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QAPA: a new method for the systematic analysis of alternative polyadenylation from RNA-seq data

Overview of attention for article published in Genome Biology, 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 (91st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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51 X users
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1 Facebook page

Citations

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158 Dimensions

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189 Mendeley
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1 CiteULike
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Title
QAPA: a new method for the systematic analysis of alternative polyadenylation from RNA-seq data
Published in
Genome Biology, March 2018
DOI 10.1186/s13059-018-1414-4
Pubmed ID
Authors

Kevin C. H. Ha, Benjamin J. Blencowe, Quaid Morris

Abstract

Alternative polyadenylation (APA) affects most mammalian genes. The genome-wide investigation of APA has been hampered by an inability to reliably profile it using conventional RNA-seq. We describe 'Quantification of APA' (QAPA), a method that infers APA from conventional RNA-seq data. QAPA is faster and more sensitive than other methods. Application of QAPA reveals discrete, temporally coordinated APA programs during neurogenesis and that there is little overlap between genes regulated by alternative splicing and those by APA. Modeling of these data uncovers an APA sequence code. QAPA thus enables the discovery and characterization of programs of regulated APA using conventional RNA-seq.

X Demographics

X Demographics

The data shown below were collected from the profiles of 51 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 189 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 189 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 26%
Researcher 35 19%
Student > Bachelor 19 10%
Student > Master 13 7%
Student > Doctoral Student 7 4%
Other 19 10%
Unknown 46 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 51 27%
Agricultural and Biological Sciences 50 26%
Computer Science 8 4%
Medicine and Dentistry 5 3%
Engineering 5 3%
Other 18 10%
Unknown 52 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 23 April 2018.
All research outputs
#1,412,159
of 25,382,440 outputs
Outputs from Genome Biology
#1,121
of 4,468 outputs
Outputs of similar age
#30,786
of 344,304 outputs
Outputs of similar age from Genome Biology
#14
of 38 outputs
Altmetric has tracked 25,382,440 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 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 74% 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 344,304 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 38 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.