Summarizing Multiple Gene Trees Using Cluster Networks

  • Daniel H. Huson
  • Regula Rupp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5251)


The result of a multiple gene tree analysis is usually a number of different tree topologies that are each supported by a significant proportion of the genes. We introduce the concept of a cluster network that can be used to combine such trees into a single rooted network, which can be drawn either as a cladogram or phylogram. In contrast to split networks, which can grow exponentially in the size of the input, cluster networks grow only quadratically. A cluster network is easily computed using a modification of the tree-popping algorithm, which we call network-popping. The approach has been implemented as part of the Dendroscope tree-drawing program and its application is illustrated using data and results from three recent studies on large numbers of gene trees.


Gene Tree Horizontal Gene Transfer Phylogenetic Network Hasse Diagram Cluster Network 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    King, N., Rokas, A., Williams, B.L., Carroll, S.B.: Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature 425(6960), 798–804 (2003)CrossRefGoogle Scholar
  2. 2.
    Bandelt, H.-J., Dress, A.W.M.: A canonical decomposition theory for metrics on a finite set. Advances in Mathematics 92, 47–105 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Bordewich, M., Semple, C.: Computing the minimum number of hybridization events for a consistent evolutionary history. Discrete Appl. Math. 155(8), 914–928 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Doolittle, W.F., Bapteste, E.: Pattern pluralism and the tree of life hypothesis. PNAS 104, 2043–2049 (2007)CrossRefGoogle Scholar
  5. 5.
    Ebersberger, I., Galgoczy, P., Taudien, S., Taenzer, S., Platzer, M., von Haeseler, A.: Mapping Human Genetic Ancestry. Mol. Biol. Evol. 24(10), 2266–2276 (2007)CrossRefGoogle Scholar
  6. 6.
    Felsenstein, J.: Inferring Phylogenies. Sinauer Associates, Inc. (2004)Google Scholar
  7. 7.
    Gascuel, O.: BIONJ: An improved version of the NJ algorithm based on a simple model of sequence data. Mol. Biol. Evol. 14, 685–695 (1997)Google Scholar
  8. 8.
    Gusfield, D.: Algorithms on Strings, Trees and Sequences. Cambridge University Press, Cambridge (1997)zbMATHGoogle Scholar
  9. 9.
    Gusfield, D., Eddhu, S., Langley, C.: Efficient reconstruction of phylogenetic networks with constrained recombination. In: Proceedings of the IEEE Computer Society Conference on Bioinformatics, p. 363 (2003)Google Scholar
  10. 10.
    Holland, B., Huber, K., Moulton, V., Lockhart, P.J.: Using consensus networks to visualize contradictory evidence for species phylogeny. Molecular Biology and Evolution 21, 1459–1461 (2004)CrossRefGoogle Scholar
  11. 11.
    Huson, D.H.: SplitsTree: A program for analyzing and visualizing evolutionary data. Bioinformatics 14(10), 68–73 (1998)CrossRefGoogle Scholar
  12. 12.
    Huson, D.H., Bryant, D.: Application of phylogenetic networks in evolutionary studies. Molecular Biology and Evolution 23, 254–267 (2006), CrossRefGoogle Scholar
  13. 13.
    Huson, D.H., Kloepper, T., Lockhart, P.J., Steel, M.A.: Reconstruction of reticulate networks from gene trees. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 233–249. Springer, Heidelberg (2005)Google Scholar
  14. 14.
    Huson, D.H., Kloepper, T.H.: Computing recombination networks from binary sequences. Bioinformatics 21(suppl. 2), ii159–ii165 (2005)Google Scholar
  15. 15.
    Huson, D.H., Richter, D.C., Rausch, C., Dezulian, T., Franz, M., Rupp, R.: Dendroscope: An interactive viewer for large phylogenetic trees. BMC Bioinformatics 8, 460 (2007),, doi:10.1186/1471-2105-8-460CrossRefGoogle Scholar
  16. 16.
    Huson, D.H., Steel, M.A., Whitfield, J.: Reducing distortion in phylogenetic networks. In: Bücher, P., Moret, B.M.E. (eds.) WABI 2006. LNCS (LNBI), vol. 4175, pp. 150–161. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Leebens-Mack, J., Raubeson, L.A., Cui, L., Kuehl, J.V., Fourcade, M.H., Chumley, T.W., Boore, J.L., Jansen, R.K., de Pamphilis, C.W.: Identifying the Basal Angiosperm Node in Chloroplast Genome Phylogenies: Sampling One’s Way Out of the Felsenstein Zone. Mol. Biol. Evol. 22(10), 1948–1963 (2005)CrossRefGoogle Scholar
  18. 18.
    Meacham, C.A.: Theoretical and computational considerations of the compatibility of qualitative taxonomic characters. In: Felsenstein, J. (ed.) Numerical Taxonomy. NATO ASI Series, vol. G1, Springer, Berlin (1983)Google Scholar
  19. 19.
    Nakhleh, L., Warnow, T., Linder, C.R.: Reconstructing reticulate evolution in species - theory and practice. In: Proceedings of the Eighth International Conference on Research in Computational Molecular Biology (RECOMB), pp. 337–346 (2004)Google Scholar
  20. 20.
    Patterson, N., Richter, D.J., Gnerre, S., Lander, E.S., Reich, D.: Genetic evidence for complex speciation of humans and chimpanzees. Nature 441, 1103–1108 (2006)CrossRefGoogle Scholar
  21. 21.
    Steel, M.A.: Recovering a tree from the leaf colorations it generates under a Markov model. Appl. Math. Lett. 7(2), 19–24 (1994)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Daniel H. Huson
    • 1
  • Regula Rupp
    • 1
  1. 1.Center for Bioinformatics ZBITTübingen UniversityTübingenGermany

Personalised recommendations