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KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data

Overview of attention for article published in BMC Bioinformatics, April 2012
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

blogs
1 blog
twitter
12 tweeters

Citations

dimensions_citation
55 Dimensions

Readers on

mendeley
165 Mendeley
citeulike
5 CiteULike
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Title
KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data
Published in
BMC Bioinformatics, April 2012
DOI 10.1186/1471-2105-13-s6-s5
Pubmed ID
Authors

Gustavo AT Sacomoto, Janice Kielbassa, Rayan Chikhi, Raluca Uricaru, Pavlos Antoniou, Marie-France Sagot, Pierre Peterlongo, Vincent Lacroix

Abstract

In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KISSPLICE, to extract alternative splicing events.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
France 4 2%
Italy 3 2%
Germany 2 1%
Switzerland 1 <1%
Austria 1 <1%
Sweden 1 <1%
Netherlands 1 <1%
Paraguay 1 <1%
Other 6 4%
Unknown 140 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 48 29%
Student > Ph. D. Student 41 25%
Student > Master 32 19%
Other 10 6%
Professor > Associate Professor 7 4%
Other 20 12%
Unknown 7 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 111 67%
Biochemistry, Genetics and Molecular Biology 19 12%
Computer Science 16 10%
Medicine and Dentistry 4 2%
Chemistry 2 1%
Other 4 2%
Unknown 9 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 19 June 2013.
All research outputs
#1,217,003
of 14,013,582 outputs
Outputs from BMC Bioinformatics
#417
of 5,221 outputs
Outputs of similar age
#10,177
of 122,496 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 6 outputs
Altmetric has tracked 14,013,582 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,221 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 92% 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 122,496 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 91% of its contemporaries.
We're also able to compare this research output to 6 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