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VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Overview of attention for article published in BMC Genomics, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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7 tweeters

Citations

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47 Dimensions

Readers on

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84 Mendeley
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Title
VarElect: the phenotype-based variation prioritizer of the GeneCards Suite
Published in
BMC Genomics, June 2016
DOI 10.1186/s12864-016-2722-2
Pubmed ID
Authors

Gil Stelzer, Inbar Plaschkes, Danit Oz-Levi, Anna Alkelai, Tsviya Olender, Shahar Zimmerman, Michal Twik, Frida Belinky, Simon Fishilevich, Ron Nudel, Yaron Guan-Golan, David Warshawsky, Dvir Dahary, Asher Kohn, Yaron Mazor, Sergey Kaplan, Tsippi Iny Stein, Hagit N. Baris, Noa Rappaport, Marilyn Safran, Doron Lancet

Abstract

Next generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing down further towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates. We describe a novel tool, VarElect ( http://ve.genecards.org ), a comprehensive phenotype-dependent variant/gene prioritizer, based on the widely-used GeneCards, which helps rapidly identify causal mutations with extensive evidence. The GeneCards suite offers an effective and speedy alternative, whereby >120 gene-centric automatically-mined data sources are jointly available for the task. VarElect cashes on this wealth of information, as well as on GeneCards' powerful free-text Boolean search and scoring capabilities, proficiently matching variant-containing genes to submitted disease/symptom keywords. The tool also leverages the rich disease and pathway information of MalaCards, the human disease database, and PathCards, the unified pathway (SuperPaths) database, both within the GeneCards Suite. The VarElect algorithm infers direct as well as indirect links between genes and phenotypes, the latter benefitting from GeneCards' diverse gene-to-gene data links in GenesLikeMe. Finally, our tool offers an extensive gene-phenotype evidence portrayal ("MiniCards") and hyperlinks to the parent databases. We demonstrate that VarElect compares favorably with several often-used NGS phenotyping tools, thus providing a robust facility for ranking genes, pointing out their likelihood to be related to a patient's disease. VarElect's capacity to automatically process numerous NGS cases, either in stand-alone format or in VCF-analyzer mode (TGex and VarAnnot), is indispensable for emerging clinical projects that involve thousands of whole exome/genome NGS analyses.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 2%
Brazil 1 1%
Unknown 81 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 25%
Student > Ph. D. Student 19 23%
Student > Master 10 12%
Student > Bachelor 10 12%
Student > Doctoral Student 5 6%
Other 11 13%
Unknown 8 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 40%
Agricultural and Biological Sciences 23 27%
Medicine and Dentistry 8 10%
Computer Science 3 4%
Engineering 2 2%
Other 5 6%
Unknown 9 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 July 2016.
All research outputs
#1,629,305
of 7,974,292 outputs
Outputs from BMC Genomics
#1,278
of 5,711 outputs
Outputs of similar age
#69,534
of 262,510 outputs
Outputs of similar age from BMC Genomics
#53
of 190 outputs
Altmetric has tracked 7,974,292 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,711 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 77% 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 262,510 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 190 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.