A weighted sampling algorithm for the design of RNA sequences with targeted secondary structure and nucleotide distribution

Saccharomyces cerevisiae cellular RNase P is composed of both protein and RNA components that are essential for activity. The isolated holoenzyme contains a highly structured RNA of 369 nucleotides that has extensive sequence similarities to the 286-nucleotide RNA associated with Schizosaccharomyces pombe RNase P but bears little resemblance to the analogous RNA sequences in procaryotes or S. cerevisiae mitochondria. Even so, the predicted secondary structure of S. cerevisiae RNA is strikingly similar to the bacterial phylogenetic consensus rather than to previously predicted structures of other eucaryotic RNase P RNAs.

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Modified S-transform as a tool to identify secondary structure elements in RNA

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2017-0023 ◽

2017 ◽

Vol 13 (4) ◽

Author(s):

Nancy Singh ◽

Sunil Datt Sharma ◽

Ragothaman M. Yennamalli

Keyword(s):

Signal Processing ◽

Secondary Structure ◽

Dna Sequences ◽

Tertiary Structure ◽

Processing Method ◽

Rna Sequences ◽

Signal Processing Method ◽

S Transform ◽

Highly Correlated ◽

Signal Processing Methods

AbstractIn this article, we describe the applicability of a signal processing method, specifically the modified S-transform (MST) method, on RNA sequences to identify periodicities between 2 and 11. MicroRNAs (miRNA) are associated with gene regulation and gene silencing and thus have wide applications in biological sciences. Also, the functionality of miRNA is highly associated with its secondary structures (stem, bulge and loop). Signal processing methods have been previously applied on genomic data to reveal the periodicities that determine a wide variety of biological functions, ranging from exon detection to microsatellite identification in DNA sequences. However, there has been less focus on RNA-based signal processing. Here, we show that the signal processing method can be successfully applied to miRNA sequences. We observed that these periodicities are highly correlated with the secondary structure of miRNA and such methods could possibly be used as indicators of secondary and tertiary structure formation.

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TOPAS: network-based structural alignment of RNA sequences

Bioinformatics ◽

10.1093/bioinformatics/btz001 ◽

2019 ◽

Vol 35 (17) ◽

pp. 2941-2948 ◽

Cited By ~ 2

Author(s):

Chun-Chi Chen ◽

Hyundoo Jeong ◽

Xiaoning Qian ◽

Byung-Jun Yoon

Keyword(s):

Computational Complexity ◽

Secondary Structure ◽

Large Scale ◽

Structural Alignment ◽

Programming Approach ◽

Rna Sequences