f RNC: Uncovering the dynamic and condition-specific RBP-ncRNA circuits from multi-omics data

Abstract

The RNA binding protein (RBP) and non-coding RNA (ncRNA) interacting networks are increasingly recognized as the main mechanism in gene regulation, and are tightly associated with cellular malfunction and disease. Here, we present fRNC, a systems biology tool to uncover the dynamic spectrum of RBP-ncRNA circuits (RNC) by integrating transcriptomics, interactomics and proteomics data. fRNC constructs the RBP-ncRNA network derived from CLIP-seq or PARE experiments. Given scoring on nodes and edges according to differential analysis of expression data, it finds an RNC containing global maximum significant RBPs and ncRNAs. Alternatively, it can also capture the locally maximum scoring RNC according to user-defined starting nodes with the greedy search. When compared with existing tools, fRNC can detect more accurate and robust sub-network with scalability. As shown in the cases of esophageal carcinoma, breast cancer and Alzheimer's disease, fRNC enables users to analyze the collective behaviors between RBP and the interacting ncRNAs, and reveal novel insights into the disease-associated processes. The fRNC R package is available at https://github.com/BioinformaticsSTU/fRNC.

Keywords: Gene regulation; NcRNA; R package; RNA binding protein; RNA binding protein-ncRNA circuits.

Graphical abstract

Fig. 1

Overview of the fRNC architecture. The RBP-ncRNA network is constructed from experiment-derived RBP-ncRNAs interactions. fRNC scores the nodes and edges according to differential analysis of expression data or survival analysis. Two algorithms, integer-linear programming approach (global) and greedy local search maximum approach are utilized to search the maximally scoring RNC.

Fig. 2

Comparison of the accuracy of fRNC, LncACT and WGCNA. (A-B) Expression abundance comparison between RBP and miRNA in BC and ESCA. (C-D) The comparison of the specificity versus sensitivity between fRNC, LncACT and WGCNA based on BC and ESCA. The real network size is set to 30,50 and 70, respectively. The four levels represent the expression abundance of RBP/miRNA by 0.5-fold, 1-fold, 2-fold and 5-fold, respectively. (E) Effects of simulated network size on the sensitivity of fRNC.

Fig. 3

Comparison of the robustness of fRNC, LncACT and WGCNA. (A) Jaccard index of node similarity of modules in BC data. (B) Jaccard index of node similarity of modules in ESCA data. (C-D)The log-log plots show that the degree distributions of the RNCs network follow the power law.