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BioMed Research International - Volume 2015 2015, Article ID 839590, 14 pages -

Research Article

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China

Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, Heilongjiang 150086, China

Received 15 November 2014; Revised 5 February 2015; Accepted 6 February 2015

Academic Editor: Tatsuya Akutsu

Copyright © 2015 Yun Xiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Long noncoding RNAs lncRNAs have been shown to play key roles in various biological processes. However, functions of most lncRNAs are poorly characterized. Here, we represent a framework to predict functions of lncRNAs through construction of a regulatory network between lncRNAs and protein-coding genes. Using RNA-seq data, the transcript profiles of lncRNAs and protein-coding genes are constructed. Using the Bayesian network method, a regulatory network, which implies dependency relations between lncRNAs and protein-coding genes, was built. In combining protein interaction network, highly connected coding genes linked by a given lncRNA were subsequently used to predict functions of the lncRNA through functional enrichment. Application of our method to prostate RNA-seq data showed that 762 lncRNAs in the constructed regulatory network were assigned functions. We found that lncRNAs are involved in diverse biological processes, such as tissue development or embryo development e.g., nervous system development and mesoderm development. By comparison with functions inferred using the neighboring gene-based method and functions determined using lncRNA knockdown experiments, our method can provide comparable predicted functions of lncRNAs. Overall, our method can be applied to emerging RNA-seq data, which will help researchers identify complex relations between lncRNAs and coding genes and reveal important functions of lncRNAs.

Author: Yun Xiao, Yanling Lv, Hongying Zhao, Yonghui Gong, Jing Hu, Feng Li, Jinyuan Xu, Jing Bai, Fulong Yu, and Xia Li



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