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Exactly computing the parsimony scores on phylogenetic networks using dynamic programming.

Lavanya Kannan1, Ward C Wheeler

  • 1Division of Invertebrate Zoology and Richard Gilder Graduate School , American Museum of Natural History, New York, New York.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|February 25, 2014
PubMed
Summary
This summary is machine-generated.

We present a dynamic programming approach to efficiently score phylogenetic networks using maximum parsimony. This method extends Sankoff's algorithm, enabling more accurate evolutionary framework searches for diverse datasets.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Phylogenetic network scoring is crucial for identifying evolutionary frameworks.
  • Maximum parsimony is a key principle for evaluating evolutionary models.
  • Existing methods for scoring phylogenetic networks are computationally challenging.

Purpose of the Study:

  • To develop an efficient dynamic programming algorithm for scoring phylogenetic networks.
  • To generalize scoring criteria to include substitution costs between states.
  • To provide a method for searching optimal evolutionary frameworks.

Main Methods:

  • Dynamic programming applied to phylogenetic networks.
  • Generalization of maximum parsimony scoring with substitution costs.
  • Algorithm complexity analysis for different network structures.

Main Results:

  • Developed exact algorithms for two NP-hard phylogenetic network scoring problems.
  • Achieved O(nm(p)k(2)) time complexity, extending Sankoff's algorithm.
  • Demonstrated polynomial time complexity for networks with disjoint cycles.

Conclusions:

  • The dynamic programming approach effectively scores phylogenetic networks.
  • The methodology offers an improved way to study evolutionary histories.
  • This work provides guidance for selecting scoring criteria and traversing network space.