Simple Reconstruction of Binary Near-Perfect Phylogenetic Trees

  • Srinath Sridhar
  • Kedar Dhamdhere
  • Guy E. Blelloch
  • Eran Halperin
  • R. Ravi
  • Russell Schwartz
Conference paper

DOI: 10.1007/11758525_107

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3992)
Cite this paper as:
Sridhar S., Dhamdhere K., Blelloch G.E., Halperin E., Ravi R., Schwartz R. (2006) Simple Reconstruction of Binary Near-Perfect Phylogenetic Trees. In: Alexandrov V.N., van Albada G.D., Sloot P.M.A., Dongarra J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3992. Springer, Berlin, Heidelberg

Abstract

We consider the problem of reconstructing near-perfect phylogenetic trees using binary character states (referred to as BNPP). A perfect phylogeny assumes that every character mutates at most once in the evolutionary tree, yielding an algorithm for binary character states that is computationally efficient but not robust to imperfections in real data. A near-perfect phylogeny relaxes the perfect phylogeny assumption by allowing at most a constant number q of additional mutations. In this paper, we develop an algorithm for constructing optimal phylogenies and provide empirical evidence of its performance. The algorithm runs in time O((72 κ)qnm + nm2) where n is the number of taxa, m is the number of characters and κ is the number of characters that share four gametes with some other character. This is fixed parameter tractable when q and κ are constants and significantly improves on the previous asymptotic bounds by reducing the exponent to q. Furthermore, the complexity of the previous work makes it impractical and in fact no known implementation of it exists. We implement our algorithm and demonstrate it on a selection of real data sets, showing that it substantially outperforms its worst-case bounds and yields far superior results to a commonly used heuristic method in at least one case. Our results therefore describe the first practical phylogenetic tree reconstruction algorithm that finds guaranteed optimal solutions while being easily implemented and computationally feasible for data sets of biologically meaningful size and complexity.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Srinath Sridhar
    • 1
  • Kedar Dhamdhere
    • 2
  • Guy E. Blelloch
    • 1
  • Eran Halperin
    • 3
  • R. Ravi
    • 4
  • Russell Schwartz
    • 5
  1. 1.Computer Science DeptCMUUSA
  2. 2.Google IncMountain View
  3. 3.ICSIUniversity of CaliforniaBerkeley
  4. 4.Tepper School of BusinessCMUUSA
  5. 5.Department of Biological SciencesCMUUSA

Personalised recommendations