SHAPE Selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data.
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ABSTRACT:
SUBMITTER: Poulsen LD
PROVIDER: S-EPMC4408784 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:RNA molecules fold into complex three-dimensional shapes, guided by the pattern of hydrogen bonding between nucleotides. This pattern of base pairing, known as RNA secondary structure, is critical to their cellular function. Recently several diverse methods have been developed to assay RNA secondary structure on a transcriptome-wide scale using high-throughput sequencing. Each approach has its own strengths and caveats, however there is no widely available tool for visualizing and comparing the results from these varied methods.To address this, we have developed Structure Surfer, a database and visualization tool for inspecting RNA secondary structure in six transcriptome-wide data sets from human and mouse ( http://tesla.pcbi.upenn.edu/strucuturesurfer/ ). The data sets were generated using four different high-throughput sequencing based methods. Each one was analyzed with a scoring pipeline specific to its experimental design. Users of Structure Surfer have the ability to query individual loci as well as detect trends across multiple sites.Here, we describe the included data sets and their differences. We illustrate the database's function by examining known structural elements and we explore example use cases in which combined data is used to detect structural trends.In total, Structure Surfer provides an easy-to-use database and visualization interface for allowing users to interrogate the currently available transcriptome-wide RNA secondary structure information for mammals.
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Project description:RNA structure is a primary determinant of its function, and methods that merge chemical probing with next generation sequencing have created breakthroughs in the throughput and scale of RNA structure characterization. However, little work has been done to examine the effects of library preparation and sequencing on the measured chemical probe reactivities that encode RNA structural information. Here, we present the first analysis and optimization of these effects for selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). We first optimize SHAPE-Seq, and show that it provides highly reproducible reactivity data over a wide range of RNA structural contexts with no apparent biases. As part of this optimization, we present SHAPE-Seq v2.0, a 'universal' method that can obtain reactivity information for every nucleotide of an RNA without having to use or introduce a specific reverse transcriptase priming site within the RNA. We show that SHAPE-Seq v2.0 is highly reproducible, with reactivity data that can be used as constraints in RNA folding algorithms to predict structures on par with those generated using data from other SHAPE methods. We anticipate SHAPE-Seq v2.0 to be broadly applicable to understanding the RNA sequence-structure relationship at the heart of some of life's most fundamental processes.
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Project description:Classical approaches to determine structures of noncoding RNA (ncRNA) probed only one RNA at a time with enzymes and chemicals, using gel electrophoresis to identify reactive positions. To accelerate RNA structure inference, we developed fragmentation sequencing (FragSeq), a high-throughput RNA structure probing method that uses high-throughput RNA sequencing of fragments generated by digestion with nuclease P1, which specifically cleaves single-stranded nucleic acids. In experiments probing the entire mouse nuclear transcriptome, we accurately and simultaneously mapped single-stranded RNA regions in multiple ncRNAs with known structure. We probed in two cell types to verify reproducibility. We also identified and experimentally validated structured regions in ncRNAs with, to our knowledge, no previously reported probing data.
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Project description:Cultural evolution relies on the social transmission of cultural traits along a population's social network. Research indicates that network structure affects information spread and thus the capacity for cumulative culture. However, how network structure itself is driven by population-culture co-evolution remains largely unclear. We use a simple model to investigate how populations negotiate the trade-off between acquiring new skills and getting better at existing skills and how this trade-off shapes social networks. We find unexpected eco-evolutionary feedbacks from culture onto social networks and vice versa. We show that selecting for skill generalists results in sparse networks with diverse skill sets, whereas selecting for skill specialists results in dense networks and a population that specializes on the same few skills on which everyone is an expert. Our model advances our understanding of the complex feedbacks in cultural evolution and demonstrates how individual-level behavior can lead to the emergence of population-level structure.
