genes has stimu
Relaxed Phylogenetics and Dating with Confidence
"... In phylogenetics, the unrooted model of phylogeny and the strict molecular clock model are two extremes of a continuum. Despite their dominance in phylogenetic inference, it is evident that both are biologically unrealistic and that the real evolutionary process lies between these two extremes. Fort ..."
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Cited by 125 (4 self)
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In phylogenetics, the unrooted model of phylogeny and the strict molecular clock model are two extremes of a continuum. Despite their dominance in phylogenetic inference, it is evident that both are biologically unrealistic and that the real evolutionary process lies between these two extremes. Fortunately, intermediate models employing relaxed molecular clocks have been described. These models open the gate to a new field of ‘‘relaxed phylogenetics.’’ Here we introduce a new approach to performing relaxed phylogenetic analysis. We describe how it can be used to estimate phylogenies and divergence times in the face of uncertainty in evolutionary rates and calibration times. Our approach also provides a means for measuring the clocklikeness of datasets and comparing this measure between different genes and phylogenies. We find no significant rate autocorrelation among branches in three large datasets, suggesting that autocorrelated models are not necessarily suitable for these data. In addition, we place these datasets on the continuum of clocklikeness between a strict molecular clock and the alternative unrooted extreme. Finally, we
Species trees from gene trees: Reconstructing Bayesian posterior distributions of a species phylogeny using estimated gene tree distributions
 SYSTEMATIC BIOLOGY
, 2007
"... The estimation of species trees has become popular as a considerable amount of multilocus molecular data is available for inferring the evolutionary history of species. However, the current phylogenetic paradigm, that reconstructs gene trees to represent the species tree suggests that commonly used ..."
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Cited by 109 (11 self)
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The estimation of species trees has become popular as a considerable amount of multilocus molecular data is available for inferring the evolutionary history of species. However, the current phylogenetic paradigm, that reconstructs gene trees to represent the species tree suggests that commonly used methods such as the concatenation method, the consensus tree method, or the gene tree parsimony method may be either inconsistent or highly biased. In this paper, we propose a Bayesian hierarchical model to estimate the phylogeny of a group of species using multiple estimated gene tree distributions such as those that arise in a Bayesian analysis of DNA sequence data. Our model employs substitution models used in traditional phylogenetics, but also uses coalescent theory to explain genealogical signals from species trees to gene trees and from gene trees to sequence data, thereby forming a stochastic model to estimate gene trees, species trees, ancestral population sizes and species divergence times simultaneously. Our model is founded on the assumption that gene trees, even of unlinked loci, are correlated due to being derived from a single species tree and therefore should be estimated jointly. We apply the method to two multilocus DNA sequences datasets. The estimates of the
Molecular phylogenetics: stateoftheart methods for looking into the past. Trends Genet 2001;17:261–72
"... methods for looking into the past ..."
Molecular systematics of the eastern fence lizard (Sceloporus undulatus): A comparison of parsimony, likelihood, and Bayesian approaches
 Syst. Biol
, 2002
"... Abstract.—Phylogenetic analysis of large datasets using complex nucleotide substitution models under a maximum likelihood framework can be computationally infeasible, especially when attempting to infer con�dence values by way of nonparametric bootstrapping. Recent developments in phylogenetics sugg ..."
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Cited by 91 (8 self)
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Abstract.—Phylogenetic analysis of large datasets using complex nucleotide substitution models under a maximum likelihood framework can be computationally infeasible, especially when attempting to infer con�dence values by way of nonparametric bootstrapping. Recent developments in phylogenetics suggest the computational burden can be reduced by using Bayesian methods of phylogenetic inference. However, few empirical phylogenetic studies exist that explore the ef�ciency of Bayesian analysis of large datasets. To this end, we conducted an extensive phylogenetic analysis of the wideranging and geographically variable Eastern Fence Lizard (Sceloporus undulatus). Maximum parsimony, maximum likelihood, and Bayesian phylogenetic analyses were performed on a combined mitochondrial DNA dataset (12S and 16S rRNA, ND1 proteincoding gene, and associated tRNA; 3,688 bp total) for 56 populations of S. undulatus (78 total terminals including other S. undulatus group species and outgroups). Maximum parsimony analysis resulted in numerous equally parsimonious trees (82,646 from equally weighted parsimony and 335 from weighted parsimony). The majority rule consensus tree derived from the Bayesian analysis was topologically identical to the single best phylogeny inferred from the maximum likelihood analysis, but required �80 % less computational time. The mtDNA data provide strong support for the monophyly of the S. undulatus group and
Phylogenetic Tree Construction Using Markov Chain Monte Carlo
, 1999
"... We describe a Bayesian method based on Markov chain simulation to study the phylogenetic relationship in a group of DNA sequences. Under simple models of mutational events, our method produces a Markov chain whose stationary distribution is the conditional distribution of the phylogeny given the obs ..."
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Cited by 86 (0 self)
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We describe a Bayesian method based on Markov chain simulation to study the phylogenetic relationship in a group of DNA sequences. Under simple models of mutational events, our method produces a Markov chain whose stationary distribution is the conditional distribution of the phylogeny given the observed sequences. Our algorithm strikes a reasonable balance between the desire to move globally through the space of phylogenies and the need to make computationally feasible moves in areas of high probability. Since phylogenetic information is described by a tree, we have created new diagnostics to handle this type of data structure. An important byproduct of the Markov chain Monte Carlo phylogeny building technique is that it provides estimates and corresponding measures of variability for any aspect of the phylogeny under study.