miRFFLDB

miRFFL.DB provides an integrated resource for human microRNA (miRNA)–transcription factor (TF)–target gene (TG) coregulatory networks and identifies Feed Forward Loops (FFLs) linking miRNAs, TFs, and TGs.

Key Features:

• Integration of regulatory elements: Aggregates interactions among microRNAs (miRNAs), transcription factors (TFs), and target genes (TGs) from high-throughput studies.
• Coregulatory network mapping: Maps miRNA–TF–TG interplay into human coregulatory networks representing transcriptional and post-transcriptional regulation.
• FFL identification: Identifies recurring three-node regulatory circuits (Feed Forward Loops, FFLs) in which a miRNA and a TF regulate a common TG and one regulator controls the other.
• FFL types: Detects two classes of motifs: miRNA-FFLs and TF-FFLs.
• Computational approach: Applies graph theory principles using in-house scripts to detect FFL motifs.
• Biological scope: Focuses specifically on human miRNA–TF–TG interactions relevant to gene regulation dynamics.

Scientific Applications:

• Gene regulatory mechanism analysis: Use identified FFLs to study dynamics of transcriptional and post-transcriptional regulation.
• Disease research: Investigate dysregulation of miRNA–TF–TG networks and FFLs in disease initiation and progression.
• Network motif discovery: Characterize recurring regulatory-circuits within human regulatory networks.
• Candidate generation for validation: Provide prioritized miRNA–TF–TG triplets for experimental follow-up.

Methodology:

Aggregates interactions from high-throughput studies and applies graph theory-based in-house scripts to identify miRNA-FFL and TF-FFL three-node motifs.