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Choice of transcripts and software has a large effect on variant annotation

Overview of attention for article published in Genome Medicine, March 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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

blogs
2 blogs
twitter
99 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
128 Dimensions

Readers on

mendeley
505 Mendeley
citeulike
9 CiteULike
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Title
Choice of transcripts and software has a large effect on variant annotation
Published in
Genome Medicine, March 2014
DOI 10.1186/gm543
Pubmed ID
Authors

Davis J McCarthy, Peter Humburg, Alexander Kanapin, Manuel A Rivas, Kyle Gaulton, Jean-Baptiste Cazier, Peter Donnelly

Abstract

Variant annotation is a crucial step in the analysis of genome sequencing data. Functional annotation results can have a strong influence on the ultimate conclusions of disease studies. Incorrect or incomplete annotations can cause researchers both to overlook potentially disease-relevant DNA variants and to dilute interesting variants in a pool of false positives. Researchers are aware of these issues in general, but the extent of the dependency of final results on the choice of transcripts and software used for annotation has not been quantified in detail.

Twitter Demographics

The data shown below were collected from the profiles of 99 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 505 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 3%
United Kingdom 9 2%
Germany 4 <1%
Italy 3 <1%
Finland 3 <1%
Brazil 2 <1%
Sweden 2 <1%
Ireland 1 <1%
Australia 1 <1%
Other 7 1%
Unknown 460 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 130 26%
Researcher 125 25%
Student > Master 72 14%
Student > Bachelor 48 10%
Other 39 8%
Other 61 12%
Unknown 30 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 209 41%
Biochemistry, Genetics and Molecular Biology 129 26%
Medicine and Dentistry 49 10%
Computer Science 41 8%
Neuroscience 6 1%
Other 31 6%
Unknown 40 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 70. 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 26 April 2017.
All research outputs
#407,268
of 19,194,973 outputs
Outputs from Genome Medicine
#78
of 1,273 outputs
Outputs of similar age
#4,501
of 202,731 outputs
Outputs of similar age from Genome Medicine
#1
of 17 outputs
Altmetric has tracked 19,194,973 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,273 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.5. This one has done particularly well, scoring higher than 93% 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 202,731 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 97% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.