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Reconstructing phylogeny by quadratically approximated maximum likelihood.

M D Woodhams1, M D Hendy

  • 1Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand. m.d.woodhams@massey.ac.nz

Bioinformatics (Oxford, England)
|July 21, 2004
PubMed
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Maximum likelihood (ML) phylogenetic inference faces computational limits with large datasets. This study introduces a quadratic approximation (QAML) for faster, accurate tree searching in phylogenetic analysis.

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Maximum likelihood (ML) is a standard for phylogenetic inference from sequence data.
  • ML methods face computational challenges, especially with a large number of taxa, necessitating heuristic search restrictions.

Purpose of the Study:

  • To develop a computationally efficient approximation to the likelihood function for phylogenetic inference.
  • To enable more feasible global tree searches in phylogenetics.

Main Methods:

  • Derivation of a quadratic approximation (QAML) to the likelihood function.
  • Utilizing Hadamard conjugation for the approximation.
  • Limitation to simple symmetric models (Kimura, Jukes-Cantor).

Main Results:

Related Experiment Videos

  • The maximum of the QAML approximation can be easily determined for a given tree.
  • Preliminary tests show QAML achieves accuracy comparable to traditional ML.

Conclusions:

  • QAML offers a computationally tractable alternative for phylogenetic inference.
  • This approximation facilitates more efficient exploration of phylogenetic trees, particularly for large datasets.