Skip to main content
SpringerLink
Log in

The Essentials of Computational Molecular Evolution

  • Protocol
  • First Online:
Evolutionary Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 855))

Abstract

In this chapter, we give a brief yet self-contained introduction to computational molecular evolution. In particular, we present the emergence of the use of likelihood-based methods, review the standard DNA substitution models, and introduce how model choice operates. We also present recent developments in inferring absolute dates and rates on a phylogeny and show how state-of-the-art models take inspiration from diffusion theory to link population genetics, which traditionally focuses at a taxonomic level under that of species, and molecular evolution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nei, M. and Kumar, S. (2000) Molecular evolution and phylogenetics. Oxford University Press, Oxford, UK.

    Google Scholar 

  2. Higgs, P. G. and Attwood, T. K. (2005) Bioinformatics and molecular evolution. Blackwell Pub, Malden, MA.

    Google Scholar 

  3. Yang, Z. (2006) Computational molecular evolution. Oxford University Press, Oxford, UK.

    Google Scholar 

  4. Balding, D. J., Bishop, M. J., and Cannings, C. (2007) Handbook of statistical genetics. John Wiley & Sons, 3rd ed edn, Chichester, UK.

    Google Scholar 

  5. Salemi, M., Vandamme, A.-M., and Lemey, P. (2009) The phylogenetic handbook: a practical approach to phylogenetic analysis and hypothesis testing. Cambridge University Press, 2nd edn, Cambridge, UK.

    Google Scholar 

  6. Aris-Brosou, S. and Xia, X. (2008) Phylogenetic analyses: A toolbox expanding towards Bayesian methods. Int J Plant Genomics, 2008, 683509.

    PubMed  Google Scholar 

  7. Rodrigue, N. and Philippe, H. (2010) Mechanistic revisions of phenomenological modeling strategies in molecular evolution. Trends Genet, 26, 248–52.

    Article  PubMed  CAS  Google Scholar 

  8. Yang, Z. (2000) Complexity of the simplest phylogenetic estimation problem. Proc Biol Sci, 267, 109–16.

    Article  PubMed  CAS  Google Scholar 

  9. Sober, E. (1988) Reconstructing the past: parsimony, evolution, and inference. MIT Press, Cambridge, MA.

    Google Scholar 

  10. Durbin, R. (1998) Biological sequence analysis: probabalistic models of proteins and nucleic acids. Cambridge University Press, Cambridge, UK.

    Google Scholar 

  11. Felsenstein, J. (2004) Inferring phylogenies. Sinauer Associates, Sunderland, MA.

    Google Scholar 

  12. Yang, Z. (2007) PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol, 24, 1586–91.

    Article  PubMed  CAS  Google Scholar 

  13. Efron, B. and Tibshirani, R. (1993) An introduction to the bootstrap, vol. 57. Chapman & Hall, New York, NY.

    Google Scholar 

  14. Efron, B., Halloran, E., and Holmes, S. (1996) Bootstrap confidence levels for phylogenetic trees. Proc Natl Acad Sci U S A, 93, 7085–90.

    Article  PubMed  CAS  Google Scholar 

  15. Felsenstein, J. (1985) Confidence limits on phylogenies: An approach using the bootstrap. Evolution, 39, 783–791.

    Article  Google Scholar 

  16. Baldauf, S. L. (2003) Phylogeny for the faint of heart: a tutorial. Trends Genet, 19, 345–51.

    Article  PubMed  CAS  Google Scholar 

  17. Hasegawa, M. and Kishino, H. (1989) Confidence limits of the maximum-likelihood estimate of the hominoid three from mitochondrial-DNA sequences. Evolution, 43, 672–677.

    Article  Google Scholar 

  18. Anisimova, M. and Gascuel, O. (2006) Approximate likelihood-ratio test for branches: A fast, accurate, and powerful alternative. Syst Biol, 55, 539–52.

    Article  PubMed  Google Scholar 

  19. Hillis, D. M. and Bull, J. J. (1993) An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis. Syst Biol, 42, pp. 182–192.

    Google Scholar 

  20. Felsenstein, J. and Kishino, H. (1993) Is there something wrong with the bootstrap on phylogenies? a reply to Hillis and Bull. Syst Biol, 42, pp. 193–200.

    Google Scholar 

  21. Yang, Z. and Rannala, B. (2005) Branch-length prior influences Bayesian posterior probability of phylogeny. Syst Biol, 54, 455–70.

    Article  PubMed  Google Scholar 

  22. Berry, V. and Gascuel, O. (1996) On the interpretation of bootstrap trees: Appropriate threshold of clade selection and induced gain. Mol Biol Evol, 13, 999.

    Article  CAS  Google Scholar 

  23. Shimodaira, H. and Hasegawa, M. (2001) CONSEL: for assessing the confidence of phylogenetic tree selection. Bioinformatics, 17, 1246–7.