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Project description:Chemical probing is an important tool for characterizing the complex folded structures of RNA molecules, many of which play key cellular roles. Electrophilic SHAPE reagents create adducts at the 2'-hydroxyl position on the RNA backbone of flexible ribonucleotides with relatively little dependence on nucleotide identity. Strategies for adduct detection such as mutational profiling (MaP) allow accurate, automated calculation of relative adduct frequencies for each nucleotide in a given RNA or group of RNAs. A number of alternative reagents and adduct detection strategies have been proposed, especially for use in living cells. Here we evaluate five SHAPE reagents: three previously well-validated reagents 1M7 (1-methyl-7-nitroisatoic anhydride), 1M6 (1-methyl-6-nitroisatoic anhydride), and NMIA ( N-methylisatoic anhydride), one more recently proposed NAI (2-methylnicotinic acid imidazolide), and one novel reagent 5NIA (5-nitroisatoic anhydride). We clarify the importance of carefully designed software in reading out SHAPE experiments using massively parallel sequencing approaches. We examine SHAPE modification in living cells in diverse cell lines, compare MaP and reverse transcription-truncation as SHAPE adduct detection strategies, make recommendations for SHAPE reagent choice, and outline areas for future development.
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Project description:SummaryExperimental structure probing data has been shown to improve thermodynamics-based RNA secondary structure prediction. To this end, chemical reactivity information (as provided e.g. by SHAPE) is incorporated, which encodes whether or not individual nucleotides are involved in intra-molecular structure. Since inter-molecular RNA-RNA interactions are often confined to unpaired RNA regions, SHAPE data is even more promising to improve interaction prediction. Here, we show how such experimental data can be incorporated seamlessly into accessibility-based RNA-RNA interaction prediction approaches, as implemented in IntaRNA. This is possible via the computation and use of unpaired probabilities that incorporate the structure probing information. We show that experimental SHAPE data can significantly improve RNA-RNA interaction prediction. We evaluate our approach by investigating interactions of a spliceosomal U1 snRNA transcript with its target splice sites. When SHAPE data is incorporated, known target sites are predicted with increased precision and specificity.Availability and implementationhttps://github.com/BackofenLab/IntaRNA.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6691327 | biostudies-literature
Project description:To accelerate previous RNA structure probing approaches, which focus on analyzing one RNA sequence at a time, we have developed FragSeq, a high-throughput RNA structure probing method that uses high-throughput RNA sequencing to identify single-stranded RNA (ssRNA) regions from fragments generated by nuclease P1, which is specific for single-stranded nucleic acids. In the accompanying study, we show that we can accurately and simultaneously map ssRNA regions in multiple non-coding RNAs with known structure in experiments probing the entire mouse nuclear transcriptome. We carried out probing in two cell types to assess reproducibility. We also identified and experimentally validated structured regions in ncRNAs never previously probed. We examined mouse nuclear RNA from two cell types: undifferentiated embryonic stem cells (UNDIFF) and cells differentiated into neural precursors (D5NP). For each cell type, nuclear RNA was purified and deproteinized, denatured, and refolded in vitro, from which we prepared three barcoded samples: "nuclease" (RNA partially digested with P1 ssRNA-specific nuclease, yielding 5'-PO4/3'-OH end chemistry at each cleavage site), "control" (control for "nuclease" sample to idenfity endogenous 5'-PO4/3'-OH), and "PNK" (same as "control" but followed by a polynucleotide kinase treatment to convert 5'-OH/3'-cyclic-phosphate ends to clonable 5'-PO4/3'-OH ends). Resulting RNA fragments were cloned using the SOLiD Small RNA Expression Kit (SREK) protocol, which ligates linkers only to 5'-PO4/3'-OH containing RNA, enriching for clones of products resulting from P1 cleavage in "nuclease" sample and selecting against random degradation. Two cell types, three treatments each, thus resulted in six barcoded samples total (barcodes 01, 02, 04, 05, 07, 08). Four other barcoded samples were prepared for separate experiments not used in our study (barcodes 03, 06, 09, 10), so their preparation is not described here. The total run of ten barcodes was done on the ABI SOLiD3 platform and a custom algorithm (FragSeq v0.0.1) was used to compute "cutting scores" (as described in our paper) that show ssRNA regions in hundreds of ncRNAs.
