Insights into RNA structure and function from genome-wide studies

Mortimer, Stefanie; Kidwell, Mary Anne; Doudna, Jennifer A.

doi:10.1038/nrg3681

Cited by 415 publications

(367 citation statements)

References 101 publications

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“…The major difficulty still resides in the high false discovery rate associated with de novo predictions. However, the experimental identification of regions involved in secondary structures either using Parallel Analysis of RNA Structures (PARS), Parallel Analysis of RNA structures with Temperature Elevation (PARTE), or ds/ssRNA-seq (for review, see Mortimer et al 2014) should overcome this issue in the future. Selection on nucleotide composition in lncRNAs www.rnajournal.org 327…”

Section: Discussionmentioning

confidence: 99%

Unexpected selection to retain high GC content and splicing enhancers within exons of multiexonic lncRNA loci

Haerty

Ponting

2015

RNA

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If sequencing was possible only for genomes, and not for RNAs or proteins, then functional protein-coding exons would be recognizable by their unusual patterns of nucleotide composition, specifically a high GC content across the body of exons, and an unusual nucleotide content near their edges. RNAs and proteins can, of course, be sequenced but the extent of functionality of intergenic long noncoding RNAs (lncRNAs) remains under question owing to their low nucleotide conservation. Inspired by the nucleotide composition patterns of protein-coding exons, we sought evidence for functionality across lncRNA loci from diverse species. We found that such patterns across multiexonic lncRNA loci mirror those of proteincoding genes, although to a lesser degree: Specifically, compared with introns, lncRNA exons are GC rich. Additionally we report evidence for the action of purifying selection to preserve exonic splicing enhancers within human multiexonic lncRNAs and nucleotide composition in fruit fly lncRNAs. Our findings provide evidence for selection for more efficient rates of transcription and splicing within lncRNA loci. Despite only a minor proportion of their RNA bases being constrained, multiexonic intergenic lncRNAs appear to require accurate splicing of their exons to transact their function.

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Section: Discussionmentioning

confidence: 99%

Unexpected selection to retain high GC content and splicing enhancers within exons of multiexonic lncRNA loci

Haerty

Ponting

2015

RNA

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“…Similar to proteins, RNA performs this wide variety of functions by folding into diverse base-pairing patterns (secondary) and three-dimensional (tertiary) structures (Wan et al 2011;Mortimer et al 2014). The flexible nature of RNA, however, makes it difficult to determine RNA tertiary structures experimentally by either X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

“…Such genome-scale structural information significantly advances our understanding of RNA structure-function relations (Wan et al 2011;Mortimer et al 2014), because RNA secondary structure and accessibility can be explored in vivo. In contrast, RNA structures determined by in vitro methods such as NMR and X-ray crystallography may or may not reflect the functional state of RNA in cells (Blount and Uhlenbeck 2005;Yajima et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction

Yang

Zhao

et al. 2016

RNA

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As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5 ′ cap of 5 ′ and 3 ′ cap of 3 ′ untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org.

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“…RNA plays a central role in various cellular functions, such as protein synthesis and gene regulation (Sharp 2009;Mortimer et al 2014). It is widely accepted that the structure of an RNA is essential to its functionality, underlining the importance of deciphering structure.…”

Section: Introductionmentioning

confidence: 99%

Data-directed RNA secondary structure prediction using probabilistic modeling

Deng

Ledda

Vaziri

et al. 2016

RNA

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Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.

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Insights into RNA structure and function from genome-wide studies

Cited by 415 publications

References 101 publications

Unexpected selection to retain high GC content and splicing enhancers within exons of multiexonic lncRNA loci

Unexpected selection to retain high GC content and splicing enhancers within exons of multiexonic lncRNA loci

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction

Data-directed RNA secondary structure prediction using probabilistic modeling

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