    Article  PubMed  CAS  Google Scholar 

  24. Felsenstein, J. (1978) Cases in which parsimony or compatibility methods will be positively misleading. Syst Zool, 27, 401–410.

    Article  Google Scholar 

  25. Tuffley, C. and Steel, M. (1997) Links between maximum likelihood and maximum parsimony under a simple model of site substitution. Bull Math Biol, 59, 581–607.

    Article  PubMed  CAS  Google Scholar 

  26. Steel, M. and Penny, D. (2000) Parsimony, likelihood, and the role of models in molecular phylogenetics. Mol Biol Evol, 17, 839–50.

    Article  PubMed  CAS  Google Scholar 

  27. Holder, M. T., Lewis, P. O., and Swofford, D. L. (2010) The Akaike Information Criterion will not choose the no common mechanism model. Syst Biol, 59, 477–85.

    Article  PubMed  Google Scholar 

  28. Philippe, H., Zhou, Y., Brinkmann, H., Rodrigue, N., and Delsuc, F. (2005) Heterotachy and long-branch attraction in phylogenetics. BMC Evol Biol, 5, 50.

    Article  PubMed  CAS  Google Scholar 

  29. Brinkmann, H., van der Giezen, M., Zhou, Y., Poncelin de Raucourt, G., and Philippe, H. (2005) An empirical assessment of long-branch attraction artefacts in deep eukaryotic phylogenomics. Syst Biol, 54, 743–57.

    Article  PubMed  Google Scholar 

  30. Hampl, V., Hug, L., Leigh, J. W., Dacks, J. B., Lang, B. F., Simpson, A. G. B., and Roger, A. J. (2009) Phylogenomic analyses support the monophyly of Excavata and resolve relationships among eukaryotic “supergroups.” Proc Natl Acad Sci U S A, 106, 3859–64.

    Article  PubMed  CAS  Google Scholar 

  31. Liu, H., Aris-Brosou, S., Probert, I., and de Vargas, C. (2010) A timeline of the environmental genetics of the haptophytes. Mol Biol Evol, 27, 161–76.

    Article  PubMed  CAS  Google Scholar 

  32. Zuckerkandl, E. and Pauling, L. (1965) Evolutionary divergence and convergence in proteins. Bryson, V. and Vogel, H. J. (eds.), Evolving Genes and Proteins, pp. 97–166, Academic Press, New York, NY.

    Google Scholar 

  33. Galtier, N., Gascuel, O., and Jean-Marie, A. (2005) Markov models in molecular evolution. Nielsen, R. (ed.), Statistical Methods in Molecular Evolution, pp. 3–24, Statistics for Biology and Health, Springer, New York, NY.

    Google Scholar 

  34. Cox, D. R. and Miller, H. D. (1965) The theory of stochastic processes. Wiley, New York, NY.

    Google Scholar 

  35. Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol, 17, 368–76.

    Article  PubMed  CAS  Google Scholar 

  36. Jukes, J. C. and Cantor, C. R. (1969) Evolution of protein molecules. Munro, H. N. (ed.), Mammalian protein metabolism, pp. 21–123, Academic Press, New York, NY.

    Google Scholar 

  37. Kimura, M. (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol, 16, 111–20.

    Article  PubMed  CAS  Google Scholar 

  38. Hasegawa, M., Kishino, H., and Yano, T. (1985) Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol, 22, 160–74.

    Article  PubMed  CAS  Google Scholar 

  39. Tavaré, S. (1986) Some probabilistic and statistical problems in the analysis of DNA sequences. Lectures on Mathematics in the Life Sciences, 17, 57–86.

    Google Scholar 

  40. Huelsenbeck, J. P., Larget, B., and Alfaro, M. E. (2004) Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo. Mol Biol Evol, 21, 1123–33.

    Article  PubMed  CAS  Google Scholar 

  41. Yang, Z. and Roberts, D. (1995) On the use of nucleic acid sequences to infer early branchings in the tree of life. Mol Biol Evol, 12, 451–8.

    PubMed  CAS  Google Scholar 

  42. Huelsenbeck, J. P., Bollback, J. P., and Levine, A. M. (2002) Inferring the root of a phylogenetic tree. Syst Biol, 51, 32–43.

    Article  PubMed  Google Scholar 

  43. Yang, Z. (2000) Maximum likelihood estimation on large phylogenies and analysis of adaptive evolution in human influenza virus A. J Mol Evol, 51, 423–32.

    PubMed  CAS  Google Scholar 

  44. Aris-Brosou, S. (2005) Determinants of adaptive evolution at the molecular level: the extended complexity hypothesis. Mol Biol Evol, 22, 200–9.

