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PureCLIP: capturing target-specific protein–RNA interaction footprints from single-nucleotide CLIP-seq data

Overview of attention for article published in Genome Biology, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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17 X users

Citations

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Title
PureCLIP: capturing target-specific protein–RNA interaction footprints from single-nucleotide CLIP-seq data
Published in
Genome Biology, December 2017
DOI 10.1186/s13059-017-1364-2
Pubmed ID
Authors

Sabrina Krakau, Hugues Richard, Annalisa Marsico

Abstract

The iCLIP and eCLIP techniques facilitate the detection of protein-RNA interaction sites at high resolution, based on diagnostic events at crosslink sites. However, previous methods do not explicitly model the specifics of iCLIP and eCLIP truncation patterns and possible biases. We developed PureCLIP ( https://github.com/skrakau/PureCLIP ), a hidden Markov model based approach, which simultaneously performs peak-calling and individual crosslink site detection. It explicitly incorporates a non-specific background signal and, for the first time, non-specific sequence biases. On both simulated and real data, PureCLIP is more accurate in calling crosslink sites than other state-of-the-art methods and has a higher agreement across replicates.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 132 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 27%
Researcher 19 14%
Student > Master 16 12%
Student > Bachelor 12 9%
Student > Doctoral Student 6 5%
Other 16 12%
Unknown 28 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 49 37%
Agricultural and Biological Sciences 26 20%
Computer Science 12 9%
Neuroscience 3 2%
Pharmacology, Toxicology and Pharmaceutical Science 3 2%
Other 9 7%
Unknown 30 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 21 April 2020.
All research outputs
#3,539,290
of 25,382,440 outputs
Outputs from Genome Biology
#2,454
of 4,468 outputs
Outputs of similar age
#74,415
of 448,935 outputs
Outputs of similar age from Genome Biology
#33
of 48 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% 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 is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
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 448,935 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 83% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.