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Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts

Overview of attention for article published in BMC 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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

twitter
35 tweeters
facebook
1 Facebook page
f1000
1 research highlight platform

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
82 Mendeley
citeulike
5 CiteULike
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Title
Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0865-9
Pubmed ID
Authors

Jörg Hakenberg, Wei-Yi Cheng, Philippe Thomas, Ying-Chih Wang, Andrew V. Uzilov, Rong Chen

Abstract

Data from a plethora of high-throughput sequencing studies is readily available to researchers, providing genetic variants detected in a variety of healthy and disease populations. While each individual cohort helps gain insights into polymorphic and disease-associated variants, a joint perspective can be more powerful in identifying polymorphisms, rare variants, disease-associations, genetic burden, somatic variants, and disease mechanisms. We have set up a Reference Variant Store (RVS) containing variants observed in a number of large-scale sequencing efforts, such as 1000 Genomes, ExAC, Scripps Wellderly, UK10K; various genotyping studies; and disease association databases. RVS holds extensive annotations pertaining to affected genes, functional impacts, disease associations, and population frequencies. RVS currently stores 400 million distinct variants observed in more than 80,000 human samples. RVS facilitates cross-study analysis to discover novel genetic risk factors, gene-disease associations, potential disease mechanisms, and actionable variants. Due to its large reference populations, RVS can also be employed for variant filtration and gene prioritization. A web interface to public datasets and annotations in RVS is available at https://rvs.u.hpc.mssm.edu/ .

Twitter Demographics

The data shown below were collected from the profiles of 35 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Brazil 1 1%
France 1 1%
Belgium 1 1%
Netherlands 1 1%
Unknown 75 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 32%
Student > Ph. D. Student 20 24%
Student > Master 9 11%
Student > Bachelor 8 10%
Other 6 7%
Other 9 11%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 34%
Computer Science 18 22%
Biochemistry, Genetics and Molecular Biology 17 21%
Engineering 5 6%
Medicine and Dentistry 5 6%
Other 4 5%
Unknown 5 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 15 June 2016.
All research outputs
#1,105,114
of 17,826,855 outputs
Outputs from BMC Bioinformatics
#221
of 6,281 outputs
Outputs of similar age
#26,764
of 378,066 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 478 outputs
Altmetric has tracked 17,826,855 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 6,281 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 96% 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 378,066 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 92% of its contemporaries.
We're also able to compare this research output to 478 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.