Advertisement

Maximum Parsimony Method in Phylogenetics

  • Xuhua Xia
Chapter

Abstract

A phylogenetic tree offers a simple way of visualizing evolutionary history. Out of many possible trees one can draw to link different species, we must have a criterion to choose the best tree, just as we need a criterion to choose the best alignment (the highest alignment score given the scoring scheme) or the best substitution model (the smallest information-theoretic indices). Maximum parsimony (MP) is one of the criteria for choosing the best phylogenetic tree, i.e., the tree that requires the smallest number of changes to account for the sequence variation is the best tree. This chapter details two frequently used algorithms used in the MP framework to obtain the minimum number of changes: the Fitch algorithm which assumes all nucleotides or amino acids replacing each other with equal frequency, and the Sankoff algorithm which uses the cost matrix to accommodate differential substitution rate among different nucleotides or different amino acids. Both belong to the dynamic programming algorithm we were first exposed to in the chapter on sequence alignment. The long-branch attraction problem, which is associated not only with MP method but also with other methods that do not correct, or insufficiently correct, for multiple substitutions, is numerically illustrated in detail. Two different ways of reporting statistical support for a phylogenetic tree are presented: the resampling method (bootstrapping and jackknifing) and the statistical test of alternative topologies. Various ways of improving the power of the statistical tests are included and discussed.

References

  1. Akaike H (1973) Information theory and an extension of maximum likelihood principle. In: Petrov BN, Csaki F (eds) Second international symposium on information theory. Akademiai Kiado, Budapest, pp 267–281Google Scholar
  2. Brooks DR, McLennan DA (1991) Phylogeny, ecology and behavior: a research program in comparative biology. University of Chicago Press, ChicagoGoogle Scholar
  3. Chithambaram S, Prabhakaran R, Xia X (2014a) Differential codon adaptation between dsDNA and ssDNA phages in escherichia coli. Mol Biol Evol 31(6):1606–1617CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chithambaram S, Prabhakaran R, Xia X (2014b) The effect of mutation and selection on codon adaptation in escherichia coli bacteriophage. Genetics 197(1):301–315CrossRefPubMedPubMedCentralGoogle Scholar
  5. Drouin G, Daoud H, Xia J (2008) Relative rates of synonymous substitutions in the mitochondrial, chloroplast and nuclear genomes of seed plants. Mol Phylogenet Evol 49(3):827–831CrossRefPubMedGoogle Scholar
  6. Efron B (1982) The jackknife, the bootstrap and other resampling plans. Society for Industrial and Applied Mathematics, PhiladelphiaCrossRefGoogle Scholar
  7. Felsenstein J (1978a) Cases in which parsimony and compatibility methods will be positively misleading. Syst Zool 27:401–410CrossRefGoogle Scholar
  8. Felsenstein J (1978b) The number of evolutionary trees. Syst Zool 27:27–33CrossRefGoogle Scholar
  9. Felsenstein J (1985) Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39:783–791CrossRefGoogle Scholar
  10. Felsenstein J, Churchill GA (1996) A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol 13(1):93–104CrossRefPubMedGoogle Scholar
  11. Fitch WM (1971) Toward defining the course of evolution: minimum change for a specific tree topology. Syst Zool 20:406–416CrossRefGoogle Scholar
  12. Frederico LA, Kunkel TA, Shaw BR (1990) A sensitive genetic assay for the detection of cytosine deamination: determination of rate constants and the activation energy. Biochemistry (Mosc) 29(10):2532–2537CrossRefGoogle Scholar
  13. Hendy MD, Penny D (1982) Branch and bound algorithms to determine minimal evolutionary trees. Math Biosci 60:133–142CrossRefGoogle Scholar
  14. Hendy MD, Penny D (1989) A framework for the quantitative study of evolutionary trees. Syst Zool 38:297–309CrossRefGoogle Scholar
  15. Jukes TH, Cantor CR (1969) Evolution of protein molecules. In: Munro HN (ed) Mammalian protein metabolism. Academic, New York, pp 21–123CrossRefGoogle Scholar
  16. Kass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90(430):773–795CrossRefGoogle Scholar
  17. Kimura M (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol 16:111–120CrossRefGoogle Scholar
  18. Kishino H, Hasegawa M (1989) Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in Hominoidea. J Mol Evol 29:170–179CrossRefPubMedGoogle Scholar
  19. Kreutzer DA, Essigmann JM (1998) Oxidized, deaminated cytosines are a source of C --> T transitions in vivo. Proc Natl Acad Sci U S A 95(7):3578–3582CrossRefPubMedPubMedCentralGoogle Scholar
  20. Nei M (1996) Phylogenetic analysis in molecular evolutionary genetics. Annu Rev Genet 30:371–403CrossRefPubMedGoogle Scholar
  21. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425PubMedGoogle Scholar
  22. Sankoff D (1975) Minimal mutation trees of sequences. J SIAM Appl Math 28:35–42CrossRefGoogle Scholar
  23. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464CrossRefGoogle Scholar
  24. Swofford D (1993) Phylogenetic analysis using parsimony. Illinois Natural History Survey, ChampaignGoogle Scholar
  25. Takezaki N, Nei M (1994) Inconsistency of the maximum parsimony method when the rate of nucleotide substitution is constant. J Mol Evol 39(2):210–218PubMedGoogle Scholar
  26. Tamura K, Nei M (1993) Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol 10:512–526PubMedGoogle Scholar
  27. Wolfe KH, Li WH, Sharp PM (1987) Rates of nucleotide substitution vary greatly among plant mitochondrial, chloroplast and nuclear DNAs. Proc Natl Acad Sci U S A 84:9054–9058CrossRefPubMedPubMedCentralGoogle Scholar
  28. Xia X (2012c) Rapid evolution of animal mitochondria. In: Singh RS, Xu J, Kulathinal RJ (eds) Evolution in the fast lane: rapidly evolving genes and genetic systems. Oxford University Press, Oxford, pp 73–82CrossRefGoogle Scholar
  29. Xia X (2013) DAMBE5: a comprehensive software package for data analysis in molecular biology and evolution. Mol Biol Evol 30:1720–1728PubMedPubMedCentralCrossRefGoogle Scholar
  30. Xia X (2017d) Self-organizing map for characterizing heterogeneous nucleotide and amino acid sequence motifs. Computation 5(4):43CrossRefGoogle Scholar
  31. Xia X, Hafner MS, Sudman PD (1996) On transition bias in mitochondrial genes of pocket gophers. J Mol Evol 43:32–40CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  • Xuhua Xia
    • 1
  1. 1.University of Ottawa CAREG and Biology DepartmentOttawaCanada

Personalised recommendations