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The Ensembl Variant Effect Predictor

Overview of attention for article published in Genome Biology (Online Edition), June 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)

Mentioned by

news
3 news outlets
blogs
2 blogs
twitter
75 tweeters
peer_reviews
1 peer review site
wikipedia
1 Wikipedia page
googleplus
1 Google+ user
video
1 video uploader

Citations

dimensions_citation
2642 Dimensions

Readers on

mendeley
2179 Mendeley
citeulike
9 CiteULike
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Title
The Ensembl Variant Effect Predictor
Published in
Genome Biology (Online Edition), June 2016
DOI 10.1186/s13059-016-0974-4
Pubmed ID
Authors

William McLaren, Laurent Gil, Sarah E. Hunt, Harpreet Singh Riat, Graham R. S. Ritchie, Anja Thormann, Paul Flicek, Fiona Cunningham

Abstract

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

Twitter Demographics

The data shown below were collected from the profiles of 75 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 2,179 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 <1%
United Kingdom 2 <1%
Ghana 1 <1%
Italy 1 <1%
Norway 1 <1%
Unknown 2169 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 462 21%
Researcher 398 18%
Student > Master 302 14%
Student > Bachelor 243 11%
Student > Doctoral Student 122 6%
Other 268 12%
Unknown 384 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 794 36%
Agricultural and Biological Sciences 446 20%
Medicine and Dentistry 174 8%
Computer Science 85 4%
Neuroscience 30 1%
Other 173 8%
Unknown 477 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 78. 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 2021.
All research outputs
#361,580
of 19,016,777 outputs
Outputs from Genome Biology (Online Edition)
#267
of 3,792 outputs
Outputs of similar age
#8,572
of 275,572 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 2 outputs
Altmetric has tracked 19,016,777 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,792 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.0. This one has done particularly well, scoring higher than 93% 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 275,572 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them