    Article  PubMed  CAS  Google Scholar 

  45. Burnham, K. P. and Anderson, D. R. (1998) Model selection and inference: a practical information-theoretic approach. Springer, New York, NY.

    Google Scholar 

  46. Anisimova, M., Bielawski, J. P., and Yang, Z. (2001) Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution. Mol Biol Evol, 18, 1585–92.

    Article  PubMed  CAS  Google Scholar 

  47. Whelan, S. and Goldman, N. (2004) Estimating the frequency of events that cause multiple-nucleotide changes. Genetics, 167, 2027–43.

    Article  PubMed  CAS  Google Scholar 

  48. Wong, W. S. W., Yang, Z., Goldman, N., and Nielsen, R. (2004) Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics, 168, 1041–51.

    Article  PubMed  CAS  Google Scholar 

  49. Massingham, T. and Goldman, N. (2005) Detecting amino acid sites under positive selection and purifying selection. Genetics, 169, 1753–62.

    Article  PubMed  CAS  Google Scholar 

  50. Zhang, J., Nielsen, R., and Yang, Z. (2005) Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol, 22, 2472–9.

    Article  PubMed  CAS  Google Scholar 

  51. Anisimova, M. and Yang, Z. (2007) Multiple hypothesis testing to detect lineages under positive selection that affects only a few sites. Mol Biol Evol, 24, 1219–28.

    Article  PubMed  CAS  Google Scholar 

  52. Yang, Z. (2010) A likelihood ratio test of speciation with gene flow using genomic sequence data. Genome Biol Evol, 2, 200–11.

    Article  PubMed  CAS  Google Scholar 

  53. Fletcher, W. and Yang, Z. (2010) The effect of insertions, deletions, and alignment errors on the branch-site test of positive selection. Mol Biol Evol, 27, 2257–67.

    Article  PubMed  CAS  Google Scholar 

  54. Yang, Z. and dos Reis, M. (2011) Statistical properties of the branch-site test of positive selection. Mol Biol Evol, 28, 1217–28.

    Google Scholar 

  55. Self, S. G. and Liang, K.-Y. (1987) Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. JASA, 82, 605–610.

    Article  Google Scholar 

  56. Posada, D. and Crandall, K. A. (1998) MODELTEST: testing the model of DNA substitution. Bioinformatics, 14, 817–8.

    Article  PubMed  CAS  Google Scholar 

  57. Posada, D. (2008) jModelTest: phylogenetic model averaging. Mol Biol Evol, 25, 1253–6.

    Article  PubMed  CAS  Google Scholar 

  58. Cunningham, C. W., Zhu, H., and Hillis, D. M. (1998) Best-fit maximum-likelihood models for phylogenetic inference: Empirical tests with known phylogenies. Evolution, 52, 978–987.

    Article  Google Scholar 

  59. Pol, D. (2004) Empirical problems of the hierarchical likelihood ratio test for model selection. Syst Biol, 53, 949–62.

    Article  PubMed  Google Scholar 

  60. Kullback, S. and Leibler, R. A. (1951) On information and sufficiency. Ann Math Stat, 22, 79–86.

    Article  Google Scholar 

  61. Minin, V., Abdo, Z., Joyce, P., and Sullivan, J. (2003) Performance-based selection of likelihood models for phylogeny estimation. Syst Biol, 52, 674–83.

    Article  PubMed  Google Scholar 

  62. Ripplinger, J. and Sullivan, J. (2008) Does choice in model selection affect maximum likelihood analysis? Syst Biol, 57, 76–85.

    Article  PubMed  Google Scholar 

  63. Posada, D. and Crandall, K. A. (2001) Selecting the best-fit model of nucleotide substitution. Syst Biol, 50, 580–601.

    Article  PubMed  CAS  Google Scholar 

  64. Abdo, Z., Minin, V. N., Joyce, P., and Sullivan, J. (2005) Accounting for uncertainty in the tree topology has little effect on the decision-theoretic approach to model selection in phylogeny estimation. Mol Biol Evol, 22, 691–703.

    Article  PubMed  CAS  Google Scholar 

  65. Luo, A., Qiao, H., Zhang, Y., Shi, W., Ho, S. Y., Xu, W., Zhang, A., and Zhu, C. (2010) Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets. BMC Evol Biol, 10, 242.

    Article  PubMed  CAS  Google Scholar 

  66. Schwarz, G. (1978) Estimating the dimension of a model. Ann Stat, 6, 461–464.

    Article  Google Scholar 

  67. Evans, J. and Sullivan, J. (2011) Approximating model probabilities in Bayesian Information Criterion and Decision-Theoretic approaches to model selection in phylogenetics. Mol Biol Evol, 28, 343–9.

