Cirscan

Cirscan infers circRNA-mediated competitive endogenous RNA (ceRNA) sponge mechanisms by reconstructing circRNA–miRNA–mRNA networks from human transcriptomic data.

Key Features:

• CircRNA–miRNA–mRNA network inference: Infers and reconstructs circRNA–miRNA–mRNA networks from multi-level expression data across two biological conditions (e.g., tumor versus normal) in human transcriptomes.
• Condition-specific sponge identification: Identifies differentially active sponge mechanisms that are specific to a given biological condition at large scale.
• Sponge score ranking: Assigns a "sponge score" to each circRNA–miRNA–mRNA subnetwork by integrating interaction reliability and expression levels to prioritize biologically relevant networks.
• Network visualization: Generates visual representations of top-ranked sponge subnetworks for inspection and interpretation.
• Enrichment analysis: Performs enrichment analyses on identified networks to aid biological interpretation and functional annotation.
• Candidate prioritization: Retrieves previously described sponge mechanisms and identifies novel circRNA candidates with potential sponge activity for downstream validation.

Scientific Applications:

• Discovery of ce-circRNAs: Prioritizes circRNAs acting as ce-circRNAs that modulate miRNA activity through competitive endogenous RNA interactions.
• Comparative condition analysis: Detects condition-specific sponge mechanisms between two states such as tumor versus normal tissue.
• Functional interpretation: Uses enrichment results from networks to infer pathways, biological functions, and potential therapeutic implications.
• Experimental prioritization: Produces ranked candidate sponge mechanisms to guide hypothesis generation and experimental validation.

Methodology:

Reconstruct circRNA–miRNA–mRNA networks from multi-level expression data across two conditions, compute a "sponge score" integrating interaction reliability and expression levels to rank subnetworks, visualize top-ranked subnetworks, and perform enrichment analyses on identified networks.