Reducing Distortion in Phylogenetic Networks

  • Daniel H. Huson
  • Mike A. Steel
  • Jim Whitfield
Conference paper

DOI: 10.1007/11851561_14

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4175)
Cite this paper as:
Huson D.H., Steel M.A., Whitfield J. (2006) Reducing Distortion in Phylogenetic Networks. In: Bücher P., Moret B.M.E. (eds) Algorithms in Bioinformatics. WABI 2006. Lecture Notes in Computer Science, vol 4175. Springer, Berlin, Heidelberg


When multiple genes are used in a phylogenetic study, the result is often a collection of incompatible trees. Phylogenetic networks and super-networks can be employed to analyze and visualize the incompatible signals in such a data set. In many situations, it is important to have control over the amount of imcompatibility that is represented in a phylogenetic network, for example reducing noise by removing splits that do not recur among the source trees. Current algorithms for computing hybridization networks from trees are based on a combinatorial analysis of the arising set of splits, and are thus sensitive to false positive splits. Here, a filter is desirable that can identify and remove splits that are not compatible with a hybridization scenario. To address these issues, the concept of the distortion of a tree relative to a split is defined as a measure of how much the tree needs to be modified in order to accommodate the split, and some of its properties are investigated. We demonstrate the usefulness of the approach by recovering a plausible hybridization scenario for buttercups from a pair of gene trees that cannot be obtained by existing methods. In a second example, a set of seven gene trees from microgastrine braconid wasps is investigated using filtered networks. A user-friendly implementation of the method is provided as a plug-in for the program SplitsTree4.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniel H. Huson
    • 1
  • Mike A. Steel
    • 2
  • Jim Whitfield
    • 3
  1. 1.Center for Bioinformatics (ZBIT)Tübingen UniversityGermany
  2. 2.Allan Wilson CentreUniversity of CanterburyChristchurchNew Zealand
  3. 3.Department of EntomologyUniversity of Illinois at Urbana-ChampaignUSA

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