New regulatory roles continue to emerge for both natural and engineered noncoding RNAs, many of which have specific secondary and tertiary structures essential to their function. Thus there is a growing need to develop technologies that enable rapid characterization of structural features within complex RNA populations. We have developed a high-throughput technique, SHAPE-Seq, that can simultaneously measure quantitative, single nucleotide-resolution secondary and tertiary structural information for hundreds of RNA molecules of arbitrary sequence. SHAPE-Seq combines selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) chemistry with multiplexed paired-end deep sequencing of primer extension products. This generates millions of sequencing reads, which are then analyzed using a fully automated data analysis pipeline, based on a rigorous maximum likelihood model of the SHAPE-Seq experiment. We demonstrate the ability of SHAPESeq to accurately infer secondary and tertiary structural information, detect subtle conformational changes due to single nucleotide point mutations, and simultaneously measure the structures of a complex pool of different RNA molecules. SHAPE-Seq thus represents a powerful step toward making the study of RNA secondary and tertiary structures high throughput and accessible to a wide array of scientific pursuits, from fundamental biological investigations to engineering RNA for synthetic biological systems.chemical probing | RNA sequencing | RNA folding | genomics O ver the past several years, there has been an explosion in the discovery of noncoding, but functional RNAs that play central roles in maintaining, regulating, and defending the genome (1). At the same time, RNA-based mechanisms have emerged as powerful tools for engineering synthetic biological systems (2). Many of these natural and synthetic RNAs have specific secondary and tertiary structures essential to their function, and there is a growing need to develop technologies that enable rapid characterization of structural features within complex RNA populations. Such a high-throughput structure characterization assay would allow rapid assessment of the impact of sequence on structure and function and enable RNA engineers to design libraries of RNA molecules with desired structural properties.Two techniques for high-throughput RNA structure characterization have recently been reported: parallel analysis of RNA structures (PARS) (3) and fragmentation sequencing (FragSeq) (4). Both techniques couple classic in vitro nuclease probing techniques that are traditionally performed one RNA at a time, with deep sequencing of RNA fragments to simultaneously probe a complex mixture of RNAs sampled from transcriptomes. Although important first steps, these techniques provide only low-resolution secondary structure information due to the limitations inherent in nuclease probing (5).We have developed a high-throughput technique, SHAPESeq, that can simultaneously measure quantitative, single nucleotide-resolution secondary and tertia...

Sequence census methods reduce molecular measurements such as transcript abundance and protein-nucleic acid interactions to counting problems via DNA sequencing. We focus on a novel assay utilizing this approach, called selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq), that can be used to characterize RNA secondary and tertiary structure. We describe a fully automated data analysis pipeline for SHAPE-Seq analysis that includes read processing, mapping, and structural inference based on a model of the experiment. Our methods rely on the solution of a series of convex optimization problems for which we develop efficient and effective numerical algorithms. Our results can be easily extended to other chemical probes of RNA structure, and also generalized to modeling polymerase drop-off in other sequence census-based experiments. O ver the past 30 years, techniques have been developed that probe RNA structures with small molecules. In this class of techniques, a chemical reagent modifies RNA molecules in a structure-dependent fashion. Depending on the reagent used, four distinct types of information can be gleaned, including spatial nucleotide contact information, solvent accessibility of the RNA backbone, the local electrostatic environment adjacent to each nucleotide, and the local nucleotide flexibility (1). In each of these techniques, the modification location is detected during conversion to cDNA by blockage of reverse transcriptase at the modification site (Fig. 1). The detection can be performed by direct sequencing of the cDNA fragments using high-throughput sequencing technology (2). However, because at most a single modified site is revealed by every sequenced fragment (the closest modification to the 3′ end), a mathematical model and inference framework are needed to accurately infer the underlying structural properties given the observed fragment distribution.In this work, we introduce such a model and framework in the context of the SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) technique for characterizing local nucleotide flexibility (3-5). The identification of adduct formation can be performed by capillary electrophoresis (SHAPE-CE) or by high-throughput sequencing of cDNA fragments (SHAPESeq) (2) (Fig. 1). Every fragment begins at the 3′ end of the molecule and terminates at some adduct [(+) channel], or possibly at a location where there was natural polymerase drop-off (6, 7), which is controlled for in a separate control experiment [(−) channel]. Following sequencing, reads are mapped back to the RNA sequence and are classified by their end location. The resulting read counts are the sufficient statistics for a model that is used to infer estimates of the probabilities of adduct formation at each nucleotide, called relative reactivities.The probabilistic model we develop for SHAPE and the sequencing that follows in SHAPE-Seq is highly structured and has recursive properties that allow for efficient maximum-likelihood inference and confidenc...

