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Reconstruction of Evolutionary Trees

  • Kenneth Lange
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Inferring the evolutionary relationships among related taxa (species, genera, families, or higher groupings) is one of the most fascinating problems of molecular genetics [11, 13, 14]. It is now relatively simple to sequence genes and to compare the results from several contemporary taxa. In the current chapter we will assume that the chore of aligning the DNA sequences from these taxa has been successfully accomplished. The taxa are then arranged in an evolutionary tree (or phytogeny) depicting how taxa diverge from common ancestors. A single ancestral taxon roots the binary tree describing the evolution of the contemporary taxa. The reconstruction problem can be briefly stated as finding the rooted evolutionary tree best fitting the current DNA data. Once the best tree is identified, it is also of interest to estimate the branch lengths of the tree. These tell us something about the pace of evolution. For the sake of brevity, we will focus on the problem of finding the best tree.

Keywords

Maximum Parsimony Rooted Tree Equilibrium Distribution Internal Node Evolutionary Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Cavender JA (1989) Mechanized derivation of linear invariants. Mol Biol Evol 6:301–316Google Scholar
  2. [2]
    Eck RV, Dayhoff MO (1966) Atlas of Protein Sequence and Structure. National Biomedical Research Foundation, Silver Spring, MDGoogle Scholar
  3. [3]
    Felsenstein J (1978) Cases in which parsimony or compatibility methods will be positively misleading. Syst Zool 27:401–410CrossRefGoogle Scholar
  4. [4]
    Felsenstein J (1981) Evolutionary trees from DNA sequences: A maximum likelihood approach. J Mol Evol 17:368–376CrossRefGoogle Scholar
  5. [5]
    Fitch WM (1971) Toward defining the course of evolution: Minimum change for a specific tree topology. Syst Zool 20:406–416CrossRefGoogle Scholar
  6. [6]
    Hartigan JA (1973) Minimum mutation fits to a given tree. Biometrics 29:53–65CrossRefGoogle Scholar
  7. [7]
    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
  8. [8]
    Lake JA (1987) A rate-independent technique for analysis of nucleic acid sequences: evolutionary parsimony. Mol Biol Evol 4:167–191Google Scholar
  9. [9]
    Lake JA (1988) Origin of the eukaryotic nucleus determined by rate-invariant analysis of rRNA sequences. Nature 331:184–186CrossRefGoogle Scholar
  10. [10]
    Lamperti J (1977) Stochastic Processes. A Survey of the Mathematical Theory. Springer-Verlag, New YorkzbMATHGoogle Scholar
  11. [11]
    Li W-H, Graur D (1991) Fundamentals of Molecular Evolution. Sinauer, Sunderland, MAGoogle Scholar
  12. [12]
    Solberg J J (1975) A graph theoretic formula for the steady state distribution of a finite Markov process. Management Sci 21:1040–1048zbMATHCrossRefMathSciNetGoogle Scholar
  13. [13]
    Waterman MS (1995) Introduction to Computational Biology: Maps, Sequences, and Genomes. Chapman and Hall, LondonzbMATHGoogle Scholar
  14. [14]
    Weir BS (1990) Genetic Data Analysis. Sinauer, Sunderland, MAGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Kenneth Lange
    • 1
  1. 1.Department of Biostatistics and MathematicsUniversity of MichiganAnn ArborUSA

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