RAID v2.0: an updated resource of RNA-associated interactions across organisms

Ying, Yibin; Zhao, Yue; Li, Chunhua; Zhang, Lin; Huang, Huiying; Li, Yana; Liu, Lanlan; Hou, Ping; Cui, Tianyu; Tan, Puwen; Hu, Yongfei; Zhang, Ting; Huang, Yan; Li, Xiaobo; Yu, Jia; Wang, Dong

doi:10.1093/nar/gkw1052

Cited by 194 publications

(132 citation statements)

References 29 publications

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“…Other resources have started to collect RNA interactions Hao et al 2016;Junge et al 2017;Yi et al 2017). However, new interactions are continuously published for yeast and mammalian mRNAs (Baltz et al 2012;Baejen et al 2014;Farazi et al 2014;Gerstberger et al 2014;Castello et al 2016b) and for less characterized classes of ncRNAs such as SUT, CUT, and XUT (Tuck and Tollervey 2013).…”

Section: Discussionmentioning

confidence: 99%

“…However, unlike proteinprotein interaction data, no systematic manual curation of the published literature in this area has yet been undertaken. Recently, a few resources have gathered RNA interactions and made them available on the web, such as Clipdb (Yang et al 2015), NPinter (Hao et al 2016), RAIN (Junge et al 2017), and RAID (Yi et al 2017). An important issue that previously made it difficult for interaction databases to capture and organize ncRNA interactions was the lack of a single central resource providing unambiguous identifiers for each RNA molecule, and linking that identifier to the sequence of that RNA.…”

Section: Introductionmentioning

confidence: 99%

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The yeast noncoding RNA interaction network

Panni

Prakash

Bateman

et al. 2017

RNA

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This article describes the creation of the first expert manually curated noncoding RNA interaction networks for S. cerevisiae. The RNA-RNA and RNA-protein interaction networks have been carefully extracted from the experimental literature and made available through the IntAct database (www.ebi.ac.uk/intact). We provide an initial network analysis and compare their properties to the much larger protein-protein interaction network. We find that the proteins that bind to ncRNAs in the network contain only a small proportion of classical RNA binding domains. We also see an enrichment of WD40 domains suggesting their direct involvement in ncRNA interactions. We discuss the challenges in collecting noncoding RNA interaction data and the opportunities for worldwide collaboration to fill the unmet need for this data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The yeast noncoding RNA interaction network

Panni

Prakash

Bateman

et al. 2017

RNA

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“…NPInter [96], RAID [97] and RAIN [98] collected both experimental and predicted data of RNA-associated interactions. However, RRIs aggregated in these resources are largely miRNA related; and computational predicted interactions contribute most of the data.…”

Section: Discussionmentioning

confidence: 99%

Advances and challenges towards the study of RNA-RNA interactions in a transcriptome-wide scale

Gong

Шао

et al. 2018

Quant Biol

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Background: RNA molecules play crucial roles in various biological processes. Their regulation and function are mediated by interacting with other molecules. Among them RNA-RNA interactions (RRIs) are important in many basic cellular activities including transcription, RNA processing, localization, and translation. However, we just start to unveil the complexity of the knowledge and underlying mechanisms of RRIs. Results: In this review, we will summarize approaches for RRI identifications, including both conventional, focused biophysical and biochemical methods and recently developed large scale sequencing-based techniques. We will also discuss discoveries per RRI type revealed by using these technologies, as well as challenges towards a systematic and functional understanding of RRIs. Conclusions: The development of sequencing-based techniques has revolutionized the study of RRIs. Applying these techniques in multiple organisms has identified thousands of RRIs, many of which could potentially regulate multiple aspects of gene expression. However, despite the great breakthrough, the RNA-RNA interactome of any species remains far from complete due to intrinsic complex nature of RRI and limitations in current techniques. More efficient experimental methods and computational framework are needed to obtain the full image of RRI networks, and their possible regulatory roles in biology and medicine.

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“…We exercised P value cutoff of ≤0.05 to identify significant pairs of lncRNA‐TFMs. These lncRNA‐TFM pairs were mapped to ncRNA‐protein interacting pairs obtained from RAIDv2.0 that contains RNA‐associated interactions including lncRNA‐protein interactions from experimental and computational predictions. From this, we obtained lncRNA‐TFM pairs in which lncRNA physically interacts with TF and coexpressed with the significant number of downstream targets of TFM.…”

Section: Methodsmentioning

confidence: 99%

Modulation of specific cell cycle phases in human embryonic stem cells by lncRNA RNA decoys

Sahu

Mallick

2018

J of Molecular Recognition

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Recent studies have shown that long noncoding RNAs (lncRNAs) are crucial regulators of human embryonic stem cells (hESCs). However, modes of actions of lncRNAs in hESCs are not well illustrated. Here, we predicted a regulatory network in hESCs in which lncRNAs interact with TFs and thereby control the expressions of downstream targets of TFs. The predicted network is comprised of 2289 3-motif subgraphs which are characterized by 3 nodes: (i) a lncRNA which is predicted to interact with (ii) a TF and (iii) a gene which is a target of TF and coexpressing with lncRNA. We performed functional annotation of the network by identifying hub nodes followed by pathway enrichment study, which unveiled an active G1-S cell cycle phase transition-specific subnetwork that encompasses 2 lncRNAs, MALAT1 and DANCR. Our analysis revealed that MALAT1 and DANCR might be playing key roles in G1-S phase transition by acting as RNA decoy via interacting with crucial stemness maintaining TFs. We predicted that MALAT1 possibly compete with DNMT1 and CDCA7 genes to bind to E2F1 thereby interrupting repression of DNMT1 and activation of CDCA7 by E2F1 in hESCs, whereas DANCR possibly competes with IPO7 gene to bind to MYC thereby interrupting MYC-mediated activation of IPO7 in hESCs. Both of these are conjectured to contribute to rapid G1-S phase transition aiding in stemness maintenance of hESCs. This study presents a crucial TF target cross talks mediated by lncRNAs in hESCs regulating its properties which needs further investigation.

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RAID v2.0: an updated resource of RNA-associated interactions across organisms

Cited by 194 publications

References 29 publications

The yeast noncoding RNA interaction network

The yeast noncoding RNA interaction network

Advances and challenges towards the study of RNA-RNA interactions in a transcriptome-wide scale

Modulation of specific cell cycle phases in human embryonic stem cells by lncRNA RNA decoys

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