scholarly journals An algorithm for template-based prediction of secondary structures of individual RNA sequences

2017 ◽  
Author(s):  
Josef Pánek ◽  
Martin Černý
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
De Novo ◽  
Secondary Structures ◽  
Rna Structures ◽  
Rna Sequences ◽  
Biologically Relevant ◽  
Rna Molecules ◽  
Rna Structure Prediction ◽  
Limited Applicability

ABSTRACTWhile understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we provide a method to solve a simpler yet still biologically relevant problem: prediction of secondary RNA structure using structure of different molecules as a template.Our method identifies conserved and unconserved subsequences within an RNA molecule. For conserved subsequences, the template structure is directly transferred into the generated structure and combined with de-novo predicted structure for the unconserved subsequences with low evolutionary conservation. The method also determines, when the generated structure is unreliable.The method is validated using experimentally identified structures. The accuracy of the method exceeds that of classical prediction algorithms and constrained prediction methods. This is demonstrated by comparison using large number of heterogeneous RNAs. The presented method is fast and robust, and useful for various applications requiring knowledge of secondary structures of individual RNA sequences.

scholarly journals RNA structure prediction including pseudoknots through direct enumeration of states

2018 ◽  
Author(s):  
Ofer Kimchi ◽  
Tristan Cragnolini ◽  
Michael P. Brenner ◽  
Lucy J. Colwell
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Secondary Structures ◽  
Polymer Physics ◽  
Rna Sequences ◽  
Rna Structure Prediction ◽  
Order Of Magnitude ◽  
Np Complete ◽  
Physics Model ◽  
Physical Quantities

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past forty years. While many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges. Here, we describe a new method to compute the entire free energy landscape of secondary structures of RNA resulting from a primary RNA sequence, by combining a polymer physics model for the entropy of pseudoknots with exhaustive enumeration of the set of possible structures. Our polymer physics model can address arbitrarily complex pseudoknots and has only two free loop entropy parameters that correspond to concrete physical quantities, over an order of magnitude fewer than even the sparsest state-of-the-art algorithms. Our model outperforms previously published methods in predicting pseudoknots, while performing on par with current methods in the prediction of non-pseudoknotted structures. For RNA sequences of ~ 45 nucleotides, or ~ 90 with minimal heuristics, the complet–e enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem.

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 A method for RNA structure prediction shows evidence for structure in lncRNAs

2018 ◽  
Author(s):  
Riccardo Delli ponti ◽  
Alexandros Armaos ◽  
Stefanie Marti ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Sequence Information ◽  
Time Warping ◽  
Single Nucleotide ◽  
Rna Molecules ◽  
Rna Structure Prediction ◽  
Dynamic Time ◽  
Nucleotide Resolution

AbstractTo compare the secondary structures of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure at single-nucleotide resolution using sequence information, and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modelling. CROSSalign can be applied to perform pair-wise comparisons and is able to find homologues between thousands of matches identifying the exact regions of similarity between profiles of different lengths. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/CROSSalign.

scholarly journals A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

2020 ◽  
Author(s):  
Tycho Marinus ◽  
Adam B. Fessler ◽  
Craig A. Ogle ◽  
Danny Incarnato
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Critical Role ◽  
Living Cells ◽  
Pathological Process ◽  
Rna Structures ◽  
Rna Molecules ◽  
Derived Data

ABSTRACTDue to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art SHAPE reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

scholarly journals A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

2021 ◽  
Author(s):  
Tycho Marinus ◽  
Adam B Fessler ◽  
Craig A Ogle ◽  
Danny Incarnato
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Critical Role ◽  
Living Cells ◽  
Pathological Process ◽  
Rna Structures ◽  
Rna Molecules ◽  
Derived Data

Abstract Due to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

scholarly journals NOVEL INTRONIC NON-CODING RNAS CONTRIBUTE TO MAINTENANCE OF PHENOTYPE IN SACCHAROMYCES CEREVISIAE

2015 ◽  
Author(s):  
Katarzyna B Hooks ◽  
Samina Naseeb ◽  
Sam Griffiths-Jones ◽  
Daniela Delneri
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Rna Structure ◽  
Structure Prediction ◽  
De Novo ◽  
Intron Retention ◽  
Intron Loss ◽  
Common Belief ◽  
Rna Structures ◽  
Protein Coding ◽  
Non Coding Rna

