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PrioriTree: a utility for improving phylodynamic analyses in BEAST.

Jiansi Gao1, Michael R May1,2, Bruce Rannala1

  • 1Department of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.

Bioinformatics (Oxford, England)
|January 2, 2023
PubMed
Summary

PrioriTree is a new R package that helps researchers assess how prior beliefs affect phylodynamic analyses of disease spread. It aids in robustly inferring geographic history and model fit for outbreak investigations.

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Bioinformatics

Background:

  • Phylodynamic methods are crucial for understanding disease outbreak origins and spread.
  • Discrete-geographic phylodynamic models require careful parameter inference, making them sensitive to prior assumptions.

Purpose of the Study:

  • To introduce PrioriTree, an interactive R package designed to identify and manage prior sensitivity in discrete-geographic phylodynamic analyses.
  • To facilitate robust Bayesian inference and model selection for disease evolution studies.

Main Methods:

  • PrioriTree generates input files for BEAST analyses.
  • It summarizes output from BEAST, enabling data-cloning and model fit assessments.
  • The package supports the evaluation of candidate discrete-geographic models against empirical data.

Main Results:

  • PrioriTree provides a user-friendly interface for complex phylodynamic analyses.
  • It enhances the reliability of inferences regarding geographic history and demographic factors in disease outbreaks.
  • The tool aids in selecting appropriate prior models for phylodynamic inference.

Conclusions:

  • PrioriTree empowers researchers to perform more rigorous phylodynamic analyses by addressing prior sensitivity.
  • This utility improves the accuracy and interpretability of geographic and demographic history reconstructions for infectious diseases.