Edwards, S. V. Is a new and general theory of molecular systematics emerging? Evolution63, 1–19 (2009). ArticleCASPubMed Google Scholar
Marra, M. A. et al. The genome sequence of the SARS-associated coronavirus. Science300, 1399–1404 (2003). ArticleCASPubMed Google Scholar
Grenfell, B. T. et al. Unifying the epidemiological and evolutionary dynamics of pathogens. Science303, 327–332 (2004). ArticleCASPubMed Google Scholar
Gray, R. D., Drummond, A. J. & Greenhill, S. J. Language phylogenies reveal expansion pulses and pauses in pacific settlement. Science323, 479–483 (2009). ArticleCASPubMed Google Scholar
Brady, A. & Salzberg, S. PhymmBL expanded: confidence scores, custom databases, parallelization and more. Nature Methods8, 367 (2011). ArticleCASPubMedPubMed Central Google Scholar
Kellis, M., Patterson, N., Endrizzi, M., Birren, B. & Lander, E. S. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature423, 241–254 (2003). ArticleCASPubMed Google Scholar
Pedersen, J. S. et al. Identification and classification of conserved RNA secondary structures in the human genome. PLoS Comput. Biol.2, e33 (2006). ArticleCASPubMedPubMed Central Google Scholar
Gronau, I., Hubisz, M. J., Gulko, B., Danko, C. G. & Siepel, A. Bayesian inference of ancient human demography from individual genome sequences. Nature Genet.43, 1031–1034 (2011). ArticleCASPubMed Google Scholar
Ma, J. Reconstructing the history of large-scale genomic changes: biological questions and computational challenges. J. Comput. Biol.18, 879–893 (2011). ArticleCASPubMed Google Scholar
Kingman, J. F. C. On the genealogy of large populations. J. Appl. Probab.19A, 27–43 (1982). Article Google Scholar
Kingman, J. F. C. The coalescent. Stoch. Process. Appl.13, 235–248 (1982). Article Google Scholar
Edwards, S. V., Liu, L. & Pearl, D. K. High-resolution species trees without concatenation. Proc. Natl Acad. Sci. USA104, 5936–5941 (2007). This paper introduces a method for estimating the species tree despite the presence of conflicting gene trees. ArticleCASPubMedPubMed Central Google Scholar
Rannala, B. & Yang, Z. Phylogenetic inference using whole genomes. Annu. Rev. Genomics Hum. Genet.9, 217–231 (2008). ArticleCASPubMed Google Scholar
Felsenstein, J. Phylogenies and the comparative method. Am. Nat.125, 1–15 (1985). This paper introduces the bootstrap approach to phylogenetic analysis. This is the most commonly used method for assessing sampling errors in estimated phylogenies. Article Google Scholar
Yang, Z. in Handbook of Statistical Genetics (eds Balding, D., Bishop, M. & Cannings, C.) 377–406 (Wiley, New York, 2007). Google Scholar
Felsenstein, J. Inferring Phylogenies (Sinauer Associates, Sunderland, Massachusetts, 2004). Google Scholar
Yang, Z. Computational Molecular Evolution (Oxford Univ. Press, UK, 2006). Book Google Scholar
Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol.4, 406–425 (1987). CASPubMed Google Scholar
Jukes, T. H. & Cantor, C. R. in Mammalian Protein Metabolism (ed. Munro, H. N.) 21–123 (Academic Press, New York, 1969). Book Google Scholar
Kimura, M. A simple method for estimating evolutionary rate of base substitution through comparative studies of nucleotide sequences. J. Mol. Evol.16, 111–120 (1980). ArticleCASPubMed Google Scholar
