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PrioriTree: a utility for improving phylodynamic analyses in BEAST.
Gao, Jiansi; May, Michael R; Rannala, Bruce; Moore, Brian R.
Afiliação
  • Gao J; Department of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.
  • May MR; Department of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.
  • Rannala B; Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
  • Moore BR; Department of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.
Bioinformatics ; 39(1)2023 01 01.
Article em En | MEDLINE | ID: mdl-36592035
ABSTRACT

SUMMARY:

Phylodynamic methods are central to studies of the geographic and demographic history of disease outbreaks. Inference under discrete-geographic phylodynamic models-which involve many parameters that must be inferred from minimal information-is inherently sensitive to our prior beliefs about the model parameters. We present an interactive utility, PrioriTree, to help researchers identify and accommodate prior sensitivity in discrete-geographic inferences. Specifically, PrioriTree provides a suite of functions to generate input files for-and summarize output from-BEAST analyses for performing robust Bayesian inference, data-cloning analyses and assessing the relative and absolute fit of candidate discrete-geographic (prior) models to empirical datasets. AVAILABILITY AND IMPLEMENTATION PrioriTree is distributed as an R package available at https//github.com/jsigao/prioritree, with a comprehensive user manual provided at https//bookdown.org/jsigao/prioritree_manual/.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article