Selection of models of DNA evolution with jModelTest.

jModelTest is a bioinformatic tool for choosing among different models of nucleotide substitution. The program implements five different model selection strategies, including hierarchical and dynamical likelihood ratio tests (hLRT and dLRT), Akaike and Bayesian information criteria (AIC and BIC), and a performance-based decision theory method (DT). The output includes estimates of model selection uncertainty, parameter importance, and model-averaged parameter estimates, including model-averaged phylogenies. jModelTest is a Java program that runs under Mac OSX, Windows, and Unix systems with a Java Run Environment installed, and it can be freely downloaded from (http://darwin.uvigo.es).

[1]  H. Kishino,et al.  Dating of the human-ape splitting by a molecular clock of mitochondrial DNA , 2005, Journal of Molecular Evolution.

[2]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[3]  M. Kimura Estimation of evolutionary distances between homologous nucleotide sequences. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[4]  H. Akaike A new look at the statistical model identification , 1974 .

[5]  H. Munro,et al.  Mammalian protein metabolism , 1964 .

[6]  M. Nei,et al.  Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. , 1993, Molecular biology and evolution.

[7]  David L. Swofford,et al.  Are Guinea Pigs Rodents? The Importance of Adequate Models in Molecular Phylogenetics , 1997, Journal of Mammalian Evolution.

[8]  C. Cunningham,et al.  The effects of nucleotide substitution model assumptions on estimates of nonparametric bootstrap support. , 2002, Molecular biology and evolution.

[9]  M. Kimura A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences , 1980, Journal of Molecular Evolution.

[10]  Zaid Abdo,et al.  Accounting for uncertainty in the tree topology has little effect on the decision-theoretic approach to model selection in phylogeny estimation. , 2005, Molecular biology and evolution.

[11]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[12]  Zaid Abdo,et al.  Performance-based selection of likelihood models for phylogeny estimation. , 2003, Systematic biology.

[13]  Jonathan P. Bollback,et al.  Bayesian model adequacy and choice in phylogenetics. , 2002, Molecular biology and evolution.

[14]  O. Gascuel,et al.  A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. , 2003, Systematic biology.

[15]  Michael E Alfaro,et al.  Comparative performance of Bayesian and AIC-based measures of phylogenetic model uncertainty. , 2006, Systematic biology.

[16]  J. Huelsenbeck,et al.  Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo. , 2004, Molecular biology and evolution.

[17]  K. Tamura,et al.  Model selection in the estimation of the number of nucleotide substitutions. , 1994, Molecular biology and evolution.

[18]  T. Jukes CHAPTER 24 – Evolution of Protein Molecules , 1969 .

[19]  K. Crandall,et al.  Selecting the best-fit model of nucleotide substitution. , 2001, Systematic biology.

[20]  D. Posada,et al.  Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. , 2004, Systematic biology.

[21]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[22]  Tal Pupko,et al.  Combining multiple data sets in a likelihood analysis: which models are the best? , 2002, Molecular biology and evolution.

[23]  Robert M. Miura,et al.  Some mathematical questions in biology : DNA sequence analysis , 1986 .

[24]  J. Zhang,et al.  Performance of likelihood ratio tests of evolutionary hypotheses under inadequate substitution models. , 1999, Molecular biology and evolution.

[25]  J. Felsenstein Evolutionary trees from DNA sequences: A maximum likelihood approach , 2005, Journal of Molecular Evolution.

[26]  S. Whelan,et al.  Statistical tests of gamma-distributed rate heterogeneity in models of sequence evolution in phylogenetics. , 2000, Molecular biology and evolution.

[27]  Jack Sullivan,et al.  Model Selection in Phylogenetics , 2005 .

[28]  C. Kelsey,et al.  Different models, different trees: the geographic origin of PTLV-I. , 1999, Molecular phylogenetics and evolution.

[29]  T. Ohta Theoretical study of near neutrality. II. Effect of subdivided population structure with local extinction and recolonization. , 1992, Genetics.

[30]  Nick Goldman,et al.  Statistical tests of models of DNA substitution , 1993, Journal of Molecular Evolution.

[31]  David R. Anderson,et al.  Model selection and inference : a practical information-theoretic approach , 2000 .

[32]  N. Sugiura Further analysts of the data by akaike' s information criterion and the finite corrections , 1978 .

[33]  Nick Goldman,et al.  MAXIMUM LIKELIHOOD TREES FROM DNA SEQUENCES: A PECULIAR STATISTICAL ESTIMATION PROBLEM , 1995 .

[34]  Emily C. Moriarty,et al.  The importance of proper model assumption in bayesian phylogenetics. , 2004, Systematic biology.

[35]  Jack Sullivan,et al.  Does choice in model selection affect maximum likelihood analysis? , 2008, Systematic biology.

[36]  A. Zharkikh Estimation of evolutionary distances between nucleotide sequences , 1994, Journal of Molecular Evolution.

[37]  Maurice G. Kendall,et al.  The advanced theory of statistics , 1945 .

[38]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[39]  M. Kendall,et al.  The advanced theory of statistics , 1945 .

[40]  Wasserman,et al.  Bayesian Model Selection and Model Averaging. , 2000, Journal of mathematical psychology.

[41]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[42]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[43]  D. Madigan,et al.  Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window , 1994 .

[44]  O Gascuel,et al.  BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. , 1997, Molecular biology and evolution.