fRNC

fRNC constructs and scores RBP-ncRNA circuits (RNCs) by integrating transcriptomics, interactomics, proteomics and CLIP-seq or PARE data to identify RBPs and ncRNAs involved in gene-regulatory networks.

Key Features:

• Integration of Multi-Omics Data: Integrates transcriptomics, interactomics, and proteomics data to build multilayer RBP-ncRNA networks.
• Experimental Data Sources: Utilizes CLIP-seq (Crosslinking Immunoprecipitation sequencing) and PARE (Parallel Analysis of RNA Ends) experimental data to infer RBP–ncRNA interactions.
• Scoring Mechanism: Scores nodes (RBPs and ncRNAs) and edges (interactions) based on differential analysis of expression data to prioritize significant network components.
• Network Analysis Approaches: Identifies RNCs via global maximum scoring to detect globally significant RBPs and ncRNAs and via local maximum scoring using a greedy search initialized from defined starting nodes.
• Scalability and Robustness: Demonstrates scalability and robustness for detecting accurate sub-networks in large-scale biological datasets.

Scientific Applications:

• Disease association studies: Applied to esophageal carcinoma, breast cancer, and Alzheimer's disease to analyze collective behaviors of RBPs and interacting ncRNAs and to identify disease-associated processes and candidate biomarkers or therapeutic targets.
• Gene regulation analysis: Used to investigate mechanisms of gene regulation mediated by RBPs and ncRNAs within RBP-ncRNA circuits.

Methodology:

Constructs RBP-ncRNA networks from experimental data (CLIP-seq or PARE and expression data), scores nodes and edges using differential expression analysis, and extracts significant circuits using global maximum scoring and local greedy search strategies.