Polynomial Supertree Methods Revisited

  • Malte Brinkmeyer
  • Thasso Griebel
  • Sebastian Böcker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6282)

Abstract

Supertree methods allow to reconstruct large phylogenetic trees by combining smaller trees with overlapping leaf sets, into one, more comprehensive supertree. The most commonly used supertree method, matrix representation with parsimony (MRP), produces accurate supertrees but is rather slow due to the underlying hard optimization problem. In this paper, we present an extensive simulation study comparing the performance of MRP and the polynomial supertree methods MinCut Supertree, Modified MinCut Supertree, Build-with-distances, PhySIC, and PhySIC_IST. We consider both quality and resolution of the reconstructed supertrees. Our findings illustrate the trade-off between accuracy and running time in supertree construction, as well as the pros and cons of voting- and veto-based supertree approaches.

Keywords

Model Tree Input Tree Source Tree Deletion Frequency Supertree Method 
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.

References

  1. 1.
    Gordon, A.D.: Consensus supertrees: The synthesis of rooted trees containing overlapping sets of labelled leaves. J. Classif. 3, 335–348 (1986)CrossRefGoogle Scholar
  2. 2.
    Bininda-Emonds, O.R.P. (ed.): Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life. Computational Biology Book Series, vol. 4. Kluwer Academic, Dordrecht (2004)Google Scholar
  3. 3.
    Bininda-Emonds, O.R.P.: Supertree construction in the genomic age. Methods Enzymol. 395, 745–757 (2005)CrossRefPubMedGoogle Scholar
  4. 4.
    Roch, S.: A short proof that phylogenetic tree reconstruction by maximum likelihood is hard. IEEE/ACM Trans. Comput. Biol. Bioinform. 3(1), 92–94 (2006)CrossRefPubMedGoogle Scholar
  5. 5.
    Foulds, L.R., Graham, R.L.: The Steiner problem in phylogeny is NP-complete. Adv. Appl. Math. 3, 43–49 (1982)CrossRefGoogle Scholar
  6. 6.
    Baum, B.R.: Combining trees as a way of combining data sets for phylogenetic inference, and the desirability of combining gene trees. Taxon 41(1), 3–10 (1992)CrossRefGoogle Scholar
  7. 7.
    Ragan, M.A.: Matrix representation in reconstructing phylogenetic relationships among the eukaryotes. Biosystems 28(1-3), 47–55 (1992)CrossRefPubMedGoogle Scholar
  8. 8.
    Chen, D., Eulenstein, O., Fernández-Baca, D., Sanderson, M.: Minimum-flip supertrees: complexity and algorithms. IEEE/ACM Trans. Comput. Biol. Bioinform. 3(2), 165–173 (2006)CrossRefPubMedGoogle Scholar
  9. 9.
    Ross, H.A., Rodrigo, A.G.: An assessment of matrix representation with compatibility in supertree construction. In: Bininda-Emonds, O.R.P. (ed.) Phylogenetic Supertrees (combining information to reveal the tree of life), vol. 3, pp. 35–63. Kluwer Academic Publishers, Dordrecht (2004)CrossRefGoogle Scholar
  10. 10.
    Semple, C., Steel, M.: A supertree method for rooted trees. Discrete Appl. Math. 105(1-3), 147–158 (2000)CrossRefGoogle Scholar
  11. 11.
    Page, R.D.M.: Modified mincut supertrees. In: Guigó, R., Gusfield, D. (eds.) WABI 2002. LNCS, vol. 2452, pp. 537–552. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Willson, S.J.: Constructing rooted supertrees using distances. Bull. Math. Biol. 66(6), 1755–1783 (2004)CrossRefPubMedGoogle Scholar
  13. 13.
    Ranwez, V., Berry, V., Criscuolo, A., Fabre, P.-H., Guillemot, S., Scornavacca, C., Douzery, E.J.P.: PhySIC: a veto supertree method with desirable properties. Syst. Biol. 56(5), 798–817 (2007)CrossRefPubMedGoogle Scholar
  14. 14.
    Scornavacca, C., Berry, V., Lefort, V., Douzery, E.J.P., Ranwez, V.: PhySIC_IST: cleaning source trees to infer more informative supertrees. BMC Bioinformatics 9, 413 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Bininda-Emonds, O.R.P., Sanderson, M.J.: Assessment of the accuracy of matrix representation with parsimony analysis supertree construction. Syst. Biol. 50(4), 565–579 (2001)CrossRefPubMedGoogle Scholar
  16. 16.
    Levasseur, C., Lapointe, F.-J.: Total evidence, average consensus and matrix representation with parsimony: What a difference distances make. Evol. Bioinform. 2, 249–253 (2006)Google Scholar
  17. 17.
    Aho, A.V., Sagiv, Y., Szymanski, T.G., Ullman, J.D.: Inferring a tree from lowest common ancestors with an application to the optimization of relational expressions. SIAM J. Comput. 10(3), 405–421 (1981)CrossRefGoogle Scholar
  18. 18.
    Thorley, J.L., Wilkinson, M.: A view of supertree methods. In: Jannowitz, M.F., Lapointe, F.J., McMorris, F.R., Roberts, F.S. (eds.) Bioconsensus, vol. 61. The American Mathematical Society, Providence (2003)CrossRefGoogle Scholar
  19. 19.
    Goloboff, P.A., Pol, D.: Semi-strict supertrees. Cladistics 18(5), 514–525 (2002)CrossRefGoogle Scholar
  20. 20.
    Sanderson, M.J.: r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19(2), 301–302 (2003)CrossRefPubMedGoogle Scholar
  21. 21.
    Rambaut, A., Grassly, N.C.: Seq-Gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Comput. Appl. Biosci. 13(3), 235–238 (1997)PubMedGoogle Scholar
  22. 22.
    Yang, Z.: Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J. Mol. Evol. 39(3), 306–314 (1994)CrossRefPubMedGoogle Scholar
  23. 23.
    Higdon, J.W., Bininda-Emonds, O.P., Beck, R.M.D., Ferguson, S.H.: Phylogeny and divergence of the pinnipeds (carnivora: Mammalia) assessed using a multigene dataset. BMC Evol. Biol. 7, 216 (2007)CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Stamatakis, A.: RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22(21), 2688–2690 (2006)CrossRefPubMedGoogle Scholar
  25. 25.
    Swafford, D.: Paup*: Phylogenetic analysis using parsimony (*and other methods), Version 4 (2002)Google Scholar
  26. 26.
    Robinson, D.F., Foulds, L.R.: Comparison of phylogenetic trees. Math. Biosci. 53(1-2), 131–147 (1981)CrossRefGoogle Scholar
  27. 27.
    Gordon, A.D.: On the assessment and comparison of classifications. In: Tomassine, R. (ed.) Analyse de Données et Informatique, Le Chesnay, INRIA, France, pp. 149–160 (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Malte Brinkmeyer
    • 1
  • Thasso Griebel
    • 1
  • Sebastian Böcker
    • 1
  1. 1.Department of Computer ScienceFriedrich Schiller UniversityJenaGermany

Personalised recommendations