starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data.
starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data.
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starBase v2.0:从大规模 CLIP-Seq 数据中解码 miRNA-ceRNA、miRNA-ncRNA 和蛋白质-RNA 相互作用网络
DOI:
10.1093/nar/gkt1248
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发表时间:
2014-01
影响因子:
14.9
通讯作者:
Yang JH
中科院分区:
文献类型:
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作者:
Li JH;Liu S;Zhou H;Qu LH;Yang JH
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.
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影响因子:
5.8
作者:
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者:
Mesirov, Jill P.
影响因子:
14.9
作者:
Kanehisa M;Goto S;Sato Y;Furumichi M;Tanabe M
通讯作者:
Tanabe M
影响因子:
14.9
作者:
Anders G;Mackowiak SD;Jens M;Maaskola J;Kuntzagk A;Rajewsky N;Landthaler M;Dieterich C
通讯作者:
Dieterich C
影响因子:
14.9
作者:
Kozomara A;Griffiths-Jones S
通讯作者:
Griffiths-Jones S
影响因子:
64.8
作者:
通讯作者:
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