Knowledge of RNA structure is critical to understanding both the important functional roles of RNA in biology and the engineering of RNA to control biological systems. This article contains a protocol for selective 2′‐hydroxyl acylation analyzed by primer extension and sequencing (SHAPE‐Seq) that, through a combination of structure‐dependent chemical probing and next‐generation sequencing technologies, achieves structural characterization of hundreds of RNAs in a single experiment. This protocol is applicable in a variety of conditions, and represents an important tool for understanding RNA biology. The protocol includes methods for the design and synthesis of RNA mixtures for study, and the construction and analysis of structure‐dependent sequencing libraries that reveal structural information of the RNAs in the mixtures. The methods are generally applicable to studying RNA structure and interactions in vitro in a variety of conditions, and allows for the rapid characterization of RNA structures in a high‐throughput manner. Curr. Protoc. Chem. Biol. 4:275‐297 © 2012 by John Wiley & Sons, Inc.

Abstract-Despite great interest in solving RNA secondary structures due to their impact on function, it remains an open problem to determine structure from sequence. Among experimental approaches, a promising candidate is the "chemical modification strategy", which involves application of chemicals to RNA that are sensitive to structure and that result in modifications that can be assayed via sequencing technologies. One approach that can reveal paired nucleotides via chemical modification followed by sequencing is SHAPE, and it has been used in conjunction with capillary electrophoresis (SHAPE-CE) and high-throughput sequencing (SHAPE-Seq). The solution of mathematical inverse problems is needed to relate the sequence data to the modified sites, and a number of approaches have been previously suggested for SHAPE-CE, and separately for SHAPE-Seq analysis.Here we introduce a new model for inference of chemical modification experiments, whose formulation results in closedform maximum likelihood estimates that can be easily applied to data. The model can be specialized to both SHAPE-CE and SHAPE-Seq, and therefore allows for a direct comparison of the two technologies. We then show that the extra information obtained with SHAPE-Seq but not with SHAPE-CE is valuable with respect to ML estimation.

Background Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data. Results We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy. Conclusions To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.

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.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions.

Structure mapping is a classic experimental approach for determining nucleic acid structure that has gained renewed interest in recent years following advances in chemistry, genomics, and informatics. The approach encompasses numerous techniques that use different means to introduce nucleotide-level modifications in a structure-dependent manner. Modifications are assayed via cDNA fragment analysis, using electrophoresis or next-generation sequencing (NGS). The recent advent of NGS has dramatically increased the throughput, multiplexing capacity, and scope of RNA structure mapping assays, thereby opening new possibilities for genome-scale, de novo, and in vivo studies. From an informatics standpoint, NGS is more informative than prior technologies by virtue of delivering direct molecular measurements in the form of digital sequence counts. Motivated by these new capabilities, we introduce a novel model-based in silico approach for quantitative design of large-scale multiplexed NGS structure mapping assays, which takes advantage of the direct and digital nature of NGS readouts. We use it to characterize the relationship between controllable experimental parameters and the precision of mapping measurements. Our results highlight the complexity of these dependencies and shed light on relevant tradeoffs and pitfalls, which can be difficult to discern by intuition alone. We demonstrate our approach by quantitatively assessing the robustness of SHAPE-Seq measurements, obtained by multiplexing SHAPE (selective 2 ′ -hydroxyl acylation analyzed by primer extension) chemistry in conjunction with NGS. We then utilize it to elucidate design considerations in advanced genome-wide approaches for probing the transcriptome, which recently obtained in vivo information using dimethyl sulfate (DMS) chemistry.