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The VAAST Variant Prioritizer (VVP): ultrafast, easy to use whole genome variant prioritization tool

Overview of attention for article published in BMC Bioinformatics, February 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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12 X users
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2 patents

Citations

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29 Dimensions

Readers on

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100 Mendeley
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Title
The VAAST Variant Prioritizer (VVP): ultrafast, easy to use whole genome variant prioritization tool
Published in
BMC Bioinformatics, February 2018
DOI 10.1186/s12859-018-2056-y
Pubmed ID
Authors

Steven Flygare, Edgar Javier Hernandez, Lon Phan, Barry Moore, Man Li, Anthony Fejes, Hao Hu, Karen Eilbeck, Chad Huff, Lynn Jorde, Martin G. Reese, Mark Yandell

Abstract

Prioritization of sequence variants for diagnosis and discovery of Mendelian diseases is challenging, especially in large collections of whole genome sequences (WGS). Fast, scalable solutions are needed for discovery research, for clinical applications, and for curation of massive public variant repositories such as dbSNP and gnomAD. In response, we have developed VVP, the VAAST Variant Prioritizer. VVP is ultrafast, scales to even the largest variant repositories and genome collections, and its outputs are designed to simplify clinical interpretation of variants of uncertain significance. We show that scoring the entire contents of dbSNP (> 155 million variants) requires only 95 min using a machine with 4 cpus and 16 GB of RAM, and that a 60X WGS can be processed in less than 5 min. We also demonstrate that VVP can score variants anywhere in the genome, regardless of type, effect, or location. It does so by integrating sequence conservation, the type of sequence change, allele frequencies, variant burden, and zygosity. Finally, we also show that VVP scores are consistently accurate, and easily interpreted, traits not shared by many commonly used tools such as SIFT and CADD. VVP provides rapid and scalable means to prioritize any sequence variant, anywhere in the genome, and its scores are designed to facilitate variant interpretation using ACMG and NHS guidelines. These traits make it well suited for operation on very large collections of WGS sequences.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 26%
Student > Ph. D. Student 16 16%
Student > Master 10 10%
Student > Bachelor 9 9%
Other 8 8%
Other 11 11%
Unknown 20 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 31 31%
Agricultural and Biological Sciences 18 18%
Computer Science 8 8%
Nursing and Health Professions 3 3%
Medicine and Dentistry 3 3%
Other 12 12%
Unknown 25 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 27 January 2022.
All research outputs
#3,097,006
of 22,994,508 outputs
Outputs from BMC Bioinformatics
#1,094
of 7,311 outputs
Outputs of similar age
#65,941
of 330,974 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 92 outputs
Altmetric has tracked 22,994,508 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,311 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 84% 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 330,974 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 92 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.