CircNet

CircNet integrates circRNA sequencing datasets and predictive algorithms to identify circular RNAs and characterize circRNA–miRNA–gene regulatory networks for cancer research.

Key Features:

• Dataset integration: Aggregates circRNA datasets from circAtlas, MiOncoCirc, and The Cancer Genome Atlas (TCGA), comprising 2,732 samples across 37 cancer types.
• circRNA detection: Leverages several circRNA detection algorithms to identify circRNAs from sequencing data.
• miRNA target prediction: Predicts target microRNAs from full-length circRNA sequences using PITA, miRanda, and TargetScan.
• Regulatory network construction: Builds circRNA–miRNA–gene regulatory networks from predicted interactions.
• Experimental interaction support: Incorporates 384,897 experimentally verified miRNA–target interactions from miRTarBase to support network construction and validation.

Scientific Applications:

• Biomarker discovery: Enables investigation of circRNAs as diagnostic and prognostic biomarkers across multiple cancer types.
• Mechanistic studies: Supports elucidation of molecular mechanisms of cancer pathogenesis via circRNA–miRNA–gene networks.
• Therapeutic research: Assists identification and validation of candidate circRNA-related therapeutic targets and pathways.
• Network validation: Facilitates construction and validation of circRNA–miRNA–gene regulatory networks using experimentally verified miRNA–target interactions.

Methodology:

Integrates circAtlas, MiOncoCirc, and TCGA datasets (2,732 samples, 37 cancer types), applies multiple circRNA detection algorithms, predicts miRNA targets from full-length circRNA sequences using PITA, miRanda, and TargetScan, incorporates 384,897 miRTarBase miRNA–target interactions, and constructs circRNA–miRNA–gene regulatory networks.