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Evolutionary tree reconstruction using structural expectation maximization and homotopy.

J Li1, M Guo

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. lijianfu@hit.edu.cn

Genetics and Molecular Research : GMR
|November 7, 2007
PubMed
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A new algorithm, HSEMPHY, improves evolutionary tree reconstruction by using homotopy continuation to overcome local optimality issues found in SEMPHY. This method is robust and performs comparably to existing popular tools.

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Evolutionary Biology

Background:

  • The Structural Expectation Maximization (SEM) algorithm, implemented in SEMPHY, is an efficient method for reconstructing evolutionary trees.
  • However, SEMPHY suffers from the problem of local optimality, which can affect the accuracy of the reconstructed trees.

Purpose of the Study:

  • To address the local optimality issue in SEMPHY and enhance evolutionary tree reconstruction.
  • To introduce a novel algorithm, HSEMPHY, that utilizes the homotopy continuation principle.

Main Methods:

  • HSEMPHY computes conditional probabilities of hidden variables using the maximum entropy principle.
  • The algorithm simulates the homotopy principle with a homotopy parameter (beta) to mitigate the influence of initial values on the final tree resolution.

Related Experiment Videos

Main Results:

  • HSEMPHY was tested on both real and simulated datasets.
  • Performance was compared against SEMPHY, PHYML, and RAXML, demonstrating comparable or superior results.
  • HSEMPHY showed significant robustness, especially when dealing with suboptimal starting trees.

Conclusions:

  • HSEMPHY offers an improved approach to evolutionary tree reconstruction, effectively addressing the local optimality limitations of SEMPHY.
  • The algorithm demonstrates strong performance and robustness, making it a valuable tool for phylogenetic analysis.