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GrabBlur - a framework to facilitate the secure exchange of whole-exome and -genome SNV data using VCF files

Overview of attention for article published in BMC Genomics, May 2014
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Title
GrabBlur - a framework to facilitate the secure exchange of whole-exome and -genome SNV data using VCF files
Published in
BMC Genomics, May 2014
DOI 10.1186/1471-2164-15-s4-s8
Pubmed ID
Authors

Björn Stade, Dominik Seelow, Ingo Thomsen, Michael Krawczak, Andre Franke

Abstract

Next Generation Sequencing (NGS) of whole exomes or genomes is increasingly being used in human genetic research and diagnostics. Sharing NGS data with third parties can help physicians and researchers to identify causative or predisposing mutations for a specific sample of interest more efficiently. In many cases, however, the exchange of such data may collide with data privacy regulations. GrabBlur is a newly developed tool to aggregate and share NGS-derived single nucleotide variant (SNV) data in a public database, keeping individual samples unidentifiable. In contrast to other currently existing SNV databases, GrabBlur includes phenotypic information and contact details of the submitter of a given database entry. By means of GrabBlur human geneticists can securely and easily share SNV data from resequencing projects. GrabBlur can ease the interpretation of SNV data by offering basic annotations, genotype frequencies and in particular phenotypic information - given that this information was shared - for the SNV of interest.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 6%
United States 1 6%
Germany 1 6%
Unknown 13 81%

Demographic breakdown

Readers by professional status Count As %
Other 4 25%
Researcher 4 25%
Student > Ph. D. Student 2 13%
Student > Doctoral Student 1 6%
Student > Master 1 6%
Other 3 19%
Unknown 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 31%
Biochemistry, Genetics and Molecular Biology 4 25%
Computer Science 2 13%
Economics, Econometrics and Finance 1 6%
Social Sciences 1 6%
Other 0 0%
Unknown 3 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 16 March 2015.
All research outputs
#18,402,666
of 22,794,367 outputs
Outputs from BMC Genomics
#8,175
of 10,648 outputs
Outputs of similar age
#163,055
of 226,347 outputs
Outputs of similar age from BMC Genomics
#127
of 197 outputs
Altmetric has tracked 22,794,367 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,648 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 197 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.