Phylogenetic Inference and Parsimony Analysis

  • Llewellyn D. DensmoreIII
Part of the Methods in Molecular Biology™ book series (MIMB, volume 176)


Application of phylogenetic inference methods to comparative endocrinology studies has provided researchers with a new set of tools to aid in understanding the evolution and distribution of gene families. Phylogeny, as defined by Hillis et al. (1), is the “historical relationships among lineages of organisms or their parts (e.g., genes).” Inferring phylogeny is a way of generating a best estimate of the evolutionary history of organisms (or gene families), based on the information (often incomplete, as in a gene sequence) that is available. The use of phylogenetic analyses, specifically those methods that are based on maximum parsimony, has changed the way in which characters and character states are determined and interpreted. Maximum parsimony (often simply called “parsimony”) seeks to estimate a parameter based on the minimum number of events required to explain the data. In this type of phylogenetic analysis, the best or optimal tree (generally portrayed as either a cladogram or phylogram, see Note1) is that topology which requires the fewest number of character-state changes (see below). That tree is arrived at based upon consideration of shared, derived characters. This method assumes that when two taxa (or genes) share a homologous derived character state, they do so because a common ancestor of both had that character state. One goal of phylogenetic analysis that is always implied (and often stated) is to avoid using characters that are homoplastic. Characters that have homoplasy have similarities in character states for reasons other than inheritance from a common ancestor, including convergent and parallel evolution or a reversal of state (e.g., A → G → A).


Character State Maximum Parsimony Phylogenetic Inference Pairwise Alignment Stepwise Addition 
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.


  1. 1.
    Hillis, D. M., Moritz, C., and Mable, B. K. (eds.) (1996) Molecular Systematics, Sinauer, Sunderland,MA.Google Scholar
  2. 2.
    Felsenstein, J. (1993) PHYLIP (Phylogeny Inference Package), version 3.57, Department of Genetics, University of Seattle.Google Scholar
  3. 3.
    Kishino, H., Miyata, T., and Hasegawa, M. (1990) Maximum likelihood inference of protein phylogeny and the origin of chloroplasts. J. Mol. Evol. 31, 151–160.CrossRefGoogle Scholar
  4. 4.
    Adachi, J. and Hasegawa, M. (1992) MOLPHY: programs for molecular phylogenetics I-PROTML: Maximum likelihood inference for protein phylogeny. Computer Science Monographs, No. 27, Institute of Statistical Mathematics, Tokyo.Google Scholar
  5. 5.
    Swofford, D. L., Olsen, G. J., Waddell, P. J., and Hillis, D. M. (1996) Phlylogenetic inference, in Molecular Systematics (Hillis, D. M., Moritz, C., and Mable, B. K., eds.),Sinauer, Sunderland, MA, pp. 407–514.Google Scholar
  6. 6.
    Saitou, N. and Nei, M. (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425.PubMedGoogle Scholar
  7. 7.
    Altschul, S., Gish, W., Miller, W., Myers, E. W., and Lipman, J. (1990) Basic local alignment tool. J. Mol. Biol. 215, 403–410.PubMedGoogle Scholar
  8. 8.
    Pearson, W. R. and Lipman, J. (1988) Improved tools for biological sequence comparison. Proc. Natl. Acad. Biol. USA 85, 2444–2448.CrossRefGoogle Scholar
  9. 9.
    Needleman, S. B. and Wunsch, C. D. (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443–453.PubMedCrossRefGoogle Scholar
  10. 10.
    Feng, D.-F. and Doolittle, R. F. (1987) Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J. Mol. Evol. 25, 351–360.PubMedCrossRefGoogle Scholar
  11. 11.
    Higgins, D. G., Bleasby, A. J., and Fuchs, R. (1992) CLUSTAL V: improved software for multiple sequence alignment. Comput. Appl. Biosci. 8, 189–191.PubMedGoogle Scholar
  12. 12.
    Hein, J. (1989) A new method that simultaneously aligns and reconstructs ancestral sequences for any number of homologous sequences, when phylogeny is given. Mol. Biol. Evol. 6, 649–448.PubMedGoogle Scholar
  13. 13.
    Wheeler, W. and Gladstein, D. (1994) MALIGN: a multiple sequence alignment program. J. Hered. 85, 417.Google Scholar
  14. 14.
    Fitch, W. M. (1971) Toward defining the course of evolution: minimal change for a specific tree topology. Syst. Zool. 20, 406–416.CrossRefGoogle Scholar
  15. 15.
    Camin, J. H. and Sokal, R. R. (1965) A method for deducing branching sequences in phylogeny. Evolution 19, 311–326.CrossRefGoogle Scholar
  16. 16.
    Eck, R. V. and Dayhoff, M. O. (eds.) (1966) Atlas of Protein Sequence and Structure. National Biomedical Research Foundation, Silver Springs, MD.Google Scholar
  17. 17.
    Goodman, M. (1981) Decoding the pattern of protein evolution. Progr. Biophys. Mol. Biol. 37, 105–164.CrossRefGoogle Scholar
  18. 18.
    Hendy, M. D. and Penny, D. (1982) Branch and bound algorithms to determine minimum evolutionary trees. Discrete Math. 96, 51–58.CrossRefGoogle Scholar
  19. 19.
    Swofford, D. L. (1999) PAUP*: Phylogenetic Analysis Using Parsimony, version 4.0b.2.Sinauer, Sunderland, MA.Google Scholar
  20. 20.
    Felsenstein, J. (1978) The number of evolutionary trees. Syst. Zool. 27, 27–33.CrossRefGoogle Scholar
  21. 21.
    Hillis, D. M. and Hulsenbeck, J. P. (1992) Signal, noise and reliability in molecular phylogenetic analysis. J. Hered. 83, 189–195.PubMedGoogle Scholar
  22. 22.
    Hillis, D. M., Moritz, C., and Mable, B. K. (1996) Applications of molecular systematics, in Molecular Systematics (Hillis, D. M., Moritz, C., and Mable, B. K., eds.), Sinauer, Sunderland, MA, pp. 515–544.Google Scholar
  23. 23.
    Efron, B. and Tibshirani, R. J. (1993) An Introduction to the Bootstrap.Chapman and Hall, New York.Google Scholar
  24. 24.
    Xia, Z., Gale, W. L., Chang, X, Langenau, D., Patino, R., Maule, A. G., and Densmore, L. D. (2000) Phylogenetic sequence analysis, recombinant expression and tissue distribution of a channel catfish estrogen B receptor. Gen. Comp. Endocrinol. 118, 139–149.PubMedCrossRefGoogle Scholar
  25. 25.
    Bremer, K. (1994) Branch support and tree stability. Cladistics 10, 295–304.CrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2001

Authors and Affiliations

  • Llewellyn D. DensmoreIII
    • 1
  1. 1.Department of Biological SciencesTexas Tech UniversityLubbockTX

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