4.6 Article

Phycas: Software for Bayesian Phylogenetic Analysis

Journal

SYSTEMATIC BIOLOGY
Volume 64, Issue 3, Pages 525-531

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/sysbio/syu132

Keywords

Bayes Factor; Bayesian phylogenetics; conditional predictive ordinates; data partitioning; marginal likelihood; posterior predictive model selection; steppingstone method

Funding

  1. National Science Foundation [EF-0331495, DEB-1036448, DEB-1354146, DEB-0732920, EF-0423641]
  2. Alfred P. Sloan Foundation [98-4-5 ME]
  3. National Evolutionary Synthesis Center (NESCent)
  4. Department of Ecology and Evolutionary Biology at the University of Kansas
  5. Department of Ecology and Evolutionary Biology at the University of Connecticut
  6. Bioinformatics Facility of the Biotechnology/Bioservices Center at the University of Connecticut
  7. Division Of Environmental Biology
  8. Direct For Biological Sciences [1036448] Funding Source: National Science Foundation

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Phycas is open source, freely available Bayesian phylogenetics software written primarily in C++ but with a Python interface. Phycas specializes in Bayesian model selection for nucleotide sequence data, particularly the estimation of marginal likelihoods, central to computing Bayes Factors. Marginal likelihoods can be estimated using newer methods (Thermodynamic Integration and Generalized Steppingstone) that are more accurate than the widely used Harmonic Mean estimator. In addition, Phycas supports two posterior predictive approaches to model selection: Gelfand-Ghosh and Conditional Predictive Ordinates. The General Time Reversible family of substitution models, as well as a codon model, are available, and data can be partitioned with all parameters unlinked except tree topology and edge lengths. Phycas provides for analyses in which the prior on tree topologies allows polytomous trees as well as fully resolved trees, and provides for several choices for edge length priors, including a hierarchical model as well as the recently described compound Dirichlet prior, which helps avoid overly informative induced priors on tree length.

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