starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 ( to systematically identify the RNA-RNA and protein-RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285,000 protein-RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10,000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

References in zbMATH (referenced in 10 articles )

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  1. Dhawan, Andrew; Harris, Adrian L.; Buffa, Francesca M.; Scott, Jacob G.: Endogenous miRNA sponges mediate the generation of oscillatory dynamics for a non-coding RNA network (2019)
  2. Hui, Zhang; Yanchun, Liang; Cheng, Peng; Siyu, Han; Wei, Du; Ying, Li: Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks (2019)
  3. Xuan, Zhanwei; Feng, Xiang; Yu, Jingwen; Ping, Pengyao; Zhao, Haochen; Zhu, Xianyou; Wang, Lei: A novel method for predicting disease-associated lncRNA-mIRNA pairs based on the higher-order orthogonal iteration (2019)
  4. Zhao, Haochen; Kuang, Linai; Wang, Lei; Xuan, Zhanwei: A novel approach for predicting disease-lncRNA associations based on the distance correlation set and information of the miRNAs (2018)
  5. Zhou, Shunxian; Xuan, Zhanwei; Wang, Lei; Ping, Pengyao; Pei, Tingrui: A novel model for predicting associations between diseases and lncRNA-miRNA pairs based on a newly constructed bipartite network (2018)
  6. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  7. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Graph centrality based prediction of cancer genes (2016)
  8. Fu, Benjamin M. M.; Han, Hillary S. W.; Reidys, Christian M.: On RNA-RNA interaction structures of fixed topological genus (2015)
  9. Rodríguez-Ezpeleta, Naiara (ed.); Hackenberg, Michael (ed.); Aransay, Ana M. (ed.): Bioinformatics for high throughput sequencing (2012)
  10. Yang, Jian-Hua; Li, Jun-Hao; Shao, Peng; Zhou, Hui; Chen, Yue-Qin; Qu, Liang-Hu: Starbase: a database for exploring microrna-mrna interaction maps from argonaute CLIP-seq and degradome-seq data (2011) ioport