A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Abstract

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

“…Several publicly available databases store extensive biological data related to ncRNAs, including information on sequence, expression, and function (for a detailed description of ncRNA/disease-related databases, refer to references − ). However, despite the wealth of available data, only a few ncRNAs are currently linked to disease.…”

miRNAs in Heart Development and Disease

“…MIMRDA is written in R; similar tools based on Python (e.g., MDPBMP [192] or miRModuleNet [193]) or MatLab (e.g., LGDLDA [194]) are available as well. For a complete review of this category of tools, please refer to [195].…”