NeighborNet: An Agglomerative Method for the Construction of Planar Phylogenetic Networks

  • David Bryant
  • Vincent Moulton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2452)

Abstract

We introduce NeighborNet, a network construction and data representation method that combines aspects of the neighbor joining (NJ) and SplitsTree. Like NJ, NeighborNet uses agglomeration: taxa are combined into progressively larger and larger overlapping clusters. Like SplitsTree, NeighborNet constructs networks rather than trees, and so can be used to represent multiple phylogenetic hypotheses simultaneously, or to detect complex evolutionary processes like recombination, lateral transfer and hybridization. NeighborNet tends to produce networks that are substantially more resolved than those made with SplitsTree. The method is efficient (O(n3) time) and is well suited for the preliminary analyses of complex phylogenetic data. We report results of three case studies: one based on mitochondrial gene order data from early branching eukaryotes, another based on nuclear sequence data from New Zealand alpine buttercups (Ranunculi), and a third on poorly corrected synthetic data.

Keywords

Networks Phylogenetics Hybridization SplitsTree Neighbor-Joining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    K. Atteson, The performance of Neighbor-Joining methods of phylogenetic reconstruction, Algorithmica, 25 (1999) 251–278.MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    J.C. Aude, Y. Diaz-Lazcoz Y., J.J. Codani and J.L. Risler, Application of the pyramidal clustering method to biological objects, Comput. Chem. 23, (1999) 303–315.CrossRefGoogle Scholar
  3. 3.
    H.-J. Bandelt, A. Dress, A canonical decomposition theory for metrics on a finite set, Advances in Mathematics, 92 (1992) 47–105.MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    H.-J. Bandelt, P. Forster, B. Sykes, M. Richards, Mitochondrial portraits of human populations, Genetics 141 (1995) 743–753.Google Scholar
  5. 5.
    H.-J. Bandelt, P. Forster, A. Röhl, Median-joining networks for inferring intraspecific phylogenies, Mol. Biol. Evol., 16 (1999) 37–48.Google Scholar
  6. 6.
    A. C. Barbrook, C. J. Howe, N. Blake, P. Robinson, The phylogeny of The Canterbury Tales, Nature, 394 (1998) 839.CrossRefGoogle Scholar
  7. 7.
    J. Barthélemy, A. Guenoche, Trees and Proximity Representations, John Wiley & Sons, Chichester New York Brisbane Toronto Singapore, 1991.MATHGoogle Scholar
  8. 8.
    P. Bertrand, Structural properties of pyramidal clustering, DIMACS, 19 (1995), 35–53.MathSciNetGoogle Scholar
  9. 9.
    D. Bryant. Canonizing neighbor-joining. in preparation.Google Scholar
  10. 10.
    D. Bryant, V. Moulton. The consistency of NeighborNet. in preparation.Google Scholar
  11. 11.
    P. Buneman, The recovery of trees from measures of dissimilarity. In F. Hodson et al., Math. in the Archeological and Historical Sci., (pp.387–395), Edinburgh University Press, 1971.Google Scholar
  12. 12.
    V. Chepoi, B. Fichet, A note on circular decomposable metrics. Geom. Dedicata, 69 (1998) 237–240.MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    G. Christopher, M. Farach, M. Trick, The structure of circular decomposable metrics. Algorithms—ESA’ 96 (Barcelona), 486–500, LNCS 1136, Springer, Berlin, 1996.Google Scholar
  14. 14.
    E. Diday, Une representation des classes empi tantes: les pyramides. Rapport de recherche INRIA 291 (1984).Google Scholar
  15. 15.
    J. Dopazo, A. Dress, A. von Haeseler, Split decomposition: A technique to analyze viral evolution, PNAS, 90 (1993) 10320–10324.CrossRefGoogle Scholar
  16. 16.
    A. Dress, M. Hendy, K. Huber, V. Moulton, On the number of vertices and edges of the Buneman graph, Annals Comb., 1 (1997) 329–337.MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    A. Dress, K. Huber, V. Moulton, An exceptional split geometry, Annals Comb., 4 (2000) 1–11.MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    A. Dress, D. Huson, Computing phylogenetic networks from split systems, Mnscrpt, 1998.Google Scholar
