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Recursive algorithms for phylogenetic tree counting.

Alexandra Gavryushkina1, David Welch, Alexei J Drummond

  • 1Department of Computer Science, The University of Auckland, Auckland, New Zealand. sasha.gavryushkina@auckland.ac.nz.

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Summary
This summary is machine-generated.

New algorithms efficiently count phylogenetic trees with fossil data or serial sampling. This aids Bayesian inference by enabling accurate tree space characterization and prior specification.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Phylogenetics

Background:

  • Bayesian phylogenetic inference requires understanding tree spaces and counting possible trees.
  • Standard tree counting is simple without time constraints, but complex with fossil data or serial sampling.

Purpose of the Study:

  • To develop efficient algorithms for counting trees in complex phylogenetic spaces.
  • To enable accurate prior specification in Bayesian inference with time-calibrated trees.

Main Methods:

  • Developed a polynomial-time algorithm for counting resolutions of constraint trees.
  • Generalized algorithms for fully ranked constraint trees and introduced a cubic-time algorithm for trees with sampled ancestors.

Main Results:

  • Efficient algorithms are presented for counting various types of phylogenetic trees.
  • The algorithms are polynomial, quadratic, and cubic in the number of sampled individuals.

Conclusions:

  • These algorithms are crucial for Bayesian Markov chain Monte Carlo inference.
  • They are particularly important when incorporating fossil data or serially sampled data.