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Vcfanno: fast, flexible annotation of genetic variants

Overview of attention for article published in Genome Biology (Online Edition), June 2016
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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 (91st percentile)

Mentioned by

twitter
28 tweeters
patent
2 patents

Citations

dimensions_citation
112 Dimensions

Readers on

mendeley
156 Mendeley
citeulike
5 CiteULike
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Title
Vcfanno: fast, flexible annotation of genetic variants
Published in
Genome Biology (Online Edition), June 2016
DOI 10.1186/s13059-016-0973-5
Pubmed ID
Authors

Brent S. Pedersen, Ryan M. Layer, Aaron R. Quinlan

Abstract

The integration of genome annotations is critical to the identification of genetic variants that are relevant to studies of disease or other traits. However, comprehensive variant annotation with diverse file formats is difficult with existing methods. Here we describe vcfanno, which flexibly extracts and summarizes attributes from multiple annotation files and integrates the annotations within the INFO column of the original VCF file. By leveraging a parallel "chromosome sweeping" algorithm, we demonstrate substantial performance gains by annotating ~85,000 variants per second with 50 attributes from 17 commonly used genome annotation resources. Vcfanno is available at https://github.com/brentp/vcfanno under the MIT license.

Twitter Demographics

The data shown below were collected from the profiles of 28 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 156 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
France 1 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Egypt 1 <1%
Japan 1 <1%
United States 1 <1%
Unknown 148 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 57 37%
Student > Ph. D. Student 28 18%
Student > Master 18 12%
Student > Bachelor 8 5%
Student > Doctoral Student 6 4%
Other 21 13%
Unknown 18 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 58 37%
Agricultural and Biological Sciences 49 31%
Computer Science 10 6%
Medicine and Dentistry 8 5%
Neuroscience 3 2%
Other 9 6%
Unknown 19 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 August 2020.
All research outputs
#1,288,378
of 19,764,860 outputs
Outputs from Genome Biology (Online Edition)
#1,227
of 3,865 outputs
Outputs of similar age
#24,903
of 278,457 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 2 outputs
Altmetric has tracked 19,764,860 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,865 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has gotten more attention than average, scoring higher than 68% 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 278,457 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 91% 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