Journal
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
Volume 2, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2014.00088
Keywords
long non-coding RNA; RNA-binding protein; GWAS; CLIP-Seq; RNA-Seq
Funding
- Ministry of Science and Technology of China, National Basic Research Program [2011CB811300]
- National Natural Science Foundation of China [91440110, 31230042, 31370791, 31471223, 31401975]
- Guangdong Province [S2012010010510, S2013010012457]
- Science and Technology New Star in Zhu-Jiang Guangzhou city [2012J2200025]
- Fundamental Research Funds for the Central Universities [2011330003161070, 141gjc18]
- China Postdoctoral Science Foundation [200902348]
- Guangdong Province Key Laboratory of Computational Science
- Guangdong Province Computational Science Innovative Research Team
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Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein-lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP-IncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulate gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP-lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.
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