Hasegawa, M., Kishino, H. & Yano, T. Dating the human–ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evol.22, 160–174 (1985). ArticleCASPubMed Google Scholar
Tavaré, S. Some probabilistic and statistical problems on the analysis of DNA sequences. Lect. Math. Life Sci.17, 57–86 (1986). Google Scholar
Yang, Z. Estimating the pattern of nucleotide substitution. J. Mol. Evol.39, 105–111 (1994). PubMed Google Scholar
Yang, Z. Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Mol. Biol. Evol.10, 1396–1401 (1993). CASPubMed Google Scholar
Cavalli-Sforza, L. L. & Edwards, A. W. F. Phylogenetic analysis: models and estimation procedures. Evolution21, 550–570 (1967). ArticleCASPubMed Google Scholar
Fitch, W. M. & Margoliash, E. Construction of phylogenetic trees. Science155, 279–284 (1967). ArticleCASPubMed Google Scholar
Rzhetsky, A. & Nei, M. A simple method for estimating and testing minimum-evolution trees. Mol. Biol. Evol.9, 945–967 (1992). CAS Google Scholar
Desper, R. & Gascuel, O. Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle. J. Comput. Biol.9, 687–705 (2002). ArticleCASPubMed Google Scholar
Tamura, K. et al. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evol.28, 2731–2739 (2011). ArticleCASPubMedPubMed Central Google Scholar
Bruno, W. J., Socci, N. D. & Halpern, A. L. Weighted neighbor joining: a likelihood-based approach to distance-based phylogeny reconstruction. Mol. Biol. Evol.17, 189–197 (2000). ArticleCASPubMed Google Scholar
Fitch, W. M. Toward defining the course of evolution: minimum change for a specific tree topology. Syst. Zool.20, 406–416 (1971). Article Google Scholar
Hartigan, J. A. Minimum evolution fits to a given tree. Biometrics29, 53–65 (1973). Article Google Scholar
Swofford, D. L. PAUP*: Phylogenetic Analysis by Parsimony (and Other Methods)4.0 Beta (Sinauer Associates, Massachusetts, 2000). Google Scholar
Goloboff, P. A., Farris, J. S. & Nixon, K. C. TNT, a free program for phylogenetic analysis. Cladistics24, 774–786 (2008). Article Google Scholar
Felsenstein, J. Cases in which parsimony and compatibility methods will be positively misleading. Syst. Zool.27, 401–410 (1978). Article Google Scholar
Huelsenbeck, J. P. Systematic bias in phylogenetic analysis: is the Strepsiptera problem solved? Syst. Biol.47, 519–537 (1998). CASPubMed Google Scholar
Swofford, D. L. et al. Bias in phylogenetic estimation and its relevance to the choice between parsimony and likelihood methods. Syst. Biol.50, 525–539 (2001). ArticleCASPubMed Google Scholar
Yang, Z. Among-site rate variation and its impact on phylogenetic analyses. Trends Ecol. Evol.11, 367–372 (1996). ArticleCASPubMed Google Scholar
Felsenstein, J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol.17, 368–376 (1981). This paper introduces the pruning algorithm for likelihood calculation on a tree. This approach forms the basis for modern likelihood and Bayesian methods of phylogenetic analysis. ArticleCASPubMed Google Scholar
Yang, Z. Phylogenetic analysis using parsimony and likelihood methods. J. Mol. Evol.42, 294–307 (1996). ArticleCASPubMed Google Scholar
Felsenstein, J. Phylip: Phylogenetic Inference Program, Version 3.6. (Univ. of Washington, Seattle, 2005).
