Online Consensus and Agreement of Phylogenetic Trees

  • Tanya Y. Berger-Wolf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3240)


Computational heuristics are the primary methods for reconstruction of phylogenetic trees on large datasets. Most large-scale phylogenetic analyses produce numerous trees that are equivalent for some optimization criteria. Even using the best heuristics, it takes significant amount of time to obtain optimal trees in simulation experiments. When biological data are used, the score of the optimal tree is not known. As a result, the heuristics are either run for a fixed (long) period of time, or until some measure of a lack of improvement is achieved. It is unclear, though, what is a good criterion for measuring this lack of improvement. However, often it is useful to represent the collection of best trees so far in a compact way to allow scientists to monitor the reconstruction progress. Consensus and agreement trees are common such representations. Using existing static algorithms to produce these trees increases an already lengthy computational time substantially. In this paper we present efficient online algorithms for computing strict and majority consensi and the maximum agreement subtree.


Binary Tree Consensus Tree Online Algorithm Mast Problem Depth First Search 
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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© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tanya Y. Berger-Wolf
    • 1
  1. 1.Department of Computer ScienceUniversity of New MexicoAlbuquerqueUSA

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