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Quantifying single nucleotide variant detection sensitivity in exome sequencing

Overview of attention for article published in BMC Bioinformatics, June 2013
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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18 X users
googleplus
1 Google+ user
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1 research highlight platform

Citations

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73 Dimensions

Readers on

mendeley
225 Mendeley
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8 CiteULike
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Title
Quantifying single nucleotide variant detection sensitivity in exome sequencing
Published in
BMC Bioinformatics, June 2013
DOI 10.1186/1471-2105-14-195
Pubmed ID
Authors

Alison M Meynert, Louise S Bicknell, Matthew E Hurles, Andrew P Jackson, Martin S Taylor

Abstract

The targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed.

X Demographics

X Demographics

The data shown below were collected from the profiles of 18 X users 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 225 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 2%
Italy 3 1%
Sweden 2 <1%
United States 2 <1%
Spain 2 <1%
Australia 1 <1%
South Africa 1 <1%
Norway 1 <1%
India 1 <1%
Other 7 3%
Unknown 201 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 82 36%
Student > Ph. D. Student 50 22%
Student > Master 22 10%
Other 16 7%
Student > Bachelor 13 6%
Other 24 11%
Unknown 18 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 112 50%
Biochemistry, Genetics and Molecular Biology 47 21%
Medicine and Dentistry 17 8%
Computer Science 14 6%
Engineering 3 1%
Other 10 4%
Unknown 22 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 04 November 2020.
All research outputs
#2,796,562
of 25,837,817 outputs
Outputs from BMC Bioinformatics
#777
of 7,763 outputs
Outputs of similar age
#23,238
of 210,619 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 90 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,763 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done well, scoring higher than 89% 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 210,619 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 88% of its contemporaries.
We're also able to compare this research output to 90 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.