    Google Scholar 

  68. Kleinman, C. L., Rodrigue, N., Bonnard, C., Philippe, H., and Lartillot, N. (2006) A maximum likelihood framework for protein design. BMC Bioinformatics, 7, 326.

    Article  PubMed  Google Scholar 

  69. Rodrigue, N., Philippe, H., and Lartillot, N. (2007) Exploring fast computational strategies for probabilistic phylogenetic analysis. Syst Biol, 56, 711–26.

    Article  PubMed  Google Scholar 

  70. Yang, Z. (2005) Bayesian inference in molecular phylogenetics. Gascuel, O. (ed.), Mathematics of Evolution and Phylogeny, Chap. 3, pp. 63–90, Oxford University Press, Oxford, UK.

    Google Scholar 

  71. Jeffreys, H. (1939) Theory of probability. The International series of monographs on physics, The Clarendon press, Oxford, UK.

    Google Scholar 

  72. Kass, R. E. and Raftery, A. E. (1995) Bayes factors. JASA, 90, 773–795.

    Article  Google Scholar 

  73. Lartillot, N. and Philippe, H. (2006) Computing Bayes factors using thermodynamic integration. Syst Biol, 55, 195–207.

    Article  PubMed  Google Scholar 

  74. Fan, Y., Wu, R., Chen, M.-H., Kuo, L., and Lewis, P. O. (2011) Choosing among partition models in Bayesian phylogenetics. Mol Biol Evol, 28, 523–32.

    Google Scholar 

  75. Newton, M. A. and Raftery, A. E. (1994) Approximating Bayesian inference with the weighted likelihood bootstrap. J R Stat Soc B, 56, 3–48.

    Google Scholar 

  76. Aris-Brosou, S. (2003) How Bayes tests of molecular phylogenies compare with frequentist approaches. Bioinformatics, 19, 618–24.

    Article  PubMed  CAS  Google Scholar 

  77. Ronquist, F. and Huelsenbeck, J. P. (2003) Mrbayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 19, 1572–4.

    Article  PubMed  CAS  Google Scholar 

  78. Drummond, A. J. and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol, 7, 214.

    Article  PubMed  CAS  Google Scholar 

  79. Raftery, A. E. (1996) Hypothesis testing and model selection. Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (eds.), Markov chain Monte Carlo in practice, pp. 163–187, Chapman & Hall, Boca Raton, FL.

    Google Scholar 

  80. Xie, W., Lewis, P., Fan, Y., Kuo, L., and Chen, M.-H. (2011) Improving marginal likelihood estimation for Baysian phylogenetic model selection. Syst. Biol, 60, 150–60.

    Google Scholar 

  81. Smyth, P. (2000) Model selection for probabilistic clustering using cross-validated likelihood. Statistics and Computing, 10, 63–72.

    Article  Google Scholar 

  82. Lartillot, N., Brinkmann, H., and Philippe, H. (2007) Suppression of long-branch attraction artefacts in the animal phylogeny using a site-heterogeneous model. BMC Evol Biol, 7 Suppl 1, S4.

    Google Scholar 

  83. Cavalli-Sforza, L. L. and Edwards, A. W. (1967) Phylogenetic analysis. models and estimation procedures. Am J Hum Genet, 19, 233–57.

    PubMed  CAS  Google Scholar 

  84. Aris-Brosou, S. (2003) Least and most powerful phylogenetic tests to elucidate the origin of the seed plants in the presence of conflicting signals under misspecified models. Syst Biol, 52, 781–93.

    PubMed  Google Scholar 

  85. Foulds, L. R., Penny, D., and Hendy, M. D. (1979) A general approach to proving the minimality of phylogenetic trees illustrated by an example with a set of 23 vertebrates. J Mol Evol, 13, 151–166.

    Article  PubMed  CAS  Google Scholar 

  86. Hendy, M. D. and Penny, D. (1982) Branch and bound algorithms to determine minimal evolutionary trees. Math Biosci, 59, 277–290.

    Article  Google Scholar 

  87. Saitou, N. and Nei, M. (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol, 4, 406–25.

    PubMed  CAS  Google Scholar 

  88. Gascuel, O. (1997) BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Mol Biol Evol, 14, 685–95.

    Article  PubMed  CAS  Google Scholar 

  89. Bruno, W. J., Socci, N. D., and Halpern, A. L. (2000) Weighted neighbor joining: a likelihood-based approach to distance-based phylogeny reconstruction. Mol Biol Evol, 17, 189–97.

    Article  PubMed  CAS  Google Scholar 

  90. Guindon, S., Dufayard, J.-F., Lefort, V., Anisimova, M., Hordijk, W., and Gascuel, O. (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol, 59, 307–21.

