Inference for phylogenies under a hybrid parsimony method: evolutionary-symmetric transversion parsimony

Area of Science:

• Phylogenetics
• Computational Biology
• Evolutionary Biology

Background:

• Existing methods for inferring evolutionary tree topology have limitations in statistical consistency or power.
• Maximum parsimony and transversion parsimony methods lack statistical consistency with large sequence lengths.
• Evolutionary parsimony offers consistency but may lack power for short sequences.

Purpose of the Study:

• To develop a new hybrid method, evolutionary-symmetric transversion parsimony, for improved evolutionary tree inference.
• To combine the statistical consistency of evolutionary parsimony with the power of transversion parsimony.
• To assess the performance and robustness of the proposed method across various parameter choices.

Main Methods:

• Proposed evolutionary-symmetric transversion parsimony, a hybrid statistical method.
• Incorporated a modified transversion parsimony approach within a statistical model.
• Evaluated method properties using a large simulation study with varying gamma parameter values.

Main Results:

• Evolutionary-symmetric transversion parsimony demonstrates greater discriminating power than evolutionary parsimony.
• The new method shows better calibration than symmetric transversion parsimony.
• Results are robust to the choice of the gamma parameter, with 0.90 being a reasonable default.

Conclusions:

• Evolutionary-symmetric transversion parsimony offers a computationally straightforward and effective approach for inferring evolutionary tree topology.
• The method enhances both statistical consistency and inferential power.
• This hybrid strategy provides a robust alternative to existing phylogenetic inference techniques.