  19. 19.
    P.L. Erdös, M. Steel, L.A. Szkely, and T. Warnow, A few logs suffice to build (almost) all trees (Part 2) Theoretical Computer Science 221, (1999) 77–118.MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    M. Farach, Recognizing circular decomposable metrics, J. Comp. Bio., 4 (1997) 157–162.CrossRefGoogle Scholar
  21. 21.
    F.J.F. Fisher. The alpine ranunculi of New Zealand. DSIR publishing, New Zealand. 1965.Google Scholar
  22. 22.
    O. Gascuel, BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data, Molecular Biology and Evolution, 14(7), (1997) 685–695.Google Scholar
  23. 23.
    O. Gascuel, Concerning the NJ algorithm and its unweighted version, UNJ. In B. Mirkin, F.R. McMorris, F.S. Roberts, A. Rzhetsky, Math. Hierarch. and Biol., AMS, (1997) 149–170.Google Scholar
  24. 24.
    O. Gascuel, Data model and classification by trees: the minimum variance reduction (MVR) method, Journal of Classification, 17 (2000) 67–99.MATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    S. Guindon and O. Gascuel Efficient Biased Estimation of Evolutionary Distances When Substitution Rates Vary Across Sites Mol. Biol. Evol., 19, (2002) 534–543.Google Scholar
  26. 26.
    B. Holland, K. Huber, A. Dress, V. Moulton, Some new techniques in statistical geometry, (in preparation).Google Scholar
  27. 27.
    E. Holmes, M. Worobey, A. Rambaut, Phylogenetic evidence for recombination in dengue virus, Mol. Bio. Evol., 16 (1999) 405–409.Google Scholar
  28. 28.
    D. Huson, SplitsTree: a program for analyzing and visualizing evolutionary data, Bioinformatics, 14 (1998) 68–73.CrossRefGoogle Scholar
  29. 29.
    P. Lockhart, P. McLenachan, D. Havell, D. Glenny, D. Huson, U. Jensen, Phylogeny, dispersal and radiation of New Zealand alpine buttercups: molecular evidence under split decomposition, Ann. Missouri. Bot. Gard., 88 (2001) 458–477.CrossRefGoogle Scholar
  30. 30.
    Maddison, D. R., Swofford, D. L., Maddison, W. P. NEXUS: An extensible file format for systematic information. Systematic Biology 46(4) 1997, 590–621.CrossRefGoogle Scholar
  31. 31.
    V. Makarenkov, T-REX: reconstructing and visualizing phylogenetic trees and reticulation networks, Bioinformatics, 17 (2001) 664–668.CrossRefGoogle Scholar
  32. 32.
    P. Legendre, V. Makarenkov, Reconstruction of biogeographic and evolutionary networks using retiulograms. Syst. Biol. 51 (2) (2002) 199–216.CrossRefGoogle Scholar
  33. 33.
    N. Saitou and M. Nei, The neighbor-joining method: a new method for reconstruction of phylogenetic trees, Mol. Bio. Evol., 4 (1987) 406–425.Google Scholar
  34. 34.
    M. Salemi, M. Leiws, J. Egan, W. Hall, J. Desmyter, A.-M. Vandamme, Different population dynamics of human T cell lymphotropic virus type II in intrevenous drug users compared with endemically infected tribes, PNAS, 96 (1999) 13253–13258.CrossRefGoogle Scholar
  35. 35.
    D. Sanko., D. Bryant, M. Denault, B.F. Lang, and G. Burger, Early eukaryote evolution based on mitochondrial gene order breakpoints. J. of Comp. Biology, 7(3) (2000) 521–536.CrossRefGoogle Scholar
  36. 36.
    S. Sattath, A. Tversky., Additive similarity trees, Psychometrika, 42 (3) 319–345.Google Scholar
  37. 37.
    R.R. Sokal and C.D. Michener. A statistical method for evaluating systematic relationships. Univ. Kansas Science Bull., 38 (1958) 1409–1438.Google Scholar
  38. 38.
    K. Strimmer, C. Wiuf, V. Moulton, Recombination analysis using directed graphical models, Molecular Biology and Evolution, 18 (2001) 97–99.Google Scholar
  39. 39.
    D. Swofford, G. J. Olsen, P. J. Waddell and D. M. Hillis. Phylogenetic Inference, in Molecular Systematics 2nd Edition, Hillis, D.M. and Moritz, C. and Mable, B.K. (eds). Sinauer (1996) 407–514.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • David Bryant
    • 1
  • Vincent Moulton
    • 2
  1. 1.McGill Centre for Bioinformatics3775 University St, MontréalQuébec
  2. 2.Linnaeus Center for BioinformaticsUppsala UniversityUppsalaSweden

Personalised recommendations