Inferring non-coding RNA regulatory network from transcriptomic data and curated databases

Non-coding RNAs (ncRNAs) have played an indispensable role in regulating gene expression and cellular processes. However, the complex regulatory circuits between ncRNAs and coding genes remain understudied. Major challenges for inferring ncRNA regulatory network (NRN) on a transcriptome-wide scale include high dimensionality of both ncRNA and coding gene expression data, their context-dependent interaction, and lack of validation. We hereby propose a comprehensive analytical framework, namely Construction and Analysis of non-coding RNA regulatory NETwork (CAR-NET), specifically designed to infer NRN from transcriptomic data and curated databases on experimentally validated ncRNA-gene interactions and disease-related ncRNAs. At its core is a novel computationally efficient Bayesian network structure learning method that leverages the semi-bipartite graph structure of NRN for dimension reduction, edge orientation and global network search. We also developed and applied algorithms to perform differential network, subnetwork and pathway analysis of the inferred network. We showed the strength of CAR-NET in simulations and three case studies, encompassing lncRNA regulation in brain development, miRNA differential regulation in renal cell carcinoma and regulation by different classes of ncRNAs in different cell lines using bulk or single cell RNA-seq data. An R-shiny app (https://github.com/kehongjie/CAR-NET) was developed to implement the method(s) with interactive GUI.