FFLtool

FFLtool: Feed-Forward Loop Detection in TF–miRNA Regulatory Networks

FFLtool identifies feed-forward loop (FFL) motifs involving transcription factors (TFs), microRNAs (miRNAs), and their target genes by integrating comprehensive TF–target and miRNA–target regulatory datasets from predicted and validated databases.

Key Features:

• Regulatory Data Integration: Integrates extensive TF–target and miRNA–target interaction data from multiple predicted and experimentally validated databases to support FFL identification in human systems.
• FFL Detection and Evidence Assessment: Detects potential FFLs within user-defined gene sets, assigns three evidence levels to evaluate reliability, and performs enrichment analysis to identify co-target genes regulated by the same TF and miRNA.
• Cancer-Specific FFL Exploration: Enables analysis of FFLs composed of differentially or specifically expressed TFs, miRNAs, and target genes in cancer-related contexts.

Scientific Applications:

• Regulatory Network Analysis: Investigates transcriptional and post-transcriptional regulation by characterizing TF–miRNA cross-talk and FFL motifs in biological processes and diseases, particularly cancer.

Methodology:

FFLtool integrates TF–target and miRNA–target regulatory interactions from multiple data sources and applies computational algorithms to detect candidate FFL motifs within specified gene sets. Identified FFLs are evaluated using tiered evidence criteria and enrichment analysis to assess regulatory significance.