Adachi, J. & Hasegawa, M. MOLPHY version 2.3: programs for molecular phylogenetics based on maximum likelihood. Comput. Sci. Monogr.28, 1–150 (1996). Google Scholar
Guindon, S. & Gascuel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol.52, 696–704 (2003). ArticlePubMed Google Scholar
Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics22, 2688–2690 (2006). ArticleCASPubMed Google Scholar
Zwickl, D. Genetic Algorithm Approaches for the Phylogenetic Analysis of Large Biological Sequence Datasets Under the Maximum Likelihood Criterion. Thesis, Univ. Texas at Austin (2006). Google Scholar
Yang, Z. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J. Mol. Evol.39, 306–314 (1994). ArticleCASPubMed Google Scholar
Lartillot, N. & Philippe, H. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol.21, 1095–1109 (2004). ArticleCASPubMed Google Scholar
Blanquart, S. & Lartillot, N. A site- and time-heterogeneous model of amino acid replacement. Mol. Biol. Evol.25, 842–858 (2008). ArticleCASPubMed Google Scholar
Goldman, N. Statistical tests of models of DNA substitution. J. Mol. Evol.36, 182–198 (1993). ArticleCASPubMed Google Scholar
Zuckerkandl, E. & Pauling, L. in Evolving Genes and Proteins (eds Bryson, V. & Vogel, H. J.) 97–166 (Academic Press, New York, 1965). Book Google Scholar
Nielsen, R. & Yang, Z. Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics148, 929–936 (1998). ArticleCASPubMedPubMed Central Google Scholar
Yang, Z. Likelihood ratio tests for detecting positive selection and application to primate lysozyme evolution. Mol. Biol. Evol.15, 568–573 (1998). ArticleCASPubMed Google Scholar
Yang, Z. & Nielsen, R. Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. Mol. Biol. Evol.19, 908–917 (2002). ArticleCASPubMed Google Scholar
Huelsenbeck, J. P. & Rannala, B. Phylogenetic methods come of age: testing hypotheses in an evolutionary context. Science276, 227–232 (1997). ArticleCASPubMed Google Scholar
Whelan, S., Liò, P. & Goldman, N. Molecular phylogenetics: state of the art methods for looking into the past. Trends Genet.17, 262–272 (2001). ArticleCASPubMed Google Scholar
Rannala, B. & Yang, Z. Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference. J. Mol. Evol.43, 304–311 (1996). ArticleCASPubMed Google Scholar
Yang, Z. & Rannala, B. Bayesian phylogenetic inference using DNA sequences: a Markov chain Monte Carlo Method. Mol. Biol. Evol.14, 717–724 (1997). ArticleCASPubMed Google Scholar
Mau, B. & Newton, M. A. Phylogenetic inference for binary data on dendrograms using Markov chain Monte Carlo. J. Comput. Graph. Stat.6, 122–131 (1997). Google Scholar
Li, S., Pearl, D. & Doss, H. Phylogenetic tree reconstruction using Markov chain Monte Carlo. J. Am. Stat. Assoc.95, 493–508 (2000). Article Google Scholar
Larget, B. & Simon, D. L. Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Mol. Biol. Evol.16, 750–759 (1999). ArticleCAS Google Scholar
Huelsenbeck, J. P. & Ronquist, F. MrBayes: Bayesian inference of phylogenetic trees. Bioinformatics17, 754–755 (2001). ArticleCASPubMed Google Scholar
Drummond, A. J., Ho, S. Y. W., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol.4, e88 (2006). This paper introduces a Bayesian MCMC algorithm (the BEAST program) to estimate rooted trees under relaxed-clock models. ArticlePubMedPubMed CentralCAS Google Scholar
Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution39, 783–791 (1985). ArticlePubMed Google Scholar
Felsenstein, J. & Kishino, H. Is there something wrong with the bootstrap on phylogenies? A reply to Hillis and Bull. Syst. Biol.42, 193–200 (1993). Article Google Scholar
