cRNAsp12 Web Server for the Prediction of Circular RNA Secondary Structures and Stabilities
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Abstract:
Circular RNAs (circRNAs) are a novel class of non-coding RNA that, unlike linear RNAs, form a covalently closed loop without the 5' and 3' ends. Growing evidence shows that circular RNAs play important roles in life processes and have great potential implications in clinical and research fields. The accurate modeling of circRNAs structure and stability has far-reaching impact on our understanding of their functions and our ability to develop RNA-based therapeutics. The cRNAsp12 server offers a user-friendly web interface to predict circular RNA secondary structures and folding stabilities from the sequence. Through the helix-based landscape partitioning strategy, the server generates distinct ensembles of structures and predicts the minimal free energy structures for each ensemble with the recursive partition function calculation and backtracking algorithms. For structure predictions in the limited structural ensemble, the server also provides users with the option to set the structural constraints of forcing the base pairs and/or forcing the unpaired bases, such that only structures that meet the criteria are enumerated recursively.Keywords:
Circular RNA
Nucleic acid structure
Forcing (mathematics)
Folding (DSP implementation)
Journal Article Pattern recognition in nucleic acid sequences. II. An efficient method for finding locally stable secondary structures Get access Minoru I. Kanehisa, Minoru I. Kanehisa Theoretical Biology and Biophysics Group, University of California, Los Alamos National LaboratoryLos Alamos, NM 87545, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Walter B. Goad Walter B. Goad Theoretical Biology and Biophysics Group, University of California, Los Alamos National LaboratoryLos Alamos, NM 87545, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Nucleic Acids Research, Volume 10, Issue 1, 11 January 1982, Pages 265–278, https://doi.org/10.1093/nar/10.1.265 Published: 11 January 1982 Article history Received: 15 September 1981 Published: 11 January 1982
Nucleic acid structure
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Nucleic acid structure
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Prediction of RNA secondary structure is one of the pivotal tasks in bioinformatics. Several computational methods based on dynamic programming, statistical models, have been proposed with considerable success. A typical substructure that occurs in several classes of RNAs, called pseudoknot, plays vital role in many biological processes. Prediction of the pseudoknots in RNA secondary structure is still an open research problem. In this paper, we employ matched filtering approach to determine the secondary structure of a target RNA. The central idea is to match the stem patterns in the base-pairing matrix of RNA with unknown secondary structure. The proposed approach predicts number of stems, loops and also the presence of pseudoknot in the secondary structure of RNA. Illustrative examples on real RNA sequences illustrate the effectiveness and accuracy of our proposed method.
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The crystal structure based model of the catalytic center of Ago2 revealed that the siRNA and the mRNA must be able to form an A-helix for correct positing of the scissile phosphate bond for cleavage in RNAi. This suggests that base pairing of the target mRNA with itself, i.e. secondary structure, must be removed before cleavage. Early on in the siRNA design, GC-rich target sites were avoided because of their potential to be involved in strong secondary structure. It is still unclear how important a factor mRNA secondary structure is in RNAi. However, it has been established that a difference in the thermostability of the ends of an siRNA duplex dictate which strand is loaded into the RNA-induced silencing complex. Here, we use a novel secondary structure prediction method and duplex-end differential calculations to investigate the importance of a secondary structure in the siRNA design. We found that the differential duplex-end stabilities alone account for functional prediction of 60% of the 80 siRNA sites examined, and that secondary structure predictions improve the prediction of site efficacy. A total of 80% of the non-functional sites can be eliminated using secondary structure predictions and duplex-end differential.
