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FUCHS—towards full circular RNA characterization using RNAseq

Overview of attention for article published in PeerJ, February 2017
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Title
FUCHS—towards full circular RNA characterization using RNAseq
Published in
PeerJ, February 2017
DOI 10.7717/peerj.2934
Pubmed ID
Authors

Franziska Metge, Lisa F. Czaja-Hasse, Richard Reinhardt, Chistoph Dieterich

Abstract

Circular RNAs (circRNAs) belong to a recently re-discovered species of RNA that emerge during RNA maturation through a process called back-splicing. A downstream 5' splice site is linked to an upstream 3' splice site to form a circular transcript instead of a canonical linear transcript. Recent advances in next-generation sequencing (NGS) have brought circRNAs back into the focus of many scientists. Since then, several studies reported that circRNAs are differentially expressed across tissue types and developmental stages, implying that they are actively regulated and not merely a by-product of splicing. Though functional studies have shown that some circRNAs could act as miRNA-sponges, the function of most circRNAs remains unknown. To expand our understanding of possible roles of circular RNAs, we propose a new pipeline that could fully characterizes candidate circRNA structure from RNAseq data-FUCHS: FUll CHaracterization of circular RNA using RNA-Sequencing. Currently, most computational prediction pipelines use back-spliced reads to identify circular RNAs. FUCHS extends this concept by considering all RNA-seq information from long reads (typically >150 bp) to learn more about the exon coverage, the number of double break point fragments, the different circular isoforms arising from one host-gene, and the alternatively spliced exons within the same circRNA boundaries. This new knowledge will enable the user to carry out differential motif enrichment and miRNA seed analysis to determine potential regulators during circRNA biogenesis. FUCHS is an easy-to-use Python based pipeline that contributes a new aspect to the circRNA research.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 30%
Student > Master 11 21%
Researcher 8 15%
Student > Bachelor 4 8%
Student > Postgraduate 2 4%
Other 3 6%
Unknown 9 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 30%
Agricultural and Biological Sciences 12 23%
Neuroscience 5 9%
Medicine and Dentistry 3 6%
Engineering 3 6%
Other 3 6%
Unknown 11 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 February 2017.
All research outputs
#11,654,723
of 15,268,389 outputs
Outputs from PeerJ
#6,679
of 8,752 outputs
Outputs of similar age
#178,440
of 260,399 outputs
Outputs of similar age from PeerJ
#248
of 301 outputs
Altmetric has tracked 15,268,389 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
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