Title:Computational Methods for Functional Characterization of lncRNAS in
Human Diseases: A Focus on Co-Expression Networks
Volume: 19
Issue: 1
Author(s): Prabhash Jha, Miguel Barbeiro, Adrien Lupieri, Elena Aikawa, Shizuka Uchida and Masanori Aikawa*
Affiliation:
- Center for Excellence in Vascular Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston,
USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
Keywords:
Long noncoding RNAs, co-expression network, WGCNA, functional characterization, computational methods, microRNAs, network biology, human disease.
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.