Understanding the transcriptome through RNA structure

Wan, Yao; Kertesz, Michael A.; Spitale, Robert C.; Segal, Eran; Chang, Howard Y.

doi:10.1038/nrg3049

Cited by 427 publications

(359 citation statements)

References 149 publications

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“…Even if a trans-acting factor specifi cally binds to a short sequence motif, stable structures in the RNA could prevent its access. In addition to computational prediction, the recent development of new experimental techniques to probe RNA structures, for instance by high-throughput sequencing, will yield insights into the structural organization of the eukaryotic transcriptome -the ' RNA structurome ' (80) .…”

Section: Modularity -Is There An Rna Code ?mentioning

confidence: 99%

RNA regulons and the RNA-protein interaction network

Imig¹,

Kanitz²,

Gerber

2012

BioMolecular Concepts

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The development of genome-wide analysis tools has prompted global investigation of the gene expression program, revealing highly coordinated control mechanisms that ensure proper spatiotemporal activity of a cell ' s macromolecular components. With respect to the regulation of RNA transcripts, the concept of RNA regulons, which -by analogy with DNA regulons in bacteria -refers to the coordinated control of functionally related RNA molecules, has emerged as a unifying theory that describes the logic of regulatory RNA-protein interactions in eukaryotes. Hundreds of RNA-binding proteins and small non-coding RNAs, such as microRNAs, bind to distinct elements in target RNAs, thereby exerting specifi c and concerted control over posttranscriptional events. In this review, we discuss recent reports committed to systematically explore the RNA-protein interaction network and outline some of the principles and recurring features of RNA regulons: the coordination of functionally related mRNAs through RNAbinding proteins or non-coding RNAs, the modular structure of its components, and the dynamic rewiring of RNA-protein interactions upon exposure to internal or external stimuli. We also summarize evidence for robust combinatorial control of mRNAs, which could determine the ultimate fate of each mRNA molecule in a cell. Finally, the compilation and integration of global protein-RNA interaction data has yielded fi rst insights into network structures and provided the hypothesis that RNA regulons may, in part, constitute noise ' buffers ' to handle stochasticity in cellular transcription.

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Section: Modularity -Is There An Rna Code ?mentioning

confidence: 99%

RNA regulons and the RNA-protein interaction network

Imig¹,

Kanitz²,

Gerber

2012

BioMolecular Concepts

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show abstract

“…Non-coding RNA is transcribed from what was previously called 'junk' DNA and there is an amazingly diverse and continually expanding list of different RNA species being discovered. Perhaps more daunting is that single RNA molecules can also exist as structural variants with different functional roles (Wan et al, 2011). Notwithstanding the latter, the RNA-Seq platform finally gives us the opportunity to comprehensively investigate the transcriptome of the human brain (Sutherland et al, 2011a).…”

Section: Advances In Understanding Pathogenesismentioning

confidence: 99%

Alzheimer's Disease: Approaches to Pathogenesis in the Genomic Age

Sutherland¹,

Kril²

2012

Neuroscience - Dealing With Frontiers

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“…On the other hand, the recent advent of experimental techniques to probe RNA structure by highthroughput sequencing-SHAPE (Low and Weeks, 2010); PARS (Kertesz et al, 2010); FragSeq (Underwood et al, 2010)-has enabled genome-wide measurements of RNA structure, leading to the world of the ''RNA structurome'' (Wan et al, 2011). Those experimental techniques (SHAPE, PARS, and FragSeq) stochastically estimate the flexibility of an RNA strand, which can be considered as a kind of loop probability for every nucleotide in an RNA sequence.…”

Section: Introductionmentioning

confidence: 99%

Direct Updating of an RNA Base-Pairing Probability Matrix with Marginal Probability Constraints

Hamada

2012

Journal of Computational Biology

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A base-pairing probability matrix (BPPM) stores the probabilities for every possible base pair in an RNA sequence and has been used in many algorithms in RNA informatics (e.g., RNA secondary structure prediction and motif search). In this study, we propose a novel algorithm to perform iterative updates of a given BPPM, satisfying marginal probability constraints that are (approximately) given by recently developed biochemical experiments, such as SHAPE, PAR, and FragSeq. The method is easily implemented and is applicable to common models for RNA secondary structures, such as energy-based or machine-learningbased models. In this article, we focus mainly on the details of the algorithms, although preliminary computational experiments will also be presented.

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Understanding the transcriptome through RNA structure

Cited by 427 publications

References 149 publications

RNA regulons and the RNA-protein interaction network

RNA regulons and the RNA-protein interaction network

Alzheimer's Disease: Approaches to Pathogenesis in the Genomic Age

Direct Updating of an RNA Base-Pairing Probability Matrix with Marginal Probability Constraints

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