    Article  PubMed  CAS  Google Scholar 

  91. Larget, B. and Simon, D. (1999) Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Mol Biol Evol, 16, 750.

    Article  CAS  Google Scholar 

  92. Holder, M. T., Lewis, P. O., Swofford, D. L., and Larget, B. (2005) Hastings ratio of the LOCAL proposal used in Bayesian phylogenetics. Syst Biol, 54, 961–5.

    Article  PubMed  Google Scholar 

  93. Whelan, S. (2007) New approaches to phylogenetic tree search and their application to large numbers of protein alignments. Syst Biol, 56, 727–40.

    Article  PubMed  Google Scholar 

  94. Benton, M. J. and Ayala, F. J. (2003) Dating the tree of life. Science, 300, 1698–700.

    Article  PubMed  CAS  Google Scholar 

  95. Rannala, B. and Yang, Z. (2007) Inferring speciation times under an episodic molecular clock. Syst Biol, 56, 453–66.

    Article  PubMed  Google Scholar 

  96. Wegmann, D., Leuenberger, C., and Excoffier, L. (2009) Efficient approximate Bayesian computation coupled with Markov chain Monte Carlo without likelihood. Genetics, 182, 1207–18.

    Article  PubMed  Google Scholar 

  97. Reich, D., et al. (2010) Genetic history of an archaic hominin group from denisova cave in siberia. Nature, 468, 1053–60.

    Article  PubMed  CAS  Google Scholar 

  98. Hedges, S. B., Dudley, J., and Kumar, S. (2006) TimeTree: a public knowledge-base of divergence times among organisms. Bioinformatics, 22, 2971–2.

    Article  PubMed  CAS  Google Scholar 

  99. Welch, J. J. and Bromham, L. (2005) Molecular dating when rates vary. Trends Ecol Evol, 20, 320–7.

    Article  PubMed  Google Scholar 

  100. Kimura, M. (1983) The neutral theory of molecular evolution. Cambridge University Press, Cambridge, UK.

    Google Scholar 

  101. Sarich, V. M. and Wilson, A. C. (1973) Generation time and genomic evolution in primates. Science, 179, 1144–7.

    Article  PubMed  CAS  Google Scholar 

  102. Muse, S. V. and Weir, B. S. (1992) Testing for equality of evolutionary rates. Genetics, 132, 269–76.

    PubMed  CAS  Google Scholar 

  103. Bromham, L., Penny, D., Rambaut, A., and Hendy, M. D. (2000) The power of relative rates tests depends on the data. J Mol Evol, 50, 296–301.

    PubMed  CAS  Google Scholar 

  104. Rambaut, A. (2000) Estimating the rate of molecular evolution: incorporating non-contemporaneous sequences into maximum likelihood phylogenies. Bioinformatics, 16, 395–9.

    Article  PubMed  CAS  Google Scholar 

  105. Martin, A. P. (2001) Molecular clocks. Encyclopedia of Life Sciences, pp. 1–6, Nature Pub Group, New York, NY.

    Google Scholar 

  106. Wray, G. A., Levinton, J. S., and Shapiro, L. H. (1996) Molecular evidence for deep Precambrian divergences among Metazoan phyla. Science, 274, 568–573.

    Article  CAS  Google Scholar 

  107. Kumar, S. and Hedges, S. B. (1998) A molecular timescale for vertebrate evolution. Nature, 392, 917–20.

    Article  PubMed  CAS  Google Scholar 

  108. Wang, D. Y., Kumar, S., and Hedges, S. B. (1999) Divergence time estimates for the early history of animal phyla and the origin of plants, animals and fungi. Proc Biol Sci, 266, 163–71.

    Article  PubMed  CAS  Google Scholar 

  109. Heckman, D. S., Geiser, D. M., Eidell, B. R., Stauffer, R. L., Kardos, N. L., and Hedges, S. B. (2001) Molecular evidence for the early colonization of land by fungi and plants. Science, 293, 1129–33.

    Article  PubMed  CAS  Google Scholar 

  110. Hedges, S. B., Chen, H., Kumar, S., Wang, D. Y., Thompson, A. S., and Watanabe, H. (2001) A genomic timescale for the origin of eukaryotes. BMC Evol Biol, 1, 4.

    Article  PubMed  CAS  Google Scholar 

  111. Graur, D. and Martin, W. (2004) Reading the entrails of chickens: molecular timescales of evolution and the illusion of precision. Trends Genet, 20, 80–6.

    Article  PubMed  CAS  Google Scholar 

  112. Rambaut, A. and Bromham, L. (1998) Estimating divergence dates from molecular sequences. Mol Biol Evol, 15, 442–8.

    Article  PubMed  CAS  Google Scholar 

  113. Yoder, A. D. and Yang, Z. (2000) Estimation of primate speciation dates using local molecular clocks. Mol Biol Evol, 17, 1081–90.

