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GTP Supertrees from Unrooted Gene Trees: Linear Time Algorithms for NNI Based Local Searches

  • Paweł Górecki
  • J. Gordon Burleigh
  • Oliver Eulenstein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7292)

Abstract

Gene tree parsimony (GTP) problems infer species supertrees from a collection of rooted gene trees that are confounded by evolutionary events like gene duplication, gene duplication and loss, and deep coalescence. These problems are NP-complete, and consequently, they often are addressed by effective local search heuristics that perform a stepwise search of the tree space, where each step is guided by an exact solution to an instance of a local search problem. Still, GTP problems require rooted input gene trees; however, in practice, most phylogenetic methods infer unrooted gene trees and it may be difficult to root correctly. In this work, we (i) define the first local NNI search problems to solve heuristically the GTP equivalents for unrooted input gene trees, called unrooted GTP problems, and (ii) describe linear time algorithms for these local search problems. We implemented the first NNI based local search heuristics for unrooted GTP problems, which enable analyses for thousands of genes. Further, analysis of a large plant data set using the unrooted NNI search provides support for an intriguing new hypothesis regarding the evolutionary relationships among major groups of flowering plants.

Keywords

Species Tree Local Search Gene Tree Local Search Algorithm Optimal Edge 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paweł Górecki
    • 1
  • J. Gordon Burleigh
    • 2
  • Oliver Eulenstein
    • 3
  1. 1.Institute of InformaticsUniversity of WarsawPoland
  2. 2.Department of BiologyUniversity of FloridaUSA
  3. 3.Dept. of Computer ScienceIowa State UniversityUSA

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