Bioinformatics AdvancesBioinformaticsPeer-reviewed journal article
circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging
Markus HoffmanniD, Leon SchwartziD, Octavia-Andreea CioraiD, Nico TrummeriD, Lina-Liv WillruthiD, Jakub JankowskiiD, Hye‐Kyung LeeiD, Jan BaumbachiD, Priscilla A. FurthiD, Lothar G. HennighauseniD, Markus ListiD
Abstract
MotivationCircular RNAs (circRNAs) are long non-coding RNAs (lncRNAs) often associated with diseases and considered potential biomarkers for diagnosis and treatment. Among other functions, circRNAs have been shown to act as microRNA (miRNA) sponges, preventing the role of miRNAs that repress their targets. However, there is no pipeline to systematically assess the sponging potential of circRNAs. ResultsWe developed circRNA-sponging, a nextflow pipeline that (1) identifies circRNAs via backsplicing junctions detected in RNA-seq data, (2) quantifies their expression values in relation to their linear counterparts spliced from the same gene, (3) performs differential expression analysis, (4) identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, (5) predicts miRNA binding sites on circRNAs, (6) systematically investigates potential circRNA-miRNA sponging events, (7) creates a network of competing endogenous RNAs, and (8) identifies potential circRNA biomarkers. We showed the functionality of the circRNA-sponging pipeline using RNA sequencing data from brain tissues, where we identified two distinct types of circRNAs characterized by a specific ratio of the number of the binding site to the length of the transcript. The circRNA-sponging pipeline is the first end-to-end pipeline to identify circRNAs and their sponging systematically with raw total RNA-seq and miRNA-seq files, allowing us to better indicate the functional impact of circRNAs as a routine aspect in transcriptomic research. Availabilityhttps://github.com/biomedbigdata/circRNA-sponging Contactmarkus.daniel.hoffmann@tum.de; markus.list@tum.de Supplementary MaterialSupplementary data are available at Bioinformatic Advances online.
- organism: human
- assay: bulk RNA-seq
- assay: metagenomics
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- Citations
- 24
- FWCI
- 2.13
- Field percentile
- top 11%
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