Efron, B., Halloran, E. & Holmes, S. Bootstrap confidence levels for phylogenetic trees. Proc. Natl Acad. Sci. USA93, 7085–7090 (1996); corrected article Proc. Natl Acad. Sci. USA93, 13429–13434 (1996). ArticleCASPubMedPubMed Central Google Scholar
Berry, V. & Gascuel, O. On the interpretation of bootstrap trees: appropriate threshold of clade selection and induced gain. Mol. Biol. Evol.13, 999–1011 (1996). ArticleCAS Google Scholar
Susko, E. First-order correct bootstrap support adjustments for splits that allow hypothesis testing when using maximum likelihood estimation. Mol. Biol. Evol.27, 1621–1629 (2010). ArticleCASPubMed Google Scholar
Suzuki, Y., Glazko, G. V. & Nei, M. Overcredibility of molecular phylogenies obtained by Bayesian phylogenetics. Proc. Natl Acad. Sci. USA99, 16138–16143 (2002). ArticleCASPubMedPubMed Central Google Scholar
Lewis, P. O., Holder, M. T. & Holsinger, K. E. Polytomies and Bayesian phylogenetic inference. Syst. Biol.54, 241–253 (2005). ArticlePubMed Google Scholar
Yang, Z. & Rannala, B. Branch-length prior influences Bayesian posterior probability of phylogeny. Syst. Biol.54, 455–470 (2005). ArticlePubMed Google Scholar
Huelsenbeck, J. P. & Rannala, B. Frequentist properties of Bayesian posterior probabilities of phylogenetic trees under simple and complex substitution models. Syst. Biol.53, 904–913 (2004). ArticlePubMed Google Scholar
Brown, J. M., Hedtke, S. M., Lemmon, A. R. & Lemmon, E. M. When trees grow too long: investigating the causes of highly inaccurate Bayesian branch-length estimates. Syst. Biol.59, 145–161 (2010). ArticlePubMed Google Scholar
Rannala, B., Zhu, T. & Yang, Z. Tail paradox, partial identifiability and influential priors in Bayesian branch length inference. Mol. Biol. Evol.29, 325–335 (2012). ArticleCASPubMed Google Scholar
Zhang, C., Rannala, B. & Yang, Z. Robustness of compound Dirichlet priors for Bayesian inference of branch lengths. Syst. Biol. 10 Feb 2012 (doi: 10.1093/sysbio/sys030). ArticlePubMed Google Scholar
Zierke, S. & Bakos, J. FPGA acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods. BMC Bioinform.11, 184 (2010). ArticleCAS Google Scholar
Bininda-Emonds, O. R. P. Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life (Kluwer Academic, the Netherlands, 2004). Book Google Scholar
de Queiroz, A. & Gatesy, J. The supermatrix approach to systematics. Trends Ecol. Evol.22, 34–41 (2007). ArticlePubMed Google Scholar
Yang, Z. Maximum-likelihood models for combined analyses of multiple sequence data. J. Mol. Evol.42, 587–596 (1996). ArticleCASPubMed Google Scholar
Shapiro, B., Rambaut, A. & Drummond, A. J. Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences. Mol. Biol. Evol.23, 7–9 (2006). ArticleCASPubMed Google Scholar
Ren, F., Tanaka, H. & Yang, Z. A likelihood look at the supermatrix–supertree controversy. Gene441, 119–125 (2009). ArticleCASPubMed Google Scholar
Criscuolo, A., Berry, V., Douzery, E. J. & Gascuel, O. SDM: a fast distance-based approach for (super) tree building in phylogenomics. Syst. Biol.55, 740–755 (2006). ArticlePubMed Google Scholar
Wiens, J. J. & Moen, D. S. Missing data and the accuracy of Bayesian phylogenetics. J. Syst. Evol.46, 307–314 (2008). Google Scholar
Dwivedi, B. & Gadagkar, S. Phylogenetic inference under varying proportions of indel-induced alignment gaps. BMC Evol. Biol.9, 1471–2148 (2009). ArticleCAS Google Scholar
Rodrigue, N., Philippe, H. & Lartillot, N. Mutation-selection models of coding sequence evolution with site-heterogeneous amino acid fitness profiles. Proc. Natl Acad. Sci. USA107, 4629–4634 (2010). ArticleCASPubMedPubMed Central Google Scholar
Pagel, M. & Meade, A. A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Syst. Biol.53, 571–581 (2004). ArticlePubMed Google Scholar