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The secondary structures, as well as the nucleotide sequences, are the important features of RNA molecules to characterize their functions. According to the thermodynamic model, however, the probability of any secondary structure is very small. As a consequence, any tool to predict the secondary structures of RNAs has limited accuracy. On the other hand, there are a few tools to compensate the imperfect predictions by calculating and visualizing the secondary structural information from RNA sequences. It is desirable to obtain the rich information from those tools through a friendly interface. We implemented a web server of the tools to predict secondary structures and to calculate various structural features based on the energy models of secondary structures. By just giving an RNA sequence to the web server, the user can get the different types of solutions of the secondary structures, the marginal probabilities such as base-paring probabilities, loop probabilities and accessibilities of the local bases, the energy changes by arbitrary base mutations as well as the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp, which integrates software tools, CentroidFold, CentroidHomfold, IPKnot, CapR, Raccess, Rchange and RintD.
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Prediction of RNA secondary structure is important in the functional analysis of RNA molecules. The RDfolder web server described in this paper provides two methods for prediction of RNA secondary structure: random stacking of helical regions and helical regions distribution. The random stacking method predicts secondary structure by Monte Carlo simulations. The method of helical regions distribution predicts secondary structure based on the helices that appear most frequently in the set of structures, which are generated by the random stacking method. The RDfolder web server can be accessed at http://rna.cbi.pku.edu.cn.
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Background Non-coding RNAs perform a wide range of functions inside the living cells that are related to their structures. Several algorithms have been proposed to predict RNA secondary structure based on minimum free energy. Low prediction accuracy of these algorithms indicates that free energy alone is not sufficient to predict the functional secondary structure. Recently, the obtained information from the SHAPE experiment greatly improves the accuracy of RNA secondary structure prediction by adding this information to the thermodynamic free energy as pseudo-free energy. Method In this paper, a new method is proposed to predict RNA secondary structure based on both free energy and SHAPE pseudo-free energy. For each RNA sequence, a population of secondary structures is constructed and their SHAPE data are simulated. Then, an evolutionary algorithm is used to improve each structure based on both free and pseudo-free energies. Finally, a structure with minimum summation of free and pseudo-free energies is considered as the predicted RNA secondary structure. Results and Conclusions Computationally simulating the SHAPE data for a given RNA sequence requires its secondary structure. Here, we overcome this limitation by employing a population of secondary structures. This helps us to simulate the SHAPE data for any RNA sequence and consequently improves the accuracy of RNA secondary structure prediction as it is confirmed by our experiments. The source code and web server of our proposed method are freely available at http://mostafa.ut.ac.ir/ESD-Fold/.
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Determining the structure of ribosomal RNAs (rRNAs) is one of the crucial steps in understanding the process of protein synthesis, for which rRNAs are one of the basic components. Nevertheless, due to extreme technical difficulties, spatial (3D) structures have been resolved experimentally for only 14 organisms. Also, computational prediction of 3D rRNA structure is almost impossible, and prediction of secondary structure (the list of base pairs in the folded RNA), an important intermediate step between sequence and 3D structure that is used broadly in modeling of RNA structures, is in the case of rRNAs hindered by both extreme sequence length and high structure complexity. Here we present a proof-of-concept for an rRNA secondary structure prediction method that utilizes known structures as structural templates. Our template-based prediction algorithm determines those regions of the sequence for which structure is being predicted that are conserved well enough so that their secondary structure can be copied over from the template. The structure of the remaining, unconserved regions is predicted using a thermodynamic folding model. Applying a baseline implementation of our algorithm to the E. coli 16S rRNA, we have achieved state-of-the-art recall and precision using the structure of T. thermophilus 16S rRNA as a template.
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Nucleic acid structure
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RNAstructure is a software package for RNA secondary structure prediction and analysis. This contribution describes a new set of web servers to provide its functionality. The web server offers RNA secondary structure prediction, including free energy minimization, maximum expected accuracy structure prediction and pseudoknot prediction. Bimolecular secondary structure prediction is also provided. Additionally, the server can predict secondary structures conserved in either two homologs or more than two homologs. Folding free energy changes can be predicted for a given RNA structure using nearest neighbor rules. Secondary structures can be compared using circular plots or the scoring methods, sensitivity and positive predictive value. Additionally, structure drawings can be rendered as SVG, postscript, jpeg or pdf. The web server is freely available for public use at: http://rna.urmc.rochester.edu/RNAstructureWeb.
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