Footprinting SHAPE-eCLIP Reveals Transcriptome-wide Hydrogen Bonds at RNA-Protein Interfaces

Corley, Meredith; Flynn, Ryan A.; Lee, Byron; Blue, Steven M.; Chang, Howard Y.; Yeo, G

doi:10.1016/j.molcel.2020.11.014

Cited by 27 publications

(35 citation statements)

References 73 publications

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“…Due to the plethora of cellular processes affected by RNA structures, there are numerous contexts in which summarizing local structure is important. To name a few examples, one might wish to find structural domains and druggable pockets in viral genomes (29,33,65), quantify connections between mRNA structure and gene regulation (43,48,57,(66)(67)(68), identify transcriptome-wide where RNA is differentially affected by particular stimuli (32,69), or compare structure between conditions and/or logical regions of genomes (1,30,70). The most popular approach for quantifying structuredness relies on a combination of two metrics: local reactivity and local Shannon entropy.…”

Section: Summarizing Structuredness In Rnas From Hairpin Detectionmentioning

confidence: 99%

“…These structures, as well as their ability to dynamically change between relevant configurations, are known to play central roles in almost every facet of cellular regulation (1)(2)(3)(4)(5)(6). Understanding the structures of RNA is therefore important, which has led to an explosion of methods which probe (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17), computationally predict (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28), and interpret them in various contexts (1,5,(29)(30)(31)(32)(33)(34)(35).…”

Section: Introductionmentioning

confidence: 99%

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Rapid Structure-Function Insights via Hairpin-Centric Analysis of Big RNA Structure Probing Datasets

Radecki

Uppuluri

Aviran

2021

Preprint

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The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g., transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens, and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

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Section: Summarizing Structuredness In Rnas From Hairpin Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid Structure-Function Insights via Hairpin-Centric Analysis of Big RNA Structure Probing Datasets

Radecki

Uppuluri

Aviran

2021

Preprint

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“…Corley et al recently harnessed structure probing to detect RBP binding sites in an experiment called fSHAPE and applied it transcriptome-wide to human cell lines (34). The result of their work is a large set of data encompassing in vivo and in vitro icSHAPE reactivities and fSHAPE scores, the latter of which capture differential reactivity in the presence and absence of RBPs.…”

Section: Mining Structurome Data Reveals Strong Association Between Stem-loops and Rbp Binding Signalsmentioning

confidence: 99%

“…This has warranted the development of methods designed to accommodate the growing scale of SP data in deciphering the extensive regulatory functions of mRNA structures. Such methods are useful when seeking to inspect and quantify biologically relevant changes in the structurome-for instance, to detect structural changes between different cellular conditions (27)(28)(29)(30), inspect the structural context of relevant regions, such as splice sites, miRNA targets, or alternative polyadenylation sites (31)(32)(33), profile the degree of structure across different types of mRNA (33), or explore the interplay between the structurome and RNA-protein interactome (34).…”

Section: Introductionmentioning

confidence: 99%

“…Although underpinned by simulated data, we find this resource to be more effective at training datadriven classifiers than smaller sets of real data and believe it can serve as a useful resource for machine learning method development. Moreover, we apply the classifier to an integrative transcriptomic dataset on human cells that quantifies both structure and RNA binding protein (RBP) interactions (34). We demonstrate that stable stem-loops are almost always associated with evidence of RBP binding, and that this association exists across a diverse set of stem-loop configurations.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Detection of RNA Stem-Loops in Structurome Data Reveals Widespread Association with Protein Binding Sites

Radecki

Uppuluri

Deshpande

et al. 2021

Preprint

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RNA molecules are known to fold into specific structures which often play a central role in their functions and regulation. In silico folding of RNA transcripts, especially when assisted with structure profiling (SP) data, is capable of accurately elucidating relevant structural conformations. However, such methods scale poorly to the swaths of SP data generated by transcriptome-wide experiments, which are becoming more commonplace and advancing our understanding of RNA structure and its regulation at global and local levels. This has created a need for tools capable of rapidly deriving structural assessments from SP data in a scalable manner. One such tool we previously introduced that aims to process such data is patteRNA, a statistical learning algorithm capable of rapidly mining big SP datasets for structural elements. Here, we present a reformulation of its pattern recognition scheme that sees significantly improved precision without major compromises to computational overhead. Specifically, we developed a data-driven logistic classifier which interprets its statistical characterizations of SP data in addition to local sequence properties as measured with a nearest neighbor thermodynamic model. Application of the classifier to human structurome data reveals a marked association between detected stem-loops and RNA binding protein (RBP) footprints. The results of our application demonstrate that upwards of 30% of RBP footprints occur within loops of stable stem-loop elements. Overall, our work arrives at a rapid and accurate method for automatically detecting families of RNA structure motifs and demonstrates the functional relevance of identifying them transcriptome-wide.

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Structure–function relationship of long noncoding RNAs: Advances and challenges

Longkumer

Mishra

2022

WIREs Comput Mol Sci

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Long noncoding RNAs (lncRNAs) are an emerging and a promising class of RNAs, and the lncRNA field is an intense research area. Once trashed as the junk regions of the genome, lncRNAs have now proved to be one of the crucial elements of a functional genome. These comprise a major chunk of the transcriptome, and similar to proteins, the sequence–structure–function paradigm holds true for lncRNAs as well. While some of the earliest lncRNAs like Xist and H19 have been well‐characterized, many of the emerging lncRNAs remain in oblivion. The low sequence conservation of lncRNAs has prompted researchers to decipher its conserved structure in order to gain an insight into the functional mechanisms. Here, we explore the concept of the sequence–structure–function relationship of lncRNAs, and the biophysical and biochemical laws governing a lncRNA structure which are just beginning to be understood. Proceeding from specific structures, much of the functions of lncRNAs revolve around their regulatory roles, through myriad modes of action. Throughout this review, we discuss the powerful computational as well as some experimental approaches that are applied in a synergistic fashion and highlight promising studies that have proved crucial towards an understanding of lncRNA structure and functional mechanisms. We also discuss at length, the existing challenges and the possible strategies to circumvent it. Given the unknown realm, the patterns and insights generated from these studies will be extremely useful in deciphering the way nature selects and uses a specific lncRNA to regulate a specific gene or gene sets in health and disease. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Methods Structure and Mechanism > Molecular Structures

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Footprinting SHAPE-eCLIP Reveals Transcriptome-wide Hydrogen Bonds at RNA-Protein Interfaces

Cited by 27 publications

References 73 publications

Rapid Structure-Function Insights via Hairpin-Centric Analysis of Big RNA Structure Probing Datasets

Rapid Structure-Function Insights via Hairpin-Centric Analysis of Big RNA Structure Probing Datasets

Accurate Detection of RNA Stem-Loops in Structurome Data Reveals Widespread Association with Protein Binding Sites

Structure–function relationship of long noncoding RNAs: Advances and challenges

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