Circr, a Computational Tool to Identify miRNA:circRNA Associations

Martina Dori; Jimmy Caroli; Mattia Forcato

doi:10.3389/fbinf.2022.852834

Circr, a Computational Tool to Identify miRNA:circRNA Associations

Front Bioinform. 2022 Mar 11:2:852834. doi: 10.3389/fbinf.2022.852834. eCollection 2022.

Authors

Martina Dori¹, Jimmy Caroli^{1

2}, Mattia Forcato¹

Affiliations

¹ Department of Life Sciences, University of Modena and Reggio Emilia, Modena Italy.
² Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.

Abstract

Circular RNAs (circRNAs) are known to act as important regulators of the microRNA (miRNA) activity. Yet, computational resources to identify miRNA:circRNA interactions are mostly limited to already annotated circRNAs or affected by high rates of false positive predictions. To overcome these limitations, we developed Circr, a computational tool for the prediction of associations between circRNAs and miRNAs. Circr combines three publicly available algorithms for de novo prediction of miRNA binding sites on target sequences (miRanda, RNAhybrid, and TargetScan) and annotates each identified miRNA:target pairs with experimentally validated miRNA:RNA interactions and binding sites for Argonaute proteins derived from either ChIPseq or CLIPseq data. The combination of multiple tools for the identification of a single miRNA recognition site with experimental data allows to efficiently prioritize candidate miRNA:circRNA interactions for functional studies in different organisms. Circr can use its internal annotation database or custom annotation tables to enhance the identification of novel and not previously annotated miRNA:circRNA sites in virtually any species. Circr is written in Python 3.6 and is released under the GNU GPL3.0 License at https://github.com/bicciatolab/Circr.

Keywords: circRNA; interactions prediction; miRNA; miRNA binding; prediction tools.