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EmpPrior: using outside empirical data to inform branch-length priors for Bayesian phylogenetics

Overview of attention for article published in BMC Bioinformatics, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
EmpPrior: using outside empirical data to inform branch-length priors for Bayesian phylogenetics
Published in
BMC Bioinformatics, June 2016
DOI 10.1186/s12859-016-1132-4
Pubmed ID
Authors

John J. Andersen, Bradley J. Nelson, Jeremy M. Brown

Abstract

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. We developed the software package EmpPrior to facilitate the collection and incorporation of relevant, outside information when setting branch-length priors for phylogenetics. 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. In an example analysis, we show 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.

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X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 13%
Unknown 7 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 25%
Researcher 2 25%
Student > Ph. D. Student 1 13%
Unknown 3 38%
Readers by discipline Count As %
Computer Science 2 25%
Biochemistry, Genetics and Molecular Biology 1 13%
Business, Management and Accounting 1 13%
Agricultural and Biological Sciences 1 13%
Unknown 3 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 June 2016.
All research outputs
#6,153,768
of 25,246,334 outputs
Outputs from BMC Bioinformatics
#2,052
of 7,663 outputs
Outputs of similar age
#94,719
of 361,657 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 89 outputs
Altmetric has tracked 25,246,334 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,663 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 361,657 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.