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GeneVetter: a web tool for quantitative monogenic assessment of rare diseases: Fig. 1.

Overview of attention for article published in Bioinformatics, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

news
1 news outlet
twitter
7 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
25 Mendeley
Title
GeneVetter: a web tool for quantitative monogenic assessment of rare diseases: Fig. 1.
Published in
Bioinformatics, July 2015
DOI 10.1093/bioinformatics/btv432
Pubmed ID
Authors

Christopher E. Gillies, Catherine C. Robertson, Matthew G. Sampson, Hyun Min Kang

Abstract

When performing DNA sequencing to diagnose affected individuals with monogenic forms of rare diseases, accurate attribution of causality to detected variants is imperative but imperfect. Even if a gene has variants already known to cause a disease, rare disruptive variants predicted to be causal are not always so, mainly due to imperfect ability to predict the pathogenicity of variants. Existing population-scale sequence resources such as 1000 Genomes are useful to quantify the "background prevalence" of an unaffected individual being falsely predicted to carry causal variants. We developed GeneVetter to allow users to quantify the "background prevalence" of subjects with predicted causal variants within specific genes under user-specified filtering parameters. GeneVetter helps quantify uncertainty in monogenic diagnosis and design genetic studies with support for power and sample size calculations for specific genes with specific filtering criteria. GeneVetter also allows users to analyze their own sequence data without sending genotype information over the Internet. Overall, GeneVetter is an interactive web tool that facilitates quantifying and accounting for the background prevalence of predicted pathogenic variants in a population. GeneVetter is available at http://genevetter.org/ CONTACT: mgsamps@med.umich.edu or hmkang@umich.edu SUPPLEMENTARY INFORMATION: Supplementary text is available at Bioinformatics online.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 32%
Student > Doctoral Student 3 12%
Other 3 12%
Student > Master 3 12%
Professor 2 8%
Other 4 16%
Unknown 2 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 36%
Medicine and Dentistry 5 20%
Agricultural and Biological Sciences 4 16%
Social Sciences 2 8%
Computer Science 2 8%
Other 1 4%
Unknown 2 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 31 May 2016.
All research outputs
#1,014,733
of 12,023,633 outputs
Outputs from Bioinformatics
#1,164
of 7,976 outputs
Outputs of similar age
#28,595
of 237,289 outputs
Outputs of similar age from Bioinformatics
#71
of 294 outputs
Altmetric has tracked 12,023,633 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
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 has done well, scoring higher than 84% 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 237,289 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 294 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.