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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 7284 (25 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 2182 (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:
SeaView version 4: A multiplatform graphical user interface for sequence alignment and phylogenetic tree building
 Mol Biol Evol
, 2010
"... We present SeaView version 4, a multiplatform program designed to facilitate multiple alignment and phylogenetic tree building from molecular sequence data through the use of a graphical user interface. SeaView version 4 combines all the functions of the widely used programs SeaView (in its previous ..."
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Cited by 319 (0 self)
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We present SeaView version 4, a multiplatform program designed to facilitate multiple alignment and phylogenetic tree building from molecular sequence data through the use of a graphical user interface. SeaView version 4 combines all the functions of the widely used programs SeaView (in its previous versions) and Phylo_win, and expands them by adding network access to sequence databases, alignment with arbitrary algorithm, maximumlikelihood tree building with PhyML, and display, printing, and copytoclipboard of rooted or unrooted, binary or multifurcating phylogenetic trees. In relation to the wide present offer of tools and algorithms for phylogenetic analyses, SeaView is especially useful for teaching and for occasional users of such software. SeaView is freely available at
Likelihoodbased tests of topologies in phylogenetics. Syst. Biol
, 2000
"... Abstract.—Likelihoodbased statistical tests of competing evolutionary hypotheses (tree topologies) have been available for approximately a decade. By far the most commonly used is the Kishino–Hasegawa test. However, the assumptions that have to be made to ensure the validity of the Kishino–Hasegawa ..."
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Cited by 225 (3 self)
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Abstract.—Likelihoodbased statistical tests of competing evolutionary hypotheses (tree topologies) have been available for approximately a decade. By far the most commonly used is the Kishino–Hasegawa test. However, the assumptions that have to be made to ensure the validity of the Kishino–Hasegawa test place important restrictions on its applicability. In particular, it is only valid when the topologies being compared are speci�ed a priori. Unfortunately, this means that the Kishino–Hasegawa test may be severely biased in many cases in which it is now commonly used: for example, in any case in which one of the competing topologies has been selected for testing because it is the maximum likelihood topology for the data set at hand. We review the theory of the Kishino–Hasegawa test and contend that for the majority of popular applications this test should not be used. Previously published results from invalid applications of the Kishino–Hasegawa test should be treated extremely cautiously, and future applications should use appropriate alternative tests instead. We review such alternative tests, both nonparametric and parametric, and give two examples which illustrate the importance of our contentions. [Kishino– Hasegawa test; maximum likelihood; phylogeny; Shimodaira–Hasegawa test; statistical tests; tree topology.] Hasegawa and Kishino (1989) and Kishino and Hasegawa(1989)developed methods for estimating the standard error and con�dence intervals for the difference in loglikelihoods between two topologically distinct phylogenetic trees representing hypotheses that might explain particular aligned sequence data sets. The method initially was introduced to compute con�dence intervals on posterior probabilities for topologies in a
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 203 (12 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
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.
Proposal for a new hierarchic classification system, Actinobacteria classis
, 1997
"... A new hierarchic classification structure for the taxa between the taxonomic levels of genus and class is proposed for the actinomycete line of descent as defined by analysis of small subunit (16s) rRNA and genes coding for this molecule (rDNA). While the traditional circumscription of a genus of th ..."
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Cited by 141 (3 self)
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A new hierarchic classification structure for the taxa between the taxonomic levels of genus and class is proposed for the actinomycete line of descent as defined by analysis of small subunit (16s) rRNA and genes coding for this molecule (rDNA). While the traditional circumscription of a genus of the actinomycete subphylum is by and large in accord with the 16s rRNA/rDNAbased phylogenetic clustering of these organisms, most of the higher taxa proposed in the past do not take into account the phylogenetic clustering of genera. The rich chemical, morphological and physiological diversity of phylogenetically closely related genera makes the description of families and higher taxa so broad that they become meaningless for the description of the enclosed taxa. Here we present a classification system in which phylogenetically neighboring taxa at the genus level are clustered into families, suborders, orders, subclasses, and a class irrespective of those phenotypic characteristics on which the delineation of taxa has been based in the past. Rather than being based on a listing of a wide array of chemotaxonomic, morphological, and physiological properties, the delineation is based solely on 16s rDNA/rRNA sequencebased phylogenetic clustering and the presence of taxonspecific 16s rDNA/RNA signature nucleotides. In their publication “On the nature of global classification,” Wheelis et al. (177) based the definition of higher taxa on the
Bayes or bootstrap? A simulation study comparing the performance of Bayesian Markov chain Monte Carlo sampling and bootstrapping in assessing phylogenetic conWdence.
 Mol. Biol. Evol.
, 2003
"... Bayesian Markov chain Monte Carlo sampling has become increasingly popular in phylogenetics as a method for both estimating the maximum likelihood topology and for assessing nodal confidence. Despite the growing use of posterior probabilities, the relationship between the Bayesian measure of confid ..."
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Cited by 140 (5 self)
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Bayesian Markov chain Monte Carlo sampling has become increasingly popular in phylogenetics as a method for both estimating the maximum likelihood topology and for assessing nodal confidence. Despite the growing use of posterior probabilities, the relationship between the Bayesian measure of confidence and the most commonly used confidence measure in phylogenetics, the nonparametric bootstrap proportion, is poorly understood. We used computer simulation to investigate the behavior of three phylogenetic confidence methods: Bayesian posterior probabilities calculated via Markov chain Monte Carlo sampling (BMCMCPP), maximum likelihood bootstrap proportion (MLBP), and maximum parsimony bootstrap proportion (MPBP). We simulated the evolution of DNA sequence on 17taxon topologies under 18 evolutionary scenarios and examined the performance of these methods in assigning confidence to correct monophyletic and incorrect monophyletic groups, and we examined the effects of increasing character number on support value. BMCMCPP and MLBP were often strongly correlated with one another but could provide substantially different estimates of support on short internodes. In contrast, BMCMCPP correlated poorly with MPBP across most of the simulation conditions that we examined. For a given threshold value, more correct monophyletic groups were supported by BMCMCPP than by either MLBP or MPBP. When threshold values were chosen that fixed the rate of accepting incorrect monophyletic relationship as true at 5%, all three methods recovered most of the correct relationships on the simulated topologies, although BMCMCPP and MLBP performed better than MPBP. BMCMCPP was usually a less biased predictor of phylogenetic accuracy than either bootstrapping method. BMCMCPP provided high support values for correct topological bipartitions with fewer characters than was needed for nonparametric bootstrap.
Molecular microbial diversity in soils from Eastern Amazonia: evidence for unusual microorganisms and microbial population shifts associated with deforestation.
 Appl Environ Microbiol
, 1997
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