scholarly journals SHAPER: A Web Server for Fast and Accurate SHAPE Reactivity Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Yuanzhe Zhou ◽  
Jun Li ◽  
Travis Hurst ◽  
Shi-Jie Chen
Keyword(s):  
Rna Structure ◽  
Structural Information ◽  
3D Structure ◽  
Web Server ◽  
Rna Structures ◽  
Structure Selection ◽  
Chemical Probing ◽  
Local Flexibility ◽  
Multiple Conformations ◽  
Shape Data

Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing serves as a convenient and efficient experiment technique for providing information about RNA local flexibility. The local structural information contained in SHAPE reactivity data can be used as constraints in 2D/3D structure predictions. Here, we present SHAPE predictoR (SHAPER), a web server for fast and accurate SHAPE reactivity prediction. The main purpose of the SHAPER web server is to provide a portal that uses experimental SHAPE data to refine 2D/3D RNA structure selection. Input structures for the SHAPER server can be obtained through experimental or computational modeling. The SHAPER server can accept RNA structures with single or multiple conformations, and the predicted SHAPE profile and correlation with experimental SHAPE data (if provided) for each conformation can be freely downloaded through the web portal. The SHAPER web server is available at http://rna.physics.missouri.edu/shaper/.

scholarly journals Machine learning a model for RNA structure prediction

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Nicola Calonaci ◽  
Alisha Jones ◽  
Francesca Cuturello ◽  
Michael Sattler ◽  
Giovanni Bussi
Keyword(s):  
Free Energy ◽  
Rna Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Minimum Free Energy ◽  
Experimental Information ◽  
Rna Structures ◽  
Chemical Probing ◽  
Validation Set

Abstract RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on computing the partition function of folding ensembles, and can thus estimate minimum free-energy structures and ensemble populations. These models sometimes fail in identifying native structures unless complemented by auxiliary experimental data. Here, we build a set of models that combine thermodynamic parameters, chemical probing data (DMS and SHAPE) and co-evolutionary data (direct coupling analysis) through a network that outputs perturbations to the ensemble free energy. Perturbations are trained to increase the ensemble populations of a representative set of known native RNA structures. In the chemical probing nodes of the network, a convolutional window combines neighboring reactivities, enlightening their structural information content and the contribution of local conformational ensembles. Regularization is used to limit overfitting and improve transferability. The most transferable model is selected through a cross-validation strategy that estimates the performance of models on systems on which they are not trained. With the selected model we obtain increased ensemble populations for native structures and more accurate predictions in an independent validation set. The flexibility of the approach allows the model to be easily retrained and adapted to incorporate arbitrary experimental information.

scholarly journals RMalign: an RNA structural alignment tool based on a size independent scoring function

2018 ◽  
Author(s):  
Jinfang Zheng ◽  
Juan Xie ◽  
Xu Hong ◽  
Shiyong Liu
Keyword(s):  
Phase Transition ◽  
Rna Structure ◽  
State Of The Art ◽  
Structural Alignment ◽  
Complex Structure ◽  
3D Structure ◽  
Scoring Function ◽  
Structure Alignment ◽  
Rna Structures ◽  
Transition Points

ABSTRACTRNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein complex 3D structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases. Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.8771 and 0.7766, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structure similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Taking the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME.Author summaryRNA structures are important for RNA functions. With the increasing of RNA structures in PDB, RNA 3D structure alignment approaches have been developed. However, the scoring function which is used for measuring RNA structural similarity is still length dependent. This shortcoming limits its ability to detect RNA structure templates in modeling RNA structure or RNA-protein 3D complex structure. Thus, we developed a length independent scoring function RMscore to enhance the ability to detect RNA structure homologs. The benchmarking data shows that RMscore can distinct the similar and dissimilar RNA structure effectively. RMscore should be a useful scoring function in modeling RNA structures for the biological community. Based on RMscore, we develop an RNA 3D structure alignment RMalign. In both RNA structure and function classification benchmarking, RMalign obtains as good as or even better performance than the state-of-the-art approaches. With a length independent scoring function RMscore, RMalign should be useful for the modeling RNA structures. Based on above results, we update PRIME to PRIME2.0. We provide a more accurate RNA-protein 3D complex structure modeling tool PRIME2.0 which should be useful for the biological community.

scholarly journals RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

2022 ◽  
Vol 1 ◽  
Author(s):  
Zhi-Hao Guo ◽  
Li Yuan ◽  
Ya-Lan Tan ◽  
Ben-Gong Zhang ◽  
Ya-Zhou Shi
Keyword(s):  
Statistical Analysis ◽  
Rna Structure ◽  
Structure Prediction ◽  
Local Coordinate System ◽  
3D Structure ◽  
Scoring Function ◽  
Rna Structures ◽  
Cellular Functions ◽  
3D Structures ◽  
Structure Annotation

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

scholarly journals Estimating RNA structure chemical probing reactivities from reverse transcriptase stops and mutations

