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Variant Ranker: a web-tool to rank genomic data according to functional significance

Overview of attention for article published in BMC Bioinformatics, July 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
7 tweeters

Citations

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13 Dimensions

Readers on

mendeley
52 Mendeley
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Title
Variant Ranker: a web-tool to rank genomic data according to functional significance
Published in
BMC Bioinformatics, July 2017
DOI 10.1186/s12859-017-1752-3
Pubmed ID
Authors

John Alexander, Dimitris Mantzaris, Marianthi Georgitsi, Petros Drineas, Peristera Paschou

Abstract

The increasing volume and complexity of high-throughput genomic data make analysis and prioritization of variants difficult for researchers with limited bioinformatics skills. Variant Ranker allows researchers to rank identified variants and determine the most confident variants for experimental validation. We describe Variant Ranker, a user-friendly simple web-based tool for ranking, filtering and annotation of coding and non-coding variants. Variant Ranker facilitates the identification of causal variants based on novelty, effect and annotation information. The algorithm implements and aggregates multiple prediction algorithm scores, conservation scores, allelic frequencies, clinical information and additional open-source annotations using accessible databases via ANNOVAR. The available information for a variant is transformed into user-specified weights, which are in turn encoded into the ranking algorithm. Through its different modules, users can (i) rank a list of variants (ii) perform genotype filtering for case-control samples (iii) filter large amounts of high-throughput data based on user custom filter requirements and apply different models of inheritance (iv) perform downstream functional enrichment analysis through network visualization. Using networks, users can identify clusters of genes that belong to multiple ontology categories (like pathways, gene ontology, disease categories) and therefore expedite scientific discoveries. We demonstrate the utility of Variant Ranker to identify causal genes using real and synthetic datasets. Our results indicate that Variant Ranker exhibits excellent performance by correctly identifying and ranking the candidate genes CONCLUSIONS: Variant Ranker is a freely available web server on http://paschou-lab.mbg.duth.gr/Software.html . This tool will enable users to prioritise potentially causal variants and is applicable to a wide range of sequencing data.

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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 21%
Student > Ph. D. Student 9 17%
Student > Bachelor 6 12%
Student > Master 5 10%
Professor 3 6%
Other 10 19%
Unknown 8 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 31%
Agricultural and Biological Sciences 9 17%
Computer Science 5 10%
Medicine and Dentistry 3 6%
Engineering 3 6%
Other 6 12%
Unknown 10 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 22 April 2020.
All research outputs
#5,420,633
of 17,508,799 outputs
Outputs from BMC Bioinformatics
#2,328
of 6,185 outputs
Outputs of similar age
#96,507
of 273,930 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 7 outputs
Altmetric has tracked 17,508,799 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 6,185 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 62% 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 273,930 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 64% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.