Motivation: Circular RNAs (circRNAs) are long noncoding 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.
Results: We developed circRNA-sponging, a nextflow pipeline that (i) identifies circRNAs via backsplicing junctions detected in RNA-seq data, (ii) quantifies their expression values in relation to their linear counterparts spliced from the same gene, (iii) performs differential expression analysis, (iv) identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, (v) predicts miRNA binding sites on circRNAs, (vi) systematically investigates potential circRNA-miRNA sponging events, (vii) creates a network of competing endogenous RNAs and (viii) 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.
Availability and implementation: https://github.com/biomedbigdata/circRNA-sponging.
Supplementary information: Supplementary data are available at Bioinformatics Advances online.
© The Author(s) 2023. Published by Oxford University Press.