Evidence map›Paper›PMID 36592035›Full record

ArticleBioinformatics (Oxford, England)2023

PrioriTree: a utility for improving phylodynamic analyses in BEAST.

Jiansi Gao, Michael R May, Bruce Rannala, Brian R Moore

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Model misspecification misleads inference of the spatial dynamics of disease outbreaks.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jiansi GaoDepartment of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.ORCID 0000-0002-5108-828X
Michael R MayDepartment of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.
Bruce RannalaDepartment of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.
Brian R MooreDepartment of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.

Funding

Statistical Methods and Algorithms for Population Genomic InferenceR01GM123306 · NIGMS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI RANNALA, BRUCE · 2020 to 2023
$1.6M
National Science FoundationNIGMS NIH HHS R01 GM123306
6 · The paper itself

Abstract

summaryPhylodynamic 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/.

Indexed as

Disease OutbreaksSoftwareBayes Theorem

Identifiers

PMID36592035
PMCPMC9841403

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.