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774
2007 Molecular evolutionary genetics analysis (MEGA) software version 4.0
 Mol. Ecol
, 2004
"... 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 981 (5 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 . . .
, 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 851 (14 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: http://www.lirmm.fr/w3ifa/MAAS/. [Algorithm; computer simulations; maximum likelihood; phylogeny; rbcL; RDPII project.] The size of homologous sequence data sets has increased dramatically in recent years, and many of these data sets now involve several hundreds of taxa. Moreover, current probabilist...
Learning in graphical models
, 2004
"... Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve largescale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for ..."
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Cited by 612 (11 self)
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Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve largescale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for approaching these problems, and indeed many of the models developed by researchers in these applied fields are instances of the general graphical model formalism. We review some of the basic ideas underlying graphical models, including the algorithmic ideas that allow graphical models to be deployed in largescale data analysis problems. We also present examples of graphical models in bioinformatics, errorcontrol coding and language processing. Key words and phrases: Probabilistic graphical models, junction tree algorithm, sumproduct algorithm, Markov chain Monte Carlo, variational inference, bioinformatics, errorcontrol coding.
Pfold: RNA secondary structure prediction using stochastic contextfree grammars
 Nucleic Acids Res
, 2003
"... RNA secondary structures are important in many biological processes and efficient structure prediction can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some applic ..."
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Cited by 132 (6 self)
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RNA secondary structures are important in many biological processes and efficient structure prediction can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some applications, but often it is possible to obtain related RNA sequences with conserved secondary structure. These should be included in structural analyses to give improved results. This work presents a practical way of predicting RNA secondary structure that is especially useful when related sequences can be obtained. The method improves a previous algorithm based on an explicit evolutionary model and a probabilistic model of structures. Predictions can be done on a web server at
RNA secondary structure prediction using stochastic contextfree grammars and evolutionary history
, 1999
"... Motivation: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary ..."
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Cited by 123 (12 self)
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Motivation: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic contextfree grammars (SCFGs) to give a prior probability distribution of structures.
Raxmliii: a fast program for maximum likelihoodbased inference of large phylogenetic trees
 Bioinformatics
, 2005
"... Motivation: The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer to ..."
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Cited by 114 (12 self)
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Motivation: The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer to the true tree more frequently than less elaborate methods such as parsimony or neighbor joining. Due to the combinatorial and computational complexity the size of trees which can be computed on a Biologist’s PC workstation within reasonable time is limited to trees containing approximately 100 taxa. Results: In this paper we present the latest release of our program RAxMLIII for rapid maximum likelihoodbased inference of large evolutionary trees which allows for computation of 1.000taxon trees in less than 24 hours on a single PC processor. We compare RAxMLIII to the currently fastest implementations for maximum likelihood and bayesian inference: PHYML and MrBayes. Whereas RAxMLIII performs worse than PHYML and MrBayes on synthetic data it clearly outperforms both programs on all real data alignments used in terms of speed and final likelihood values. Availability & Supplementary Information: RAxMLIII including all alignments and final trees mentioned in this paper is freely available as open source code at
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 103 (12 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.
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 85 (3 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.
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 75 (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
Using multiple alignments to improve gene prediction
 J. Comput. Biol
, 2005
"... Abstract. The multiple species de novo gene prediction problem can be stated as follows: given an alignment of genomic sequences from two or more organisms, predict the location and structure of all proteincoding genes in one or more of the sequences. Here, we present a new system, NSCAN (a.k.a. T ..."
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Cited by 61 (4 self)
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Abstract. The multiple species de novo gene prediction problem can be stated as follows: given an alignment of genomic sequences from two or more organisms, predict the location and structure of all proteincoding genes in one or more of the sequences. Here, we present a new system, NSCAN (a.k.a. TWINSCAN 3.0), for addressing this problem. NSCAN has the ability to model dependencies between the aligned sequences, contextdependent substitution rates, and insertions and deletions in the sequences. An implementation of NSCAN was created and used to generate predictions for the entire human genome. An analysis of the predictions reveals that NSCAN’s predictive accuracy in human exceeds that of all previously published wholegenome de novo gene predictors. In addition, predictions were generated for the genome of the fruit fly Drosophila melanogaster to demonstrate the applicability of NSCAN to invertebrate gene prediction. 1