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spliceR: an R package for classification of alternative splicing and prediction of coding potential from RNA-seq data

Overview of attention for article published in BMC Bioinformatics, January 2014
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
29 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
55 Dimensions

Readers on

mendeley
218 Mendeley
citeulike
10 CiteULike
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Title
spliceR: an R package for classification of alternative splicing and prediction of coding potential from RNA-seq data
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-81
Pubmed ID
Authors

Kristoffer Vitting-Seerup, Bo Porse, Albin Sandelin, Johannes Waage

Abstract

RNA-seq data is currently underutilized, in part because it is difficult to predict the functional impact of alternate transcription events. Recent software improvements in full-length transcript deconvolution prompted us to develop spliceR, an R package for classification of alternative splicing and prediction of coding potential.

Twitter Demographics

The data shown below were collected from the profiles of 29 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 218 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 5%
United Kingdom 5 2%
Germany 2 <1%
Italy 2 <1%
Brazil 2 <1%
Ireland 1 <1%
France 1 <1%
Denmark 1 <1%
Japan 1 <1%
Other 2 <1%
Unknown 191 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 62 28%
Student > Ph. D. Student 57 26%
Student > Master 30 14%
Student > Bachelor 16 7%
Professor > Associate Professor 10 5%
Other 35 16%
Unknown 8 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 115 53%
Biochemistry, Genetics and Molecular Biology 47 22%
Computer Science 15 7%
Medicine and Dentistry 7 3%
Mathematics 5 2%
Other 17 8%
Unknown 12 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 01 September 2014.
All research outputs
#708,888
of 13,847,329 outputs
Outputs from BMC Bioinformatics
#141
of 5,161 outputs
Outputs of similar age
#11,620
of 190,728 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 12 outputs
Altmetric has tracked 13,847,329 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,161 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 97% 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 190,728 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 93% of its contemporaries.
We're also able to compare this research output to 12 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 91% of its contemporaries.