    Article  PubMed  CAS  Google Scholar 

  114. Yang, Z. (2004) A heuristic rate smoothing procedure for maximum likelihood estimation of species divergence times. Acta Zool Sinica, 50, 645–56.

    Google Scholar 

  115. Aris-Brosou, S. (2007) Dating phylogenies with hybrid local molecular clocks. PLoS One, 2, e879.

    Article  PubMed  CAS  Google Scholar 

  116. Drummond, A. J. and Suchard, M. A. (2010) Bayesian random local clocks, or one rate to rule them all. BMC Biol, 8, 114.

    Article  PubMed  Google Scholar 

  117. Sanderson, M. (1997) A nonparametric approach to estimating divergence times in the absence of rate constancy. Mol Biol Evol, 14, 1218.

    Article  CAS  Google Scholar 

  118. Sanderson, M. J. (2002) Estimating absolute rates of molecular evolution and divergence times: A penalized likelihood approach. Mol Biol Evol, 19, 101–109.

    Article  PubMed  CAS  Google Scholar 

  119. Gillespie, J. H. (1991) The causes of molecular evolution. Oxford University Press, New York, NY.

    Google Scholar 

  120. Thorne, J. L., Kishino, H., and Painter, I. S. (1998) Estimating the rate of evolution of the rate of molecular evolution. Mol Biol Evol, 15, 1647–57.

    Article  PubMed  CAS  Google Scholar 

  121. Aris-Brosou, S. and Yang, Z. (2002) Effects of models of rate evolution on estimation of divergence dates with special reference to the metazoan 18S ribosomal RNA phylogeny. Syst Biol, 51, 703–14.

    Article  PubMed  Google Scholar 

  122. Aris-Brosou, S. and Yang, Z. (2003) Bayesian models of episodic evolution support a late precambrian explosive diversification of the Metazoa. Mol Biol Evol, 20, 1947–54.

    Article  PubMed  CAS  Google Scholar 

  123. Rannala, B. and Yang, Z. (1996) Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference. J Mol Evol, 43, 304–11.

    Article  PubMed  CAS  Google Scholar 

  124. Hein, J., Schierup, M. H., and Wiuf, C. (2005) Gene genealogies, variation and evolution: a primer in coalescent theory. Oxford University Press, Oxford, UK.

    Google Scholar 

  125. Pybus, O. G., Rambaut, A., and Harvey, P. H. (2000) An integrated framework for the inference of viral population history from reconstructed genealogies. Genetics, 155, 1429–37.

    PubMed  CAS  Google Scholar 

  126. Drummond, A. J., Rambaut, A., Shapiro, B., and Pybus, O. G. (2005) Bayesian coalescent inference of past population dynamics from molecular sequences. Mol Biol Evol, 22, 1185–92.

    Article  PubMed  CAS  Google Scholar 

  127. Minin, V. N., Bloomquist, E. W., and Suchard, M. A. (2008) Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. Mol Biol Evol, 25, 1459–71.

    Article  PubMed  CAS  Google Scholar 

  128. Hedges, S. B. and Kumar, S. (2004) Precision of molecular time estimates. Trends Genet, 20, 242–7.

    Article  PubMed  CAS  Google Scholar 

  129. Yang, Z. and Rannala, B. (2006) Bayesian estimation of species divergence times under a molecular clock using multiple fossil calibrations with soft bounds. Mol Biol Evol, 23, 212–26.

    Article  PubMed  CAS  Google Scholar 

  130. Inoue, J., Donoghue, P. C. J., and Yang, Z. (2010) The impact of the representation of fossil calibrations on Bayesian estimation of species divergence times. Syst Biol, 59, 74–89.

    Article  PubMed  Google Scholar 

  131. Drummond, A. J., Ho, S. Y. W., Phillips, M. J., and Rambaut, A. (2006) Relaxed phylogenetics and dating with confidence. PLoS Biol, 4, e88.

    Article  PubMed  CAS  Google Scholar 

  132. Wertheim, J. O., Sanderson, M. J., Worobey, M., and Bjork, A. (2010) Relaxed molecular clocks, the bias-variance trade-off, and the quality of phylogenetic inference. Syst Biol, 59, 1–8.

    Article  PubMed  CAS  Google Scholar 

  133. Lemey, P., Rambaut, A., Drummond, A. J., and Suchard, M. A. (2009) Bayesian phylogeography finds its roots. PLoS Comput Biol, 5, e1000520.

    Article  PubMed  CAS  Google Scholar 

  134. Lemey, P., Rambaut, A., Welch, J. J., and Suchard, M. A. (2010) Phylogeography takes a relaxed random walk in continuous space and time. Mol Biol Evol, 27, 1877–85.

