The yeast noncoding RNA interaction network

Panni, Simona; Prakash, Ananth; Bateman, Alex; Orchard, Sandra

doi:10.1261/rna.060996.117

Cited by 24 publications

(17 citation statements)

References 73 publications

(82 reference statements)

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“…The architectures of global protein–protein interaction networks (i.e. interactomes) have been extensively studied; and a recent study investigated the network features of the RNA-RNA interactions in yeast ( 38 – 40 ). We are also interested in the structural and topological features of the RNA interactomes in RISE.…”

Section: Results and Database Usagementioning

confidence: 99%

RISE: a database of RNA interactome from sequencing experiments

Gong

Шао

et al. 2017

Nucleic Acids Research

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We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest.

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Section: Results and Database Usagementioning

confidence: 99%

RISE: a database of RNA interactome from sequencing experiments

Gong

Шао

et al. 2017

Nucleic Acids Research

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“…The Molecular Interaction (MI) workgroup of the Human Proteome Organization-Proteomics Standards Initiative (HUPO-PSI) has been working for more than 15 years to create common rules and standard formats to combine data from different resources and optimize manual curation of molecular interactions [76,77]. It is worth mentioning that recently a pilot project has been started to extend this effort to ncRNA interactions, in order to assure the quality of the annotations according to the PSI-MI standards and to allow the integration of consistently curated data [78]. This project resulted in the representation of around 900 yeast RNA-RNA interactions in the IntAct database (https://www.ebi.ac.uk/intact/) [79], a member of the HUPO PSI-MI.…”

Section: Predicted Rna-rna Networkmentioning

confidence: 99%

Non-coding RNA regulatory networks

Panni

Lovering

Porras

et al. 2020

Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanism

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284

192

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It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them are involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

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“…Studies have been carried out to collect the data and analyze them from a perspective of systems biology. It is found that, very similar to protein-protein interaction (PPI) networks [89], RRI networks are also scale-free and modular in human [88], mouse [46], and yeast [90]. One characteristic of a scale-free network is the existence of a set of nodes with relatively high interaction degrees [89,91].…”

Section: Characteristics Of Global Rri Networkmentioning

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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The yeast noncoding RNA interaction network

Cited by 24 publications

References 73 publications

RISE: a database of RNA interactome from sequencing experiments

RISE: a database of RNA interactome from sequencing experiments

Non-coding RNA regulatory networks

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

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