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CherryML: scalable maximum likelihood estimation of phylogenetic models.

Sebastian Prillo1, Yun Deng2, Pierre Boyeau1

  • 1Computer Science Division, University of California, Berkeley, CA, USA.

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CherryML significantly accelerates phylogenetic model parameter estimation using a novel composite likelihood approach. This computational speedup allows for more complex evolutionary models in molecular evolution research.

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Area of Science:

  • Computational Biology
  • Molecular Evolution
  • Phylogenetics

Background:

  • Phylogenetic models are crucial for understanding molecular evolution across various timescales.
  • Maximum likelihood estimation is a standard but computationally intensive method for parameter estimation in these models.

Purpose of the Study:

  • To develop a computationally efficient method for estimating parameters in phylogenetic models.
  • To enable the use of more complex and biologically realistic models.

Main Methods:

  • Introduction of CherryML, a method utilizing a quantized composite likelihood over tree cherries.
  • Demonstration of CherryML's application to estimate a large rate matrix for protein residue-residue coevolution.

Main Results:

  • CherryML achieves several orders of magnitude speedup compared to existing methods.
  • Estimating a 400x400 rate matrix for protein coevolution using CherryML is vastly faster than traditional algorithms like expectation-maximization.

Conclusions:

  • CherryML offers a broadly applicable and highly efficient solution for phylogenetic model parameter estimation.
  • The method's speedup facilitates the exploration of more sophisticated evolutionary models in biological research.