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fRNC: Uncovering the dynamic and condition-specific RBP-ncRNA circuits from multi-omics data
Leiming Jiang1, Shijia Hao1, Lirui Lin1
1Computational Systems Biology Laboratory, Department of Bioinformatics, Shantou University Medical College (SUMC), 515041 Shantou, China.
Computational and Structural Biotechnology Journal
|April 10, 2023
Summary
fRNC is a new systems biology tool that reveals RNA binding protein (RBP) and non-coding RNA (ncRNA) interactions. This tool helps uncover disease mechanisms by analyzing gene regulation networks.
Area of Science:
- Bioinformatics
- Systems Biology
- Molecular Biology
Background:
- RNA binding proteins (RBPs) and non-coding RNAs (ncRNAs) form critical gene regulatory networks.
- Dysregulation of these networks is linked to cellular dysfunction and various diseases.
Purpose of the Study:
- To introduce fRNC, a novel systems biology tool for analyzing dynamic RBP-ncRNA circuits (RNCs).
- To integrate multi-omics data for a comprehensive understanding of RNCs in disease.
Main Methods:
- fRNC integrates transcriptomics, interactomics, and proteomics data.
- It constructs RBP-ncRNA networks using CLIP-seq or PARE data.
- The tool identifies significant RNCs via global maximum or greedy search algorithms based on differential expression analysis.
Main Results:
- fRNC identifies accurate, robust, and scalable RBP-ncRNA subnetworks.
- The tool demonstrated effectiveness in analyzing esophageal carcinoma, breast cancer, and Alzheimer's disease datasets.
- It enables the analysis of collective RBP-ncRNA behaviors and reveals disease-associated processes.
Conclusions:
- fRNC provides novel insights into gene regulation and disease mechanisms.
- The tool facilitates the discovery of disease-specific RBP-ncRNA interactions.
- fRNC is available as an R package for broader research application.