2018 ◽  
Author(s):  
Angela M Yu ◽  
Molly E. Evans ◽  
Julius B. Lucks
Keyword(s):  
Statistical Analysis ◽  
Reverse Transcriptase ◽  
Rna Structure ◽  
Rna Structures ◽  
Modeling Framework ◽  
Single Experiment ◽  
Complementary Dna ◽  
Rna Molecules ◽  
Chemical Probing ◽  
Covalent Adducts

ABSTRACTChemical probing experiments interrogate RNA structures by creating covalent adducts on RNA molecules in structure-dependent patterns. Adduct positions are then detected through conversion of the modified RNAs into complementary DNA (cDNA) by reverse transcription (RT) as either stops (RT-stops) or mutations (RT-mutations). Statistical analysis of the frequencies of RT-stops and RT-mutations can then be used to estimate a measure of chemical probing reactivity at each nucleotide of an RNA, which reveals properties of the underlying RNA structure. Inspired by recent work that showed that different reverse transcriptase enzymes show distinct biases for detecting adducts as either RT-stops or RT-mutations, here we use a statistical modeling framework to derive an equation for chemical probing reactivity using experimental signatures from both RT-stops and RT-mutations within a single experiment. The resulting formula intuitively matches the expected result from considering reactivity to be defined as the fraction of adduct observed at each position in an RNA at the end of a chemical probing experiment. We discuss assumptions and implementation of the model, as well as ways in which the model may be experimentally validated.

scholarly journals Repurposing Ribo-Seq to provide insights into structured RNAs

2020 ◽  
Author(s):  
Brayon J. Fremin ◽  
Ami S. Bhatt
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Large Scale ◽  
Structural Information ◽  
Ribosome Profiling ◽  
Rna Structures ◽  
Size Selection ◽  
Bacterial Ribosome ◽  
Powerful Approach

AbstractRibosome profiling (Ribo-Seq) is a powerful method to study translation in bacteria. However, this method can enrich RNAs that are not bound by ribosomes, but rather, are protected from degradation in another way. For example, Escherichia coli Ribo-Seq libraries also capture reads from most non-coding RNAs (ncRNAs). These fragments of ncRNAs pass all size selection steps of the Ribo-Seq protocol and survive hours of MNase treatment, presumably without protection from the ribosome or other macromolecules or proteins. Since bacterial ribosome profiling does not directly isolate ribosomes, but instead uses broad size range cutoffs to fractionate actively translated RNAs, it is understandable that some ncRNAs are retained after size selection. However, how these ‘contaminants’ survive MNase treatment is unclear. Through analyzing metaRibo-Seq reads across ssrS, a well established structured RNA in E. coli, and structured direct repeats from Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) arrays in Ruminococcus lactaris, we observed that these RNAs are protected from MNase treatment by virtue of their secondary structure. Therefore, large volumes of data previously discarded as contaminants in bacterial Ribo-Seq experiments can, in fact, be used to gain information regarding the in vivo secondary structure of ncRNAs, providing unique insight into their native functional structures.ImportanceWe observe that ‘contaminant’ signals in bacterial Ribo-Seq experiments that are often disregarded and discarded, in fact, strongly overlap with structured regions of ncRNAs. Structured ncRNAs are pivotal mediators of bioregulation in bacteria and their functions are often reliant on their specific structures. We present an approach to access important RNA structural information through merely repurposing ‘contaminant’ signals in bacterial Ribo-Seq experiments. This powerful approach enables us to partially resolve RNA structures, identify novel structured RNAs, and elucidate RNA structure-function relationships in bacteria at a large-scale and in vivo.

scholarly journals EVALUATING MIXTURE MODELS FOR BUILDING RNA KNOWLEDGE-BASED POTENTIALS

2012 ◽  
Vol 10 (02) ◽  
pp. 1241010 ◽  
Author(s):  
ADELENE Y. L. SIM ◽  
OLIVIER SCHWANDER ◽  
MICHAEL LEVITT ◽  
JULIE BERNAUER
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Rna Structure ◽  
Structural Information ◽  
A Priori ◽  
Comparable Efficacy ◽  
Rna Structures ◽  
Rna Molecules ◽  
Knowledge Based ◽  
Spline Fitting

Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedness (and hence the potential's usefulness) depends heavily on the choice of bin width to sort structural information (e.g. distances) but the appropriate bin width is not known a priori. To circumvent the binning problem, we compared knowledge-based potentials built from inter-atomic distances in RNA structures using different mixture models (Kernel Density Estimation, Expectation Minimization and Dirichlet Process). We show that the smooth knowledge-based potential built from Dirichlet process is successful in selecting native-like RNA models from different sets of structural decoys with comparable efficacy to a potential developed by spline-fitting — a commonly taken approach — to binned distance histograms. The less rugged nature of our potential suggests its applicability in diverse types of structural modeling.

scholarly journals RNAProbe: a web server for normalization and analysis of RNA structure probing data