Nishihara, H., Okada, N. & Hasegawa, M. Rooting the Eutherian tree — the power and pitfalls of phylogenomics. Genome Biol.8, R199 (2007). ArticlePubMedPubMed CentralCAS Google Scholar
Leigh, J. W., Susko, E., Baumgartner, M. & Roger, A. J. Testing congruence in phylogenomic analysis. Syst. Biol.57, 104–115 (2008). ArticlePubMed Google Scholar
Higgins, D. G. & Sharp, P. M. CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene73, 237–244 (1988). ArticleCASPubMed Google Scholar
Löytynoja, A. & Goldman, N. An algorithm for progressive multiple alignment of sequences with insertions. Proc. Natl Acad. Sci. USA102, 10557–10562 (2005). ArticlePubMedCASPubMed Central Google Scholar
Löytynoja, A. & Goldman, N. Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science320, 1632–1635 (2008). ArticleCASPubMed Google Scholar
Thorne, J. L., Kishino, H. & Felsenstein, J. An evolutionary model for maximum likelihood alignment of DNA sequences. J. Mol. Evol.33, 114–124 (1991); erratum J. Mol. Evol. 34, 91 (1992). ArticleCASPubMed Google Scholar
Hein, J., Jensen, J. L. & Pedersen, C. N. Recursions for statistical multiple alignment. Proc. Natl Acad. Sci. USA100, 14960–14965 (2003). ArticleCASPubMedPubMed Central Google Scholar
Redelings, B. D. & Suchard, M. A. Joint Bayesian estimation of alignment and phylogeny. Syst. Biol.54, 401–418 (2005). ArticlePubMed Google Scholar
Lunter, G., Miklos, I., Drummond, A., Jensen, J. L. & Hein, J. Bayesian coestimation of phylogeny and sequence alignment. BMC Bioinformatics6, 83 (2005). ArticlePubMedPubMed CentralCAS Google Scholar
Thorne, J. L., Kishino, H. & Painter, I. S. Estimating the rate of evolution of the rate of molecular evolution. Mol. Biol. Evol.15, 1647–1657 (1998). This paper describes the first Bayesian MCMC method for dating species divergence using minimum and maximum bounds to incorporate fossil calibrations. ArticleCASPubMed Google Scholar
Kishino, H., Thorne, J. L. & Bruno, W. J. Performance of a divergence time estimation method under a probabilistic model of rate evolution. Mol. Biol. Evol.18, 352–361 (2001). ArticleCASPubMed Google Scholar
Rannala, B. & Yang, Z. Inferring speciation times under an episodic molecular clock. Syst. Biol.56, 453–466 (2007). ArticlePubMed Google Scholar
Yang, Z. & Rannala, B. Bayesian estimation of species divergence times under a molecular clock using multiple fossil calibrations with soft bounds. Mol. Biol. Evol.23, 212–226 (2006). ArticleCASPubMed Google Scholar
Inoue, J., Donoghue, P. C. H. & Yang, Z. The impact of the representation of fossil calibrations on Bayesian estimation of species divergence times. Syst. Biol.59, 74–89 (2010). ArticlePubMed Google Scholar
Tavaré, S., Marshall, C. R., Will, O., Soligos, C. & Martin, R. D. Using the fossil record to estimate the age of the last common ancestor of extant primates. Nature416, 726–729 (2002). ArticlePubMedCAS Google Scholar
Wilkinson, R. D. et al. Dating primate divergences through an integrated analysis of palaeontological and molecular data. Syst. Biol.60, 16–31 (2011). ArticleCASPubMed Google Scholar
Knowles, L. L. Statistical phylogeography. Annu. Rev. Ecol. Syst.40, 593–612 (2009). Article Google Scholar
Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Comp. Biol.5, e1000520 (2009). ArticleCAS Google Scholar
Lemey, P., Rambaut, A., Welch, J. J. & Suchard, M. A. Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evol.27, 1877–1885 (2010). ArticleCASPubMedPubMed Central Google Scholar
Takahata, N., Satta, Y. & Klein, J. Divergence time and population size in the lineage leading to modern humans. Theor. Popul. Biol.48, 198–221 (1995). ArticleCASPubMed Google Scholar
