Computational Methods for Functional Characterization of lncRNAS in Human Diseases: A Focus on Co-Expression Networks

Abstract

Treatment of many human diseases involves small-molecule drugs.Some target proteins, however, are not druggable with traditional strategies. Innovative RNA-targeted therapeutics may overcome such a challenge. Long noncoding RNAs (lncRNAs) are transcribed RNAs that do not translate into proteins. Their ability to interact with DNA, RNA, microRNAs (miRNAs), and proteins makes them an interesting target for regulating gene expression and signaling pathways.In the past decade, a catalog of lncRNAs has been studied in several human diseases. One of the challenges with lncRNA studies include their lack of coding potential, making, it difficult to characterize them in wet-lab experiments functionally. Several computational tools have thus been designed to characterize functions of lncRNAs centered around lncRNA interaction with proteins and RNA, especially miRNAs. This review comprehensively summarizes the methods and tools for lncRNA-RNA interactions and lncRNA-protein interaction prediction.We discuss the tools related to lncRNA interaction prediction using commonlyused models: ensemble-based, machine-learning-based, molecular-docking and network-based computational models. In biology, two or more genes co-expressed tend to have similar functions. Coexpression network analysis is, therefore, one of the most widely-used methods for understanding the function of lncRNAs. A major focus of our study is to compile literature related to the functional prediction of lncRNAs in human diseases using co-expression network analysis. In summary, this article provides relevant information on the use of appropriate computational tools for the functional characterization of lncRNAs that help wet-lab researchers design mechanistic and functional experiments.