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Ancestral Reconstruction by Asymmetric Wagner Parsimony over Continuous Characters and Squared Parsimony over Distributions

  • Miklós Csűrös
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5267)

Abstract

Contemporary inferences about evolution occasionally involve analyzing infinitely large feature spaces, requiring specific algorithmic techniques. We consider parsimony analysis over numerical characters, where knowing the feature values at terminal taxa allows one to infer ancestral features, namely, by minimizing the total number of changes on the edges using continuous-valued distance measures. In particular, we show that ancestral reconstruction is possible in linear time for both an asymmetric linear distance measure (Wagner parsimony) over continuous-valued characters, and a quadratic distance measure over finite distributions. The former can be used to analyze gene content evolution with asymmetric gain and loss penalties, and the latter to reconstruct ancestral diversity of regulatory sequence motifs and multi-allele loci. As an example of employing asymmetric Wagner parsimony, we examine gene content evolution within Archaea.

Keywords

Piecewise Linear Function Steiner Tree Problem Ancestral Reconstruction Parsimony Score Leaf Label 
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.
    Felsenstein, J.: Inferring Phylogenies. Sinauer Associates, Sunderland (2004)Google Scholar
  2. 2.
    Farris, J.S.: Methods for computing Wagner trees. Syst. Zool. 19, 83–92 (1970)CrossRefGoogle Scholar
  3. 3.
    Swofford, D.L., Maddison, W.P.: Reconstructing ancestral states using Wagner parsimony. Math. Biosci. 87, 199–229 (1987)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Maddison, W.P.: Squared-change parsimony reconstructions of ancestral states for continuous-valued characters on a phylogenetic tree. Syst. Zool. 40, 304–314 (1991)CrossRefGoogle Scholar
  5. 5.
    Sankoff, D., Rousseau, P.: Locating the vertices of a Steiner tree in arbitrary metric space. Math. Program. 9, 240–246 (1975)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Hwang, F.K., Richards, D.S.: Steiner tree problems. Networks 22, 55–89 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Cunningham, C.W., Omland, K.E., Oakley, T.H.: Reconstructing ancestral character states: a critical reappraisal. Trends Ecol. Evol. 13, 361–366 (1998)CrossRefGoogle Scholar
  8. 8.
    Pagel, M.: Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999)CrossRefGoogle Scholar
  9. 9.
    Witmer, P.D., Doheny, K.F., Adams, M.K., Boehm, C.D., Dizon, J.S., Goldstein, J.L., Templeton, T.M., Wheaton, A.M., Dong, P.N., Pugh, E.W., Nussbaum, R.L., Hunter, K., Kelmenson, J.A., Rowe, L.B., Brownstein, M.J.: The development of a highly informative mouse simple sequence length polymorphism (SSLP) marker set and construction of a mouse family tree using parsimony analysis. Genome Res. 13, 485–491 (2003)CrossRefGoogle Scholar
  10. 10.
    Caetano-Anollés, G.: Evolution of genome size in the grasses. Crop. Sci. 45, 1809–1816 (2005)CrossRefGoogle Scholar
  11. 11.
    Omland, K.E.: Examining two standard assumptions of ancestral reconstructions: repeated loss of dichromatism in dabbling ducks (Anatini). Evolution 51, 1636–1646 (1997)CrossRefGoogle Scholar
  12. 12.
    Koonin, E.V.: Comparative genomics, minimal gene sets and the last universal common ancestor. Nat. Rev. Microbiol. 1, 127–136 (2003)CrossRefGoogle Scholar
  13. 13.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  14. 14.
    Rogers, J.S.: Deriving phylogenetic trees from allele frequencies. Syst. Zool., 52–63 (1984)Google Scholar
  15. 15.
    Schwartz, S., Silva, J., Burstein, D., Pupko, T., Eyras, E., Ast, G.: Large-scale comparative analysis of splicing signals and their corresponding splicing factors in eukaryotes. Genome Res. 18, 88–103 (2008)CrossRefGoogle Scholar
  16. 16.
    Irimia, M., Penny, D., Roy, S.W.: Coevolution of genomic intron number and splice sites. Trends Genet. 23, 321–325 (2007)CrossRefGoogle Scholar
  17. 17.
    Csűrös, M., Miklós, I.: A probabilistic model for gene content evolution with duplication, loss, and horizontal transfer. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2006. LNCS (LNBI), vol. 3909, pp. 206–220. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Iwasaki, W., Takagi, T.: Reconstruction of highly heterogeneous gene-content evolution across the three domains of life. Bioinformatics 23, i230–i239 (2007)CrossRefGoogle Scholar
  19. 19.
    Makarova, K.S., Sorokin, A.V., Novichkov, P.S., Wolf, Y.I., Koonin, E.V.: Clusters of orthologous genes for 41 archaeal genomes and implications for evolutionary genomics of archaea. Biology Direct 2, 33 (2007)CrossRefGoogle Scholar
  20. 20.
    Mirkin, B.G., Fenner, T.I., Galperin, M.Y., Koonin, E.V.: Algorithms for computing evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes. BMC Evol. Biol. 3, 2 (2003)CrossRefGoogle Scholar
  21. 21.
    Fukui, T., Atomi, H., Kanai, T., Matsumi, R., Fujiwara, S., Imanaka, T.: Complete genome sequence of the hyperthermophilic archaeon Thermococcus kodakaraensis KOD1 and comparison with Pyrococcus genomes. Genome Res. 15, 352–363 (2005)CrossRefGoogle Scholar
  22. 22.
    Brochier, C., Forterre, P., Gribaldo, S.: An emerging phylogenetic core of Archaea: phylogenies of transcription and translation machineries converge following addition of new genome sequences. BMC Evol. Biol. 5, 36 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Miklós Csűrös
    • 1
  1. 1.Department of Computer Science and Operations ResearchUniversity of MontréalMontréalCanada

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