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SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions

Overview of attention for article published in Genome Biology (Online Edition), March 2018
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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 (91st percentile)

Mentioned by

news
1 news outlet
twitter
40 tweeters
patent
1 patent

Citations

dimensions_citation
225 Dimensions

Readers on

mendeley
270 Mendeley
citeulike
1 CiteULike
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Title
SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions
Published in
Genome Biology (Online Edition), March 2018
DOI 10.1186/s13059-018-1417-1
Pubmed ID
Authors

Juan L. Trincado, Juan C. Entizne, Gerald Hysenaj, Babita Singh, Miha Skalic, David J. Elliott, Eduardo Eyras

Abstract

Despite the many approaches to study differential splicing from RNA-seq, many challenges remain unsolved, including computing capacity and sequencing depth requirements. Here we present SUPPA2, a new method that addresses these challenges, and enables streamlined analysis across multiple conditions taking into account biological variability. Using experimental and simulated data, we show that SUPPA2 achieves higher accuracy compared to other methods, especially at low sequencing depth and short read length. We use SUPPA2 to identify novel Transformer2-regulated exons, novel microexons induced during differentiation of bipolar neurons, and novel intron retention events during erythroblast differentiation.

Twitter Demographics

The data shown below were collected from the profiles of 40 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 270 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 270 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 63 23%
Researcher 50 19%
Student > Master 34 13%
Student > Bachelor 24 9%
Student > Doctoral Student 13 5%
Other 34 13%
Unknown 52 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 104 39%
Agricultural and Biological Sciences 52 19%
Computer Science 15 6%
Engineering 6 2%
Medicine and Dentistry 5 2%
Other 21 8%
Unknown 67 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 03 August 2022.
All research outputs
#992,538
of 21,775,893 outputs
Outputs from Genome Biology (Online Edition)
#840
of 4,007 outputs
Outputs of similar age
#24,166
of 299,695 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 21,775,893 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,007 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.7. This one has done well, scoring higher than 79% 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 299,695 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 1 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