Generalized buneman pruning for inferring the most parsimonious multi-state phylogeny

  • Authors:
  • Navodit Misra;Guy Blelloch;R. Ravi;Russell Schwartz

  • Affiliations:
  • Department of Physics, Carnegie Mellon University, Pittsburgh;Computer Science Department, Carnegie Mellon University, Pittsburgh;Tepper School of Business, Carnegie Mellon University, Pittsburgh;Department of Biological Sciences, Carnegie Mellon University, Pittsburgh

  • Venue:
  • RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
  • Year:
  • 2010

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Abstract

Accurate reconstruction of phylogenies remains a key challenge in evolutionary biology Most biologically plausible formulations of the problem are formally NP-hard, with no known efficient solution The standard in practice are fast heuristic methods that are empirically known to work very well in general, but can yield results arbitrarily far from optimal Practical exact methods, which yield exponential worst-case running times but generally much better times in practice, provide an important alternative We report progress in this direction by introducing a provably optimal method for the weighted multi-state maximum parsimony phylogeny problem The method is based on generalizing the notion of the Buneman graph, a construction key to efficient exact methods for binary sequences, so as to apply to sequences with arbitrary finite numbers of states with arbitrary state transition weights We implement an integer linear programming (ILP) method for the multi-state problem using this generalized Buneman graph and demonstrate that the resulting method is able to solve data sets that are intractable by prior exact methods in run times comparable with popular heuristics Our work provides the first method for provably optimal maximum parsimony phylogeny inference that is practical for multi-state data sets of more than a few characters.