Deep Coalescence Reconciliation with Unrooted Gene Trees: Linear Time Algorithms

  • Paweł Górecki
  • Oliver Eulenstein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7434)

Abstract

Gene tree reconciliation problems invoke the minimum number of evolutionary events that reconcile gene evolution within the context of a species tree. Here we focus on the deep coalescence (DC) problem, that is, given an unrooted gene tree and a rooted species tree, find a rooting of the gene tree that minimizes the number of DC events, or DC cost, when reconciling the gene tree with the species tree. We describe an O(n) time and space algorithm for the DC problem, where n is the size of the input trees, which improves on the time complexity of the best-known solution by a factor of n. Moreover, we provide an O(n) time and space algorithm that computes the DC scores for each rooting of the given gene tree. We also describe intriguing properties of the DC cost, which can be used to identify credible rootings in gene trees. Finally, we demonstrate the performance of our new algorithms in an empirical study using data from public databases.

Keywords

Gene Tree Linear Time Algorithm Star Topology Double Edge Rooted Binary 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.
    Bansal, M.S., Burleigh, J.G., Eulenstein, O.: Efficient genome-scale phylogenetic analysis under the duplication-loss and deep coalescence cost models. BMC Bioinformatics 11(suppl. 1), S42 (2010)CrossRefGoogle Scholar
  2. 2.
    Bender, M.A., Farach-Colton, M.: The LCA Problem Revisited. In: Gonnet, G.H., Viola, A. (eds.) LATIN 2000. LNCS, vol. 1776, pp. 88–94. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Bininda-Emonds, O.R.P., Gittleman, J.L., Steel, M.A.: The (super) tree of life: procedures, problems, and prospects. Annual Review of Ecology and Systematics 33, 265–289 (2002)CrossRefGoogle Scholar
  4. 4.
    Burleigh, J.G., Bansal, M.S., Eulenstein, O., Hartmann, S., Wehe, A., Vision, T.J.: Genome-scale phylogenetics: inferring the plant tree of life from 18,896 discordant gene trees. Systematic Biology 60, 117–125Google Scholar
  5. 5.
    Chaudhary, R., Bansal, M., Wehe, A., Fernández-Baca, D., Eulenstein, O.: iGTP: A software package for large-scale gene tree parsimony analysis. BMC Bioinformatics 11(1), 574 (2010)CrossRefGoogle Scholar
  6. 6.
    Chen, F., Mackey, A.J., Stoeckert, C.J., Roos, D.S.: Orthomcl-db: querying a comprehensive multi-species collection of ortholog groups. Nucleic Acids Research 34(suppl. 1), D363–D368Google Scholar
  7. 7.
    Davies, J.T., Fritz, S.A., Grenyer, R., Orme, C.D.L., Bielby, J., Bininda-Emonds, O.R.P., Cardillo, M., Jones, K.E., Gittleman, J.L., Mace, G.M., Purvis, A.: Phylogenetic trees and the future of mammalian biodiversity. PNAS 105, 11556–11563 (2008)CrossRefGoogle Scholar
  8. 8.
    Edgar, R.C.: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32, 1792–1797 (2004)CrossRefGoogle Scholar
  9. 9.
    Edwards, E.J., Still, C.J., Donoghue, M.J.: The relevance of phylogeny to studies of global change. Trends In Ecology & Evolution 22(5), 243–249 (2007)CrossRefGoogle Scholar
  10. 10.
    Forest, F., et al.: Preserving the evolutionary potential of floras in biodiversity hotspots. Nature 445(7129), 757–760 (2007)CrossRefGoogle Scholar
  11. 11.
    Górecki, P., Tiuryn, J.: DLS-trees: A model of evolutionary scenarios. Theor. Comput. Sci. 359(1-3), 378–399 (2006)MATHCrossRefGoogle Scholar
  12. 12.
    Górecki, P., Tiuryn, J.: Inferring phylogeny from whole genomes. Bioinformatics 23(2), e116–e122 (2007)Google Scholar
  13. 13.
    Guindon, S., Delsuc, F., Dufayard, J., Gascuel, O.: Estimating maximum likelihood phylogenies with PhyML. Methods Mol. Biol. 537, 113–137 (2009)CrossRefGoogle Scholar
  14. 14.
    Koonin, E.V., Galperin, M.Y.: Sequence - evolution - function: computational approaches in comparative genomics. Kluwer Academic (2003)Google Scholar
  15. 15.
    Maddison, W.P.: Gene trees in species trees. Systematic Biology 46, 523–536 (1997)CrossRefGoogle Scholar
  16. 16.
    Page, R.D.M., Holmes, E.C.: Molecular evolution: a phylogenetic approach. Blackwell Science (1998)Google Scholar
  17. 17.
    Sayers, E.W., et al. Database resources of the national center for biotechnology information. Nucleic Acids Research 37(suppl. 1), D5–D15 (2009)Google Scholar
  18. 18.
    Smith, A.: Rooting molecular trees: problems and strategies. Biol. J. Linn. Soc. 51, 279–292Google Scholar
  19. 19.
    Thuiller, W., Lavergne, S., Roquet, C., Boulangeat, I., Lafourcade, B., Araujo, M.: Consequences of climate change on the tree of life in Europe. Nature 470(7335), 531–534 (2011)CrossRefGoogle Scholar
  20. 20.
    Wheeler, W.: Nucleic acid sequence phylogeny and random outgroups. Cladistics – The International Journal of the Willi Hennig Society 51, 363–368 (1990)CrossRefGoogle Scholar
  21. 21.
    Willis, C.G., Ruhfel, B., Primack, R.B., Miller-Rushing, A.J., Davis, C.C.: Phylogenetic patterns of species loss in thoreau’s woods are driven by climate change. PNAS 105, 17029–17033 (2008)CrossRefGoogle Scholar
  22. 22.
    Yu, Y., Warnow, T., Nakhleh, L.: Algorithms for MDC-Based Multi-locus Phylogeny Inference. In: Bafna, V., Sahinalp, S.C. (eds.) RECOMB 2011. LNCS, vol. 6577, pp. 531–545. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Zhang, L.: From Gene Trees to Species Trees II: Species Tree Inference by Minimizing Deep Coalescence Events. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, 1685–1691 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paweł Górecki
    • 1
  • Oliver Eulenstein
    • 2
  1. 1.Department of Mathematics, Informatics and MechanicsUniversity of WarsawPoland
  2. 2.Department of Computer ScienceIowa State UniversityUSA

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