The Saccharomyces cerevisiae genome has undergone extensive intron loss during its evolutionary history. It has been suggested that the few remaining introns (in only 5% of protein-coding genes) are retained because of their impact on function under stress conditions. Here, we explore the possibility that novel non-coding RNA structures (ncRNAs) are embedded within intronic sequences and are contributing to phenotype and intron retention in yeast. We employed de novo RNA structure prediction tools to screen intronic sequences in S. cerevisiae and 36 other fungi. We identified and validated 19 new intronic RNAs via RNAseq and RT-PCR. Contrary to common belief that excised introns are rapidly degraded, we found that, in six cases, the excised introns were maintained intact in the cells. In other two cases we showed that the ncRNAs were further processed from their introns. RNAseq analysis confirmed higher expression of introns in the ribosomial protein genes containing predicted RNA structures. We deleted the novel intronic RNA structure within the GLC7 intron and showed that this predicted ncRNA, rather than the intron itself, is responsible for the cell???s ability to respond to salt stress. We also showed a direct association between the presence of the intronic ncRNA and GLC7 expression. Overall, these data support the notion that some introns may have been maintained in the genome because they harbour functional ncRNAs.

scholarly journals Characteristics and Prediction of RNA Structure

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Hengwu Li ◽  
Daming Zhu ◽  
Caiming Zhang ◽  
Huijian Han ◽  
Keith A. Crandall
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Rna Folding ◽  
Golden Section ◽  
Hierarchical Structures ◽  
Secondary Structures ◽  
Sequence Length ◽  
Rna Structures ◽  
Rna Secondary Structures ◽  
Hierarchical Folding

RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is NP-hard. Most RNAs fold during transcription from DNA into RNA through a hierarchical pathway wherein secondary structures form prior to tertiary structures. Real RNA secondary structures often have local instead of global optimization because of kinetic reasons. The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms. This study is a novel report on RNA folding that accords with the golden mean characteristic based on the statistical analysis of the real RNA secondary structures of all 480 sequences from RNA STRAND, which are validated by NMR or X-ray. The length ratios of domains in these sequences are approximately 0.382L, 0.5L, 0.618L, andL, whereLis the sequence length. These points are just the important golden sections of sequence. With this characteristic, an algorithm is designed to predict RNA hierarchical structures and simulate RNA folding by dynamically folding RNA structures according to the above golden section points. The sensitivity and number of predicted pseudoknots of our algorithm are better than those of the Mfold, HotKnots, McQfold, ProbKnot, and Lhw-Zhu algorithms. Experimental results reflect the folding rules of RNA from a new angle that is close to natural folding.

scholarly journals Predicting pseudoknotted structures across two RNA sequences

2012 ◽  
Vol 28 (23) ◽  
pp. 3058-3065 ◽  
Author(s):  
Jana Sperschneider ◽  
Amitava Datta ◽  
Michael J. Wise
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Prediction Method ◽  
Supplementary Information ◽  
Rna Structures ◽  
Rna Sequences ◽  
Test Set ◽  
Comparative Structure

Abstract Motivation Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity. Results A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support. Availability DotKnot-PW and the RNA structure test set are available at the web site http://dotknot.csse.uwa.edu.au/pw. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals New Algorithms in RNA Structure Prediction Based on BHG

2020 ◽  
Vol 34 (13) ◽  
pp. 2050031
Author(s):  
Zhendong Liu ◽  
Gang Li ◽  
Jun S. Liu
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Rna Folding ◽  
Rna Structures ◽  
Structural Prediction ◽  
Extended Structure ◽  
Rna Structure Prediction ◽  
Computing Algorithm ◽  
Hard Problems ◽  
Basin Hopping

There are some NP-hard problems in the prediction of RNA structures. Prediction of RNA folding structure in RNA nucleotide sequence remains an unsolved challenge. We investigate the computing algorithm in RNA folding structural prediction based on extended structure and basin hopping graph, it is a computing mode of basin hopping graph in RNA folding structural prediction including pseudoknots. This study presents the predicting algorithm based on extended structure, it also proposes an improved computing algorithm based on barrier tree and basin hopping graph, which are the attractive approaches in RNA folding structural prediction. Many experiments have been implemented in Rfam14.1 database and PseudoBase database, the experimental results show that our two algorithms are efficient and accurate than the other existing algorithms.

scholarly journals Evaluating DCA-based method performances for RNA contact prediction by a well-curated dataset

2019 ◽  
Author(s):  
F. Pucci ◽  
M. Zerihun ◽  
E. Peter ◽  
A. Schug
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Sequence Data ◽  
Three Dimensional ◽  
Spatial Proximity ◽  
Dimensional Structure ◽  
Rna Sequences ◽  
Contact Prediction ◽  
Rna Molecules ◽  
Quality Of Structure

AbstractRNA molecules play many pivotal roles in the cellular functioning that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analysed by methods such as Direct Coupling Analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated dataset of about seventy RNA structures with high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor difference between the performances of the different methods. Moreover, we discuss how these predictions are robust for different contact definitions and how strongly depend on procedures used to curate and align the families of homologous RNA sequences.

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