2020 ◽  
Vol 48 (W1) ◽  
pp. W292-W299 ◽  
Author(s):  
Tomasz K Wirecki ◽  
Katarzyna Merdas ◽  
Agata Bernat ◽  
Michał J Boniecki ◽  
Janusz M Bujnicki ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Structural Characteristics ◽  
Web Server ◽  
Rna Molecules ◽  
Chemical Probing ◽  
Structure Probing ◽  
Low Pass

Abstract RNA molecules play key roles in all living cells. Knowledge of the structural characteristics of RNA molecules allows for a better understanding of the mechanisms of their action. RNA chemical probing allows us to study the susceptibility of nucleotides to chemical modification, and the information obtained can be used to guide secondary structure prediction. These experimental results can be analyzed using various computational tools, which, however, requires additional, tedious steps (e.g., further normalization of the reactivities and visualization of the results), for which there are no fully automated methods. Here, we introduce RNAProbe, a web server that facilitates normalization, analysis, and visualization of the low-pass SHAPE, DMS and CMCT probing results with the modification sites detected by capillary electrophoresis. RNAProbe automatically analyzes chemical probing output data and turns tedious manual work into a one-minute assignment. RNAProbe performs normalization based on a well-established protocol, utilizes recognized secondary structure prediction methods, and generates high-quality images with structure representations and reactivity heatmaps. It summarizes the results in the form of a spreadsheet, which can be used for comparative analyses between experiments. Results of predictions with normalized reactivities are also collected in text files, providing interoperability with bioinformatics workflows. RNAProbe is available at https://rnaprobe.genesilico.pl.

scholarly journals Computational modeling of RNA 3D structure based on experimental data

2019 ◽  
Vol 39 (2) ◽  
Author(s):  
Almudena Ponce-Salvatierra ◽  
Astha ◽  
Katarzyna Merdas ◽  
Chandran Nithin ◽  
Pritha Ghosh ◽  
...  
Keyword(s):  
Experimental Data ◽  
Computational Methods ◽  
Rna Structure ◽  
Structure Prediction ◽  
3D Structure ◽  
Rna Structures ◽  
Data Types ◽  
Rna Sequences ◽  
Rna Molecules

Abstract RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.

scholarly journals Dynamic Regulation of RNA Structure in Mammalian Cells

2018 ◽  
Author(s):  
Lei Sun ◽  
Furqan Fazal ◽  
Pan Li ◽  
James P. Broughton ◽  
Byron Lee ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
Mammalian Cells ◽  
Rna Binding ◽  
Structural Information ◽  
Rna Structures ◽  
Rna Modifications ◽  
Dynamic Regulation ◽  
Comparative Structure

RNA structure is intimately connected to each step of gene expression. Recent advances have enabled transcriptome-wide maps of RNA secondary structure, termed RNA structuromes. However, previous whole-cell analyses lacked the resolution to unravel the dynamic regulation of RNA structure across subcellular states. Here we reveal the RNA structuromes in three compartments — chromatin, nucleoplasm and cytoplasm. The cytotopic structuromes substantially expand RNA structural information, and enable detailed investigation of the central role of RNA structure in linking transcription, translation, and RNA decay. Through comparative structure analysis, we develop a resource to visualize the interplay of RNA-protein interactions, RNA chemical modifications, and RNA structure, and predict both direct and indirect reader proteins of RNA modifications. We validate the novel role of the RNA binding protein LIN28A as an N6-methyladenosine (m6A) modification “anti-reader”. Our results highlight the dynamic nature of RNA structures and its functional significance in gene regulation.

scholarly journals Best practices for genome-wide RNA structure analysis: combination of mutational profiles and drop-off information

2017 ◽  
Author(s):  
Eva Maria Novoa ◽  
Jean-Denis Beaudoin ◽  
Antonio J Giraldez ◽  
John S Mattick ◽  
Manolis Kellis
Keyword(s):  
Reverse Transcription ◽  
Rna Structure ◽  
Structural Information ◽  
Dimethyl Sulfate ◽  
Mutational Signatures ◽  
Chemically Modified ◽  
Chemical Probing ◽  
Genome Wide ◽  
Generation Sequencing

ABSTRACTGenome-wide RNA structure maps have recently become available through the coupling of in vivo chemical probing reagents with next-generation sequencing. Initial analyses relied on the identification of truncated reverse transcription reads to identify the chemically modified nucleotides, but recent studies have shown that mutational signatures can also be used. While these two methods have been employed interchangeably, here we show that they actually provide complementary information. Consequently, analyses using exclusively one of the two methodologies may disregard a significant portion of the structural information. We also show that the identity and sequence environment of the modified nucleotide greatly affect the odds of introducing a mismatch or causing reverse transcriptase drop-off. Finally, we identify specific mismatch signatures generated by dimethyl sulfate probing that can be exploited to remove false positives typically produced in RNA structurome analyses, and how these signatures vary depending on the reverse transcription enzyme used.

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