starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

By Junhao Li, Shun Liu, Hui Zhou, Liang-Hu Qu, Jianhua Yang. Nucleic Acids Res., 2013

4,991 citations recorded by the source corpus.

Abstract 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 (http://starbase.sysu.edu.cn/) 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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5298242a0b8013ab1e379bc69687895aeaf60222

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Citations in the corpus

Identification of HuR target circular RNAs uncovers suppression of PABPN1 translation by CircPABPN1 (2017, positive): “9 Recent surveys have found that RBPs can potentially bind numerous circRNAs.(21,22) Here, we identified HuR-associated circRNAs using RIP-chip and RIP-RT-qPCR analyses, bioinformatic prediction, and in vitro binding assays (Figs.”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, methodology): “With the advancement of high-throughput technologies, such as CLIP-seq, we can verify the RBP binding sites in batches [83].”
The bioinformatics toolbox for circRNA discovery and analysis (2020, neutral): “Many databases collect the circRNA ceRNA network such as starBase [131, 132], SomamiR [133, 134], LncACTdb [135], AFCMEasyModel [136] and HumanViCe [137].”
DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions (2017, background): “Other repositories, such as StarBase (15) and CLIPZ (16), substantially differ in their scope, as they provide RNAbinding protein regions from different CLIP-seq datasets.”
RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data (2017, methodology): “To investigate whether RNA modifications influence the function of RBPs or vice versa, the relationships between CLIP-Seq experimentally supported RBP binding sites (32,33) and all RNA modification sites from RMBase were constructed.”
DIANA-miRPath v3.0: deciphering microRNA function with experimental support (2015, background): “Options for advanced visualizations are presented below [11].”
CircInteractome: A web tool for exploring circular RNAs and their interacting proteins and microRNAs (2016, background): “37 Computational analyses revealed that for select RBPs there were large numbers of binding sites in circRNA sequences (Table S2); for instance, we identi ﬁ ed ~ 117,000 circRNAs that could potentially associate with the RBP EIF4A3 (Fig.”
miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis (2016, methodology): “During the past few years, several excellent web-based applications have been developed to support miRNA functional analysis based on their targets, such as DIANAmiRPath (36), starBase-miRFunction (37), miRTar (38) and miRSystem (39).”