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CWDPRNP: a tool for cervid prion sequence analysis in program R

Overview of attention for article published in Bioinformatics, May 2017
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Mentioned by

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3 tweeters

Citations

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1 Dimensions

Readers on

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13 Mendeley
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Title
CWDPRNP: a tool for cervid prion sequence analysis in program R
Published in
Bioinformatics, May 2017
DOI 10.1093/bioinformatics/btx333
Pubmed ID
Authors

William L Miller, W David Walter

Abstract

Chronic wasting disease is a fatal neurological, disease caused by an infectious prion protein, which affects economically and ecologically important members of the family Cervidae. Single nucleotide polymorphisms within the prion protein gene have been linked to differential susceptibility to the disease in many species. Wildlife managers are seeking to determine the frequencies of disease-associated alleles and genotypes and delineate spatial genetic patterns. The CWDPRNP package, implemented in program R, provides a unified framework for analyzing prion protein gene variability and spatial structure. The CWDPRNP package, manual, and example data files are available at http://ecosystems.psu.edu/research/labs/walter-lab/additional-labs/population-genetics-lab . This package is available for all commonly used platforms. wlm159psu@gmail.com.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 23%
Researcher 3 23%
Other 2 15%
Student > Doctoral Student 1 8%
Student > Master 1 8%
Other 1 8%
Unknown 2 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 38%
Environmental Science 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Immunology and Microbiology 1 8%
Other 2 15%
Unknown 2 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 October 2017.
All research outputs
#7,517,848
of 12,023,743 outputs
Outputs from Bioinformatics
#6,457
of 7,976 outputs
Outputs of similar age
#154,864
of 272,961 outputs
Outputs of similar age from Bioinformatics
#134
of 184 outputs
Altmetric has tracked 12,023,743 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,976 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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 272,961 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 184 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.