    Article  PubMed  CAS  Google Scholar 

  135. Guillot, G., Santos, F., and Estoup, A. (2008) Analysing georeferenced population genetics data with geneland: a new algorithm to deal with null alleles and a friendly graphical user interface. Bioinformatics, 24, 1406–7.

    Article  PubMed  CAS  Google Scholar 

  136. Nadin-Davis, S. A., Feng, Y., Mousse, D., Wandeler, A. I., and Aris-Brosou, S. (2010) Spatial and temporal dynamics of rabies virus variants in big brown bat populations across Canada: footprints of an emerging zoonosis. Mol Ecol, 19, 2120–36.

    Article  PubMed  CAS  Google Scholar 

  137. Pagel, M. and Meade, A. (2004) A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Syst Biol, 53, 571–81.

    Article  PubMed  Google Scholar 

  138. Pagel, M., Meade, A., and Barker, D. (2004) Bayesian estimation of ancestral character states on phylogenies. Syst Biol, 53, 673–84.

    Article  PubMed  Google Scholar 

  139. Lartillot, N. and Poujol, R. (2011) A phylogenetic model for investigating correlated evolution of substitution rates and continuous phenotypic characters. Mol Biol Evol, 28, 729–44.

    Google Scholar 

  140. Bromham, L., Woolfit, M., Lee, M. S. Y., and Rambaut, A. (2002) Testing the relationship between morphological and molecular rates of change along phylogenies. Evolution, 56, 1921–30.

    PubMed  Google Scholar 

  141. Genomes Project Consortium, Durbin, R. M., Abecasis, G. R., Altshuler, D. L., Auton, A., Brooks, L. D., Durbin, R. M., Gibbs, R. A., Hurles, M. E., and McVean, G. A. (2010) A map of human genome variation from population-scale sequencing. Nature, 467, 1061–73.

    Article  CAS  Google Scholar 

  142. Muse, S. V. and Gaut, B. S. (1994) A likelihood approach for comparing synonymous and nonsynonymous nucleotide substitution rates, with application to the chloroplast genome. Mol Biol Evol, 11, 715–24.

    PubMed  CAS  Google Scholar 

  143. Goldman, N. and Yang, Z. (1994) A codon-based model of nucleotide substitution for protein-coding DNA sequences. Mol Biol Evol, 11, 725–36.

    PubMed  CAS  Google Scholar 

  144. Kosiol, C. and Anisimova, M. (2012) Methods for detecting natural selection in protein-coding genes. In Anisimova, M., (ed.), Evolutionary genomics: statistical and computational methods (volume 1). Methods in Molecular Biology, Springer Science+Business media, LLC.

    Google Scholar 

  145. Thorne, J. L., Choi, S. C., Yu, J., Higgs, P. G., and Kishino, H. (2007) Population genetics without intraspecific data. Mol Biol Evol, 24, 1667–77.

    Article  PubMed  CAS  Google Scholar 

  146. Choi, S. C., Hobolth, A., Robinson, D. M., Kishino, H., and Thorne, J. L. (2007) Quantifying the impact of protein tertiary structure on molecular evolution. Mol Biol Evol, 24, 1769–82.

    Article  PubMed  CAS  Google Scholar 

  147. Halpern, A. L. and Bruno, W. J. (1998) Evolutionary distances for protein-coding sequences: modeling site-specific residue frequencies. Mol Biol Evol, 15, 910–7.

    Article  PubMed  CAS  Google Scholar 

  148. Yang, Z. and Nielsen, R. (2008) Mutation-selection models of codon substitution and their use to estimate selective strengths on codon usage. Mol Biol Evol, 25, 568–79.

    Article  PubMed  CAS  Google Scholar 

  149. Rodrigue, N., Philippe, H., and Lartillot, N. (2010) Mutation-selection models of coding sequence evolution with site-heterogeneous amino acid fitness profiles. Proc Natl Acad Sci U S A, 107, 4629–34.

    Article  PubMed  CAS  Google Scholar 

  150. Choi, S. C., Redelings, B. D., and Thorne, J. L. (2008) Basing population genetic inferences and models of molecular evolution upon desired stationary distributions of DNA or protein sequences. Philos Trans R Soc Lond B Biol Sci, 363, 3931–9.

    Article  PubMed  CAS  Google Scholar 

  151. Hartl, D. L. and Clark, A. G. (2007) Principles of population genetics. Sinauer Associates, 4th ed edn, Sunderland, MA.

    Google Scholar 

  152. Kimura, M. (1962) On the probability of fixation of mutant genes in a population. Genetics, 47, 713–9.

    PubMed  CAS  Google Scholar 

  153. Rice, S. H. (2004) Evolutionary theory: mathematical and conceptual foundations. Sinauer Associates, Sunderland, MA.

    Google Scholar 

  154. Kimura, M. (1978) Change of gene frequencies by natural selection under population number regulation. Proc Natl Acad Sci U S A, 75, 1934–7.

