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Maximum a posteriori probability assignment (MAP-A): an optimality criterion for phylogenetic trees via weighting and

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  • 1Division of Invertebrate Zoology, American Museum of Natural History, Central Park West at 79th Street, New York, NY, 10024-5192, USA.

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Summary

This study introduces faster methods for phylogenetic tree analysis, replacing time-consuming Markov chain Monte Carlo (MC3) with dynamic programming to identify optimal phylogenetic trees and character states more efficiently.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Bayesian phylogenetic analysis often relies on Metropolis-coupled Markov chain Monte Carlo (MC3) methods, which are computationally intensive.
  • Determining relative posterior probabilities and maximum a posteriori (MAP) trees can be a bottleneck in phylogenetic analyses.

Purpose of the Study:

  • To present novel analytical and numerical methods for calculating tree likelihoods integrated over parameter distributions.
  • To enable efficient identification of the maximum posterior probability assignment (MAP-A) of character states to tree vertices.
  • To offer a faster alternative to MC3 for phylogenetic tree searching and optimization.

Main Methods:

  • Development of analytical and numerical methods for integrated tree likelihood calculation.
  • Application of dynamic programming for identifying the maximum posterior probability assignment (MAP-A) of character states.
  • Utilizing posterior probability as an optimality criterion for tree space searching with standard trajectory techniques.

Main Results:

  • The proposed methods allow for the identification of MAP-A trees by integrating over edge-length and other parameter distributions.
  • Dynamic programming efficiently assigns character states to non-leaf tree vertices.
  • Significant time savings are achieved compared to MC3 methods in identifying heuristically optimal MAP-A trees.

Conclusions:

  • The presented methods offer a substantial computational speedup for Bayesian phylogenetic analyses.
  • This approach facilitates more efficient identification of optimal phylogenetic trees and character state assignments.
  • The methods are applicable to diverse data types, including molecular and anatomical data, aligned or unaligned.