Long non-coding RNAs and complex diseases: from experimental results to computational models

• Published in: Briefings in bioinformatics Vol. 18; no. 4; pp. 558 - 576
• Main Authors: Chen, Xing, Yan, Chenggang Clarence, Zhang, Xu, You, Zhu-Hong
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.07.2017; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bioinformatics; Biological activity; Biomarkers; Computational Biology; Computer applications; Computer Simulation; Disease control; Humans; Life cycles; Mathematical models; Mutation; Nematodes; Predictions; RNA, Long Noncoding - genetics; Task complexity; computational model; biological network; complex disease; lncRNA-disease association prediction; long non-coding RNA; machine learning; lncRNA–disease association prediction
• DOI: 10.1093/bib/bbw060
• ISSN: 1467-5463; 1477-4054

Abstract LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA-disease associations and predicting potential human lncRNA-disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.