Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Mar 11:2:852834.
doi: 10.3389/fbinf.2022.852834. eCollection 2022.

Circr, a Computational Tool to Identify miRNA:circRNA Associations

Martina Dori  1 , Jimmy Caroli  1   2 , Mattia Forcato  1
Affiliations

Circr, a Computational Tool to Identify miRNA:circRNA Associations

Martina Dori et al. Front Bioinform. .

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
circRNAs sequence extraction. As a first step, Circr takes a set of coordinates in BED format as input. By default, it assumes that the transcripts overlapping genes on the same strand include only exons, therefore it retrieves the coordinates of the included features and uses them to construct the FASTA sequence of each circRNA. If the input file includes the exact composition of the circRNA (or if users want to avoid the automatic exon-only selection), Circr skips the initial exon/intron splitting and uses the supplied coordinates to retrieve the FASTA sequence.
FIGURE 2
FIGURE 2
Prediction of miRNA:circRNA interactions. The list of FASTA sequences generated in the first step is then given as input to each of the 3 tools that are included in Circr, i.e., miRanda, RNAhybrid, and TargetScan. The result for each tool is then parsed and collected in a comprehensive result table where only unique miRNA:circRNA interacting sites are retained. White spaces indicate a mismatch, pipe represents a match while colon represent a G-U pair.
FIGURE 3
FIGURE 3
Comparison of predicted interaction against validated databases. The final step consists in the annotation of the predicted miRNA:circRNA interactions with experimentally validated miRNA-RNA sites and peaks of Argonaute binding. The final table is then saved as a CSV format file.
FIGURE 4
FIGURE 4
Summary of the results obtained with Circr on a server with Ubuntu 18.04. The analysis was performed on 100 circRNAs selected from the list of transcripts expressed in the lateral cortex of the mouse developing brain. The BED file including the coordinates of the chosen circRNAs was supplied both as the full sequence and as the set of final features.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Agarwal V., Bell G. W., Nam J. W., Bartel D. P. (2015). Predicting Effective microRNA Target Sites in Mammalian mRNAs. Elife 4. 10.7554/eLife.05005 - DOI - PMC - PubMed
    1. Ashwal-Fluss R., Meyer M., Pamudurti N. R., Ivanov A., Bartok O., Hanan M., et al. (2014). circRNA Biogenesis Competes with Pre-mRNA Splicing. Mol. Cel 56 (1), 55–66. 10.1016/j.molcel.2014.08.019 - DOI - PubMed
    1. Balakrishnan I., Yang X., Brown J., Ramakrishnan A., Torok-Storb B., Kabos P., et al. (2014). Genome-wide Analysis of miRNA-mRNA Interactions in Marrow Stromal Cells. Stem Cells 32 (3), 662–673. 10.1002/stem.1531 - DOI - PMC - PubMed
    1. Bandyopadhyay S., Mitra R. (2009). TargetMiner: microRNA Target Prediction with Systematic Identification of Tissue-specific Negative Examples. Bioinformatics 25 (20), 2625–2631. 10.1093/bioinformatics/btp503 - DOI - PubMed
    1. Barrett S. P., Salzman J. (2016). Circular RNAs: Analysis, Expression and Potential Functions. Development 143 (11), 1838–1847. 10.1242/dev.128074 - DOI - PMC - PubMed

LinkOut - more resources