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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 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

twitter
40 tweeters
patent
1 patent

Citations

dimensions_citation
163 Dimensions

Readers on

mendeley
212 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 212 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 212 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 54 25%
Researcher 37 17%
Student > Master 27 13%
Student > Bachelor 19 9%
Student > Doctoral Student 12 6%
Other 26 12%
Unknown 37 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 82 39%
Agricultural and Biological Sciences 50 24%
Computer Science 12 6%
Engineering 5 2%
Medicine and Dentistry 4 2%
Other 14 7%
Unknown 45 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 April 2021.
All research outputs
#1,123,560
of 19,047,010 outputs
Outputs from Genome Biology (Online Edition)
#1,044
of 3,797 outputs
Outputs of similar age
#29,646
of 291,896 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 19,047,010 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 3,797 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has gotten more attention than average, scoring higher than 72% 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 291,896 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% 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