    Article  PubMed  CAS  Google Scholar 

  155. Prins, P., Belhachemi, D., Möller, S., and Smant, G. (2012) Scalable computing in evolutionary genomics. In Anisimova, M. (ed.), Evolutionary genomics: statistical and computational methods (volume 1). Methods in Molecular Biology, Springer Science+Business media, LLC.

    Google Scholar 

  156. Yang, Z. (1993) Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Mol Biol Evol, 10, 1396–401.

    PubMed  CAS  Google Scholar 

  157. Yang, Z. (1994) Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J Mol Evol, 39, 306–14.

    Article  PubMed  CAS  Google Scholar 

  158. Altekar, G., Dwarkadas, S., Huelsenbeck, J. P., and Ronquist, F. (2004) Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics, 20, 407–15.

    Article  PubMed  CAS  Google Scholar 

  159. Stamatakis, A., Hoover, P., and Rougemont, J. (2008) A rapid bootstrap algorithm for the RAxML Web servers. Syst Biol, 57, 758–71.

    Article  PubMed  Google Scholar 

  160. Hastie, T., Tibshirani, R., and Friedman, J. H. (2009) The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics, Springer, 2nd ed edn, New York, NY.

    Google Scholar 

  161. Stamatakis, A., Ludwig, T., and Meier, H. (2005) RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees. Bioinformatics, 21, 456–63.

    Article  PubMed  CAS  Google Scholar 

  162. Stamatakis, A., Göker, M., and Grimm, G. W. (2010) Maximum likelihood analyses of 3,490 rbcL sequences: Scalability of comprehensive inference versus group-specific taxon sampling. Evol Bioinform Online, 6, 73–90.

    Article  PubMed  CAS  Google Scholar 

  163. Stamatakis, A. and Alachiotis, N. (2010) Time and memory efficient likelihood-based tree searches on phylogenomic alignments with missing data. Bioinformatics, 26, i132–9.

    Article  PubMed  CAS  Google Scholar 

  164. Suchard, M. A. and Rambaut, A. (2009) Many-core algorithms for statistical phylogenetics. Bioinformatics, 25, 1370–6.

    Article  PubMed  CAS  Google Scholar 

  165. Schatz, M. C., Langmead, B., and Salzberg, S. L. (2010) Cloud computing and the DNA data race. Nat Biotechnol, 28, 691–3.

    Article  PubMed  CAS  Google Scholar 

  166. Dereeper, A., et al. (2008) Phylogeny.fr: robust phylogenetic analysis for the non-specialist. Nucleic Acids Res, 36, W465–9.

    Article  PubMed  CAS  Google Scholar 

  167. de Koning, A. P. J., Gu, W., and Pollock, D. D. (2010) Rapid likelihood analysis on large phylogenies using partial sampling of substitution histories. Mol Biol Evol, 27, 249–65.

    Article  PubMed  CAS  Google Scholar 

  168. Anisimova, M. and Yang, Z. (2004) Molecular evolution of the hepatitis delta virus antigen gene: recombination or positive selection? J Mol Evol, 59, 815–26.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We would like to thank Michelle Brazeau, Eric Chen, Ilya Hekimi, Benoît Pagé, and, in particular, Wayne Sawtell for their critical reading of a draft of this chapter. This work was partly supported by the Natural Sciences Research Council of Canada (N.R., S.A.B.) and the University of Ottawa (S.A.B.).

Author information

Authors and Affiliations

  1. Departments of Biology and Mathematics & Statistics and Center for Advanced Research in Environmental Genomics, University of Ottawa, Ottawa, ON, Canada

    Stéphane Aris-Brosou

  2. Department of Biology and Center for Advanced Research in Environmental Genomics, University of Ottawa, Ottawa, ON, Canada

    Nicolas Rodrigue

  3. Quebec Center for Biodiversity Science, McGill University, Montreal, QC, Canada

    Nicolas Rodrigue

  4. Agriculture and Agri-Food Canada, Eastern Cereal and Oilseeds Research Center, Central Experimental Farm, Ottawa, ON, Canada

    Nicolas Rodrigue

Authors
  1. Stéphane Aris-Brosou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicolas Rodrigue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stéphane Aris-Brosou .

Editor information

Editors and Affiliations

  1. Department of Computer Science, ETH Zürich, Universitätsstr. 6, Zürich, 8092, Switzerland

    Maria Anisimova

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Aris-Brosou, S., Rodrigue, N. (2012). The Essentials of Computational Molecular Evolution. In: Anisimova, M. (eds) Evolutionary Genomics. Methods in Molecular Biology, vol 855. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-582-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-582-4_4

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-61779-581-7

  • Online ISBN: 978-1-61779-582-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation