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MEGA5: Molecular evolutionary genetics analysis using maximum . . .
, 2011
"... Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version ..."
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Cited by 7244 (24 self)
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Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a userfriendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting bestfit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates sitebysite. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2176 (27 self)
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The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The core of this method is a simple hillclimbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distancebased method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximumlikelihood programs and much higher than the performance of distancebased and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximumlikelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distancebased and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page:
BEAST: Bayesian evolutionary analysis by sampling trees.
 BMC Evol Biol
, 2007
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A Hidden Markov Model approach to variation among sites in rate of evolution.
 Mol Biol Evol
, 1996
"... Abstract The method of hidden Markov models is used to allow for unequal and unknown evolutionary rates at different sites in molecular sequences. Rates of evolution at different sites are assumed to be drawn from a set of possible rates, with a finite number of possibilities. The overall likelihoo ..."
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Cited by 244 (1 self)
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Abstract The method of hidden Markov models is used to allow for unequal and unknown evolutionary rates at different sites in molecular sequences. Rates of evolution at different sites are assumed to be drawn from a set of possible rates, with a finite number of possibilities. The overall likelihood of a phylogeny is calculated as a sum of terms, each term being the probability of the data given a particular assignment of rates to sites, times the prior probability of that particular combination of rates. The probabilities of different rate combinations are specified by a stationary Markov chain that assigns rate categories to sites. While there will be a very large number of possible ways of assigning rates to sites, a simple recursive algorithm allows the contributions to the likelihood from all possible combinations of rates to be summed, in a time proportional to the number of different rates at a single site. Thus with 3 rates, the effort involved is no greater than 3 times that for a single rate. This "hidden Markov model" method allows for rates to differ between sites, and for correlations between the rates of neighboring sites. By summing over all possibilities it does not require us to know the rates at individual sites. However it does not allow for correlation of rates at nonadjacent sites, nor does it allow for a continuous distribution of rates over sites. It is shown how to use the NewtonRaphson method to estimate branch lengths of a phylogeny, and to infer from a phylogeny what assignment of rates to sites has the largest posterior probability. An example is given using βhemoglobin DNA sequences in 8 mammal species; the regions of high and low evolutionary rates are inferred and also the average length of patches of similar rates.
Bayesian phylogenetic analysis of combined data
 Syst. Biol
, 2004
"... Abstract. — The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameterrich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new typ ..."
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Cited by 198 (11 self)
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Abstract. — The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameterrich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5 % of the characters in the data set but nevertheless influenced the combineddata tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as amongsite rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more
S: PhyloBayes 3. A Bayesian software package for phylogenetic reconstruction and molecular dating
 Bioinformatics
"... Motivation: A variety of probabilistic models describing the evolution of DNA or protein sequences have been proposed for phylogenetic reconstruction or for molecular dating. However, there still lacks a common implementation allowing one to freely combine these independent features, so as to test t ..."
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Cited by 186 (8 self)
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Motivation: A variety of probabilistic models describing the evolution of DNA or protein sequences have been proposed for phylogenetic reconstruction or for molecular dating. However, there still lacks a common implementation allowing one to freely combine these independent features, so as to test their ability to jointly improve phylogenetic and dating accuracy. Results: We propose a software package, PhyloBayes 3, which can be used for conducting Bayesian phylogenetic reconstruction and molecular dating analyses, using a large variety of amino acid replacement and nucleotide substitution models, including empirical mixtures or nonparametric models, as well as alternative clock relaxation processes. Availability: PhyloBayes is freely available from our web site
Bayesian estimation of ancestral character states on phylogenies
 Syst. Biol
, 2004
"... Abstract.—Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree a ..."
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Cited by 170 (4 self)
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Abstract.—Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree and its branch lengths when making inferences about ancestral states or other comparative parameters. Here we present a general Bayesian approach for testing comparative hypotheses across statistically justified samples of phylogenies, focusing on the specific issue of reconstructing ancestral states. The method uses Markov chain Monte Carlo techniques for sampling phylogenetic trees and for investigating the parameters of a statistical model of trait evolution. We describe how to combine information about the uncertainty of the phylogeny with uncertainty in the estimate of the ancestral state. Our approach does not constrain the sample of trees only to those that contain the ancestral node or nodes of interest, and we show how to reconstruct ancestral states of uncertain nodes using a mostrecentcommonancestor approach. We illustrate the methods with data on ribonuclease evolution in the Artiodactyla. Software implementing the methods (BayesMultiState) is available from the authors. [Ancestral states; comparative methods; maximum likelihood; MCMC; phylogeny.] Given a collection of species, information on their attributes, and a phylogeny that describes their shared hierarchy of descent, the prospect is raised of inferring the
Divergence time and evolutionary rate estimation with multilocus data
 Syst. Biol
, 2002
"... Abstract.—Bayesian methods for estimating evolutionary divergence times are extended to multigene data sets, and a technique is described for detecting correlated changes in evolutionary rates among genes. Simulations are employed to explore the effect of multigene data on divergence time estimation ..."
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Cited by 166 (1 self)
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Abstract.—Bayesian methods for estimating evolutionary divergence times are extended to multigene data sets, and a technique is described for detecting correlated changes in evolutionary rates among genes. Simulations are employed to explore the effect of multigene data on divergence time estimation, and the methodology is illustrated with a previously published data set representing diverse plant taxa. The fact that evolutionary rates and times are confounded when sequence data are compared is emphasized and the importance of fossil information for disentangling rates and times is stressed. [Markov chain Monte Carlo; Metropolis–Hastings algorithm; molecular clock; phylogeny.] 689 Because of improved technology, molecular sequence data are becoming increasingly easy to collect. As a result, the pattern and process of evolution are being characterized in ever �ner detail. In the past, it was typical to infer evolutionary divergence times by selecting a single gene and then sequencing
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 155 (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
Combining phylogenetic and hidden Markov models in biosequence analysis
 J. Comput. Biol
, 2004
"... A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individ ..."
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Cited by 136 (13 self)
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A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individual sites, and hidden Markov models, which allow for changes from site to site. Besides improving the realism of ordinary phylogenetic models, they are potentially very powerful tools for inference and prediction—for gene finding, for example, or prediction of secondary structure. In this paper, we review progress on combined phylogenetic and hidden Markov models and present some extensions to previous work. Our main result is a simple and efficient method for accommodating higherorder states in the HMM, which allows for contextsensitive models of substitution— that is, models that consider the effects of neighboring bases on the pattern of substitution. We present experimental results indicating that higherorder states, autocorrelated rates, and multiple functional categories all lead to significant improvements in the fit of a combined phylogenetic and hidden Markov model, with the effect of higherorder states being particularly pronounced.