2010-11-07 | E-GEOD-24622 | biostudies-arrayexpress
Project description:Most RNA molecules form internal base pairs, leading to a folded secondary structure. Some of these structures have been demonstrated to be functionally significant. High-throughput RNA structure chemical probing methods generate millions of sequencing reads to provide structural constraints for RNA secondary structure prediction. At present, processed data from these experiments are difficult to access without computational expertise. Here we present FoldAtlas, a web interface for accessing raw and processed structural data across thousands of transcripts. FoldAtlas allows a researcher to easily locate, view, and retrieve probing data for a given RNA molecule. We also provide in silico and in vivo secondary structure predictions for comparison, visualized in the browser as circle plots and topology diagrams. Data currently integrated into FoldAtlas are from a new high-depth Structure-seq data analysis in Arabidopsis thaliana, released with this work. AVAILABILITY AND IMPLEMENTATION:The FoldAtlas website can be accessed at www.foldatlas.com Source code is freely available at github.com/mnori/foldatlas under the MIT license. Raw reads data are available under the NCBI SRA accession SRP066985. CONTACT:yiliang.ding@jic.ac.uk or matthew.norris@jic.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
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Project description:The functions of RNA molecules are intimately linked to their ability to fold into complex secondary and tertiary structures. Thus, understanding how these molecules fold is essential to determining how they function. Current methods for investigating RNA structure often use small molecules, enzymes, or ions that cleave or modify the RNA in a solvent-accessible manner. While these methods have been invaluable to understanding RNA structure, they can be fairly labor intensive and often focus on short regions of single RNAs. Here we present a new method (Mod-seq) and data analysis pipeline (Mod-seeker) for assaying the structure of RNAs by high-throughput sequencing. This technique can be utilized both in vivo and in vitro, with any small molecule that modifies RNA and consequently impedes reverse transcriptase. As proof-of-principle, we used dimethyl sulfate (DMS) to probe the in vivo structure of total cellular RNAs in Saccharomyces cerevisiae. Mod-seq analysis simultaneously revealed secondary structural information for all four ribosomal RNAs and 32 additional noncoding RNAs. We further show that Mod-seq can be used to detect structural changes in 5.8S and 25S rRNAs in the absence of ribosomal protein L26, correctly identifying its binding site on the ribosome. While this method is applicable to RNAs of any length, its high-throughput nature makes Mod-seq ideal for studying long RNAs and complex RNA mixtures.
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Project description:RNA molecules fold into complex structures that are important across many biological processes. Recent technological developments have enabled transcriptome-wide probing of RNA secondary structure using nucleases and chemical modifiers. These approaches have been widely applied to capture RNA secondary structure in many studies, but gathering and presenting such data from very different technologies in a comprehensive and accessible way has been challenging. Existing RNA structure probing databases usually focus on low-throughput or very specific datasets. Here, we present a comprehensive RNA structure probing database called RASP (RNA Atlas of Structure Probing) by collecting 161 deduplicated transcriptome-wide RNA secondary structure probing datasets from 38 papers. RASP covers 18 species across animals, plants, bacteria, fungi, and also viruses, and categorizes 18 experimental methods including DMS-seq, SHAPE-Seq, SHAPE-MaP, and icSHAPE, etc. Specially, RASP curates the up-to-date datasets of several RNA secondary structure probing studies for the RNA genome of SARS-CoV-2, the RNA virus that caused the on-going COVID-19 pandemic. RASP also provides a user-friendly interface to query, browse, and visualize RNA structure profiles, offering a shortcut to accessing RNA secondary structures grounded in experimental data. The database is freely available at http://rasp.zhanglab.net.
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