Branch-length parameters are a central component of phylogenetic models and of intrinsic biological interest. Default branch-length priors in some Bayesian phylogenetic software can be unintentionally informative and lead to branch- and tree-length estimates that are unreasonable. Alternatively, priors may be uninformative, but lead to diffuse posterior estimates. Despite the widespread availability of relevant datasets from other groups, biologists rarely leverage outside information to specify branch-length priors that are specific to the analysis they are conducting. EmpPrior efficiently queries TreeBASE to find data that are similar to focal data, in terms of taxonomic and genetic sampling, and uses them to inform branch-length priors for the focal analysis. EmpPrior consists of two components: EmpPrior-search, written in Java to query TreeBASE, and EmpPrior-fit, written in R to parameterize branch-length distributions. An example analysis shows how the use of relevant, outside data is made possible by EmpPrior and improves tree-length estimates from a focal dataset. EmpPrior is easy to use, fast, and improves both the accuracy and precision of branch-length estimates in many circumstances. While EmpPrior's focus is on branch lengths, the strategy it employs could easily be extended to address other prior parameterization problems in phylogenetics. 3 figures, 1 table, and 13 references (Publisher abstract modified)
Downloads
Similar Publications
- Correctional Officer Fatalities in Line of Duty During 2005 to 2015: A Survival Analysis
- Differential Use of Jail Confinement in California - A Study of Jail Admissions in Three Counties - Final Report
- Habeas Litigation in U.S. District Courts: An Empirical Study of Habeas Corpus Cases Filed by State Prisoners Under the Antiterrorism and Effective Death Penalty Act of 1996, Final Technical Report