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Specific identification and quantification of circular RNAs from sequencing data

Overview of attention for article published in Bioinformatics, November 2015
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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 (85th percentile)
  • Average Attention Score compared to outputs of the same age and source

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172 Mendeley
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Title
Specific identification and quantification of circular RNAs from sequencing data
Published in
Bioinformatics, November 2015
DOI 10.1093/bioinformatics/btv656
Pubmed ID
Authors

Jun Cheng, Franziska Metge, Christoph Dieterich

Abstract

Circular RNAs (circRNAs) are a poorly characterised class of molecules that have been identified decades ago. Emerging high-throughput sequencing methods as well as first reports on confirmed functions have sparked new interest in this RNA species. However, the computational detection and quantification tools are still limited. We developed the software tandem, DCC and CircTest. DCC uses output from the STAR read mapper to systematically detect back-splice junctions in next-generation sequencing data. DCC applies a series of filters and integrates data across replicate sets to arrive at a precise list of circRNA candidates. We assessed the detection performance of DCC on a newly generated mouse brain data set and publicly available sequencing data. Our software achieves a much higher precision than state-of-the-art competitors at similar sensitivity levels. Moreover, DCC estimates circRNA vs. host gene expression from counting junction and non-junction reads. These read counts are finally used to test for host gene-independence of circRNA expression across different experimental conditions by our R package CircTest. We demonstrate the benefits of this approach on previously reported age-dependent circRNAs in the fruit fly. The source code of DCC and CircTest is licensed under the GNU General Public Licence (GPL) version 3 and available from https://github.com/dieterich-lab/[DCC or CircTest]. [email protected].

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 172 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 <1%
France 1 <1%
Italy 1 <1%
Unknown 169 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 20%
Researcher 35 20%
Student > Bachelor 22 13%
Student > Master 21 12%
Student > Doctoral Student 9 5%
Other 17 10%
Unknown 33 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 56 33%
Agricultural and Biological Sciences 44 26%
Medicine and Dentistry 9 5%
Computer Science 8 5%
Neuroscience 6 3%
Other 10 6%
Unknown 39 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 01 April 2016.
All research outputs
#3,081,121
of 25,374,917 outputs
Outputs from Bioinformatics
#2,552
of 12,809 outputs
Outputs of similar age
#41,784
of 297,467 outputs
Outputs of similar age from Bioinformatics
#114
of 198 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. 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 297,467 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 85% of its contemporaries.
We're also able to compare this research output to 198 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.