Circr

Circr predicts associations between microRNAs (miRNAs) and circular RNAs (circRNAs) to identify miRNA:circRNA interactions that may regulate gene expression.

Key Features:

• Multi-algorithm prediction: Integrates miRanda, RNAhybrid, and TargetScan for de novo prediction of miRNA binding sites on target sequences.
• Cross-validation of sites: Cross-validates potential miRNA recognition sites across miRanda, RNAhybrid, and TargetScan to improve reliability of predicted interactions.
• Experimental annotation: Annotates each miRNA:target pair with experimentally validated miRNA:RNA interactions and Argonaute (AGO) binding sites derived from ChIP-seq and CLIP-seq data.
• Annotation flexibility: Uses either an internal annotation database or user-provided custom annotation tables to support analysis of novel and unannotated miRNA:circRNA sites across species.
• Implementation: Implemented in Python 3.6.

Scientific Applications:

• Candidate prioritization: Prioritizes candidate miRNA:circRNA interactions for downstream functional validation.
• Discovery of novel sites: Identifies putative miRNA binding sites on unannotated circRNAs across multiple species.
• Regulatory mechanism analysis: Supports investigations of circRNA-mediated regulation of gene expression through miRNA interactions.
• Integration with experimental data: Leverages AGO ChIP-seq and CLIP-seq annotations to strengthen evidence for predicted miRNA:circRNA interactions.

Methodology:

Performs de novo miRNA binding site prediction using miRanda, RNAhybrid, and TargetScan, cross-validates predicted sites across those algorithms, annotates miRNA:target pairs with experimentally validated miRNA:RNA interactions and AGO binding sites from ChIP-seq and CLIP-seq, and accepts either an internal annotation database or custom annotation tables; implemented in Python 3.6.