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Variant detection sensitivity and biases in whole genome and exome sequencing

Overview of attention for article published in BMC Bioinformatics, July 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
52 tweeters

Citations

dimensions_citation
139 Dimensions

Readers on

mendeley
323 Mendeley
citeulike
6 CiteULike
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Title
Variant detection sensitivity and biases in whole genome and exome sequencing
Published in
BMC Bioinformatics, July 2014
DOI 10.1186/1471-2105-15-247
Pubmed ID
Authors

Alison M Meynert, Morad Ansari, David R FitzPatrick, Martin S Taylor

Abstract

Less than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. We set out to quantify and understand how WGS compares with the targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifying single nucleotide polymorphisms (SNPs) in exome targeted regions.

Twitter Demographics

The data shown below were collected from the profiles of 52 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 323 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 2%
United Kingdom 6 2%
Germany 2 <1%
Sweden 1 <1%
Italy 1 <1%
Brazil 1 <1%
France 1 <1%
Finland 1 <1%
Netherlands 1 <1%
Other 5 2%
Unknown 296 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 81 25%
Student > Ph. D. Student 70 22%
Student > Master 33 10%
Other 26 8%
Student > Bachelor 25 8%
Other 61 19%
Unknown 27 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 112 35%
Biochemistry, Genetics and Molecular Biology 89 28%
Medicine and Dentistry 42 13%
Computer Science 26 8%
Mathematics 3 <1%
Other 10 3%
Unknown 41 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 35. 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 13 September 2019.
All research outputs
#626,581
of 15,920,653 outputs
Outputs from BMC Bioinformatics
#60
of 5,768 outputs
Outputs of similar age
#8,786
of 194,987 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 9 outputs
Altmetric has tracked 15,920,653 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,768 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 98% 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 194,987 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them