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UCSC Data Integrator and Variant Annotation Integrator

Overview of attention for article published in Bioinformatics, January 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)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

news
1 news outlet
twitter
17 X users
facebook
1 Facebook page

Citations

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57 Dimensions

Readers on

mendeley
64 Mendeley
citeulike
2 CiteULike
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Title
UCSC Data Integrator and Variant Annotation Integrator
Published in
Bioinformatics, January 2016
DOI 10.1093/bioinformatics/btv766
Pubmed ID
Authors

Angie S Hinrichs, Brian J Raney, Matthew L Speir, Brooke Rhead, Jonathan Casper, Donna Karolchik, Robert M Kuhn, Kate R Rosenbloom, Ann S Zweig, David Haussler, W James Kent

Abstract

Two new tools on the UCSC Genome Browser web site provide improved ways of combining information from multiple datasets, optionally including the user's own custom track data and/or data from track hubs. The Data Integrator combines columns from multiple data tracks, showing all items from the first track along with overlapping items from the other tracks. The Variant Annotation Integrator is tailored to adding functional annotations to variant calls; it offers a more restricted set of underlying data tracks but adds predictions of each variant's consequences for any overlapping or nearby gene transcript. When available, it optionally adds additional annotations including effect prediction scores from dbNSFP (Liu et al., 2015) for missense mutations, ENCODE regulatory summary tracks (The ENCODE Project Consortium, 2012), and conservation scores (Siepel et al., 2005; Pollard et al., 2010). The web tools are freely available at http://genome.ucsc.edu/ and the underlying database is available for download at http://hgdownload.cse.ucsc.edu/. The software (written in C and Javascript) is available from https://genome-store.ucsc.edu/ and is freely available for academic and non-profit usage; commercial users must obtain a license. [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Netherlands 2 3%
United States 1 2%
France 1 2%
Unknown 58 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 33%
Student > Ph. D. Student 12 19%
Student > Master 7 11%
Student > Bachelor 3 5%
Professor 3 5%
Other 10 16%
Unknown 8 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 39%
Biochemistry, Genetics and Molecular Biology 18 28%
Computer Science 6 9%
Medicine and Dentistry 4 6%
Immunology and Microbiology 1 2%
Other 1 2%
Unknown 9 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 01 March 2017.
All research outputs
#2,003,784
of 25,374,647 outputs
Outputs from Bioinformatics
#1,222
of 12,809 outputs
Outputs of similar age
#33,451
of 400,126 outputs
Outputs of similar age from Bioinformatics
#30
of 163 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done particularly well, scoring higher than 90% 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 400,126 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 163 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.