Rannala, B. & Yang, Z. Bayes estimation of species divergence times and ancestral population sizes using DNA sequences from multiple loci. Genetics164, 1645–1656 (2003). This study describes the multi-species coalescent model. This is the basis for carrying out comparative analyses of individual genomes and phylogeographic studies and for applying species tree methods. ArticleCASPubMedPubMed Central Google Scholar
Drummond, A. J., Nicholls, G. K., Rodrigo, A. G. & Solomon, W. Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data. Genetics161, 1307–1320 (2002). ArticleCASPubMedPubMed Central Google Scholar
Hey, J. Isolation with migration models for more than two populations. Mol. Biol. Evol.27, 905–920 (2010). ArticleCASPubMed Google Scholar
Knowles, L. L. & Carstens, B. C. Delimiting species without monophyletic gene trees. Syst. Biol.56, 887–895 (2007). ArticlePubMed Google Scholar
Yang, Z. & Rannala, B. Bayesian species delimitation using multilocus sequence data. Proc. Natl Acad. Sci. USA107, 9264–9269 (2010). This paper describes a Bayesian MCMC method for delimiting species using sequence data from multiple loci under the multi-species coalescent model. ArticleCASPubMedPubMed Central Google Scholar
Rohland, N. et al. Genomic DNA sequences from mastodon and woolly mammoth reveal deep speciation of forest and savanna elephants. PLoS Biol.8, e1000564 (2010). ArticleCASPubMedPubMed Central Google Scholar
Patterson, N., Richter, D. J., Gnerre, S., Lander, E. S. & Reich, D. Genetic evidence for complex speciation of humans and chimpanzees. Nature441, 1103–1108 (2006). ArticleCASPubMed Google Scholar
Innan, H. & Watanabe, H. The effect of gene flow on the coalescent time in the human–chimpanzee ancestral population. Mol. Biol. Evol.23, 1040–1047 (2006). ArticleCASPubMed Google Scholar
Becquet, C. & Przeworski, M. A new approach to estimate parameters of speciation models with application to apes. Genome Res.17, 1505–1519 (2007). ArticleCASPubMedPubMed Central Google Scholar
Hobolth, A., Christensen, O. F., Mailund, T. & Schierup, M. H. Genomic relationships and speciation times of human, chimpanzee, and gorilla inferred from a coalescent hidden Markov model. PLoS Genet.3, e7 (2007). ArticlePubMedPubMed CentralCAS Google Scholar
Burgess, R. & Yang, Z. Estimation of hominoid ancestral population sizes under Bayesian coalescent models incorporating mutation rate variation and sequencing errors. Mol. Biol. Evol.25, 1979–1994 (2008). ArticleCASPubMed Google Scholar
Becquet, C. & Przeworski, M. Learning about modes of speciation by computational approaches. Evolution63, 2547–2562 (2009). ArticlePubMed Google Scholar
Sitnikova, T., Rzhetsky, A. & Nei, M. Interior-branch and bootstrap tests of phylogenetic trees. Mol. Biol. Evol.12, 319–333 (1995). CASPubMed Google Scholar
Zhong, B., Yonezawa, T., Zhong, Y. & Hasegawa, M. The position of gnetales among seed plants: overcoming pitfalls of chloroplast phylogenomics. Mol. Biol. Evol.27, 2855–2863 (2010). ArticleCASPubMed Google Scholar
Kosakovsky Pond, S. L., Frost, S. D. W. & Muse, S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics21, 676–679 (2005). ArticleCAS Google Scholar
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol.24, 1586–1591 (2007). ArticleCASPubMed Google Scholar
Lartillot, N. & Philippe, H. Computing Bayes factors using thermodynamic integration. Syst. Biol.55, 195–207 (2006). ArticlePubMed Google Scholar
Xie, W., Lewis, P. O., Fan, Y., Kuo, L. & Chen, M.-H. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst. Biol.60, 150–160 (2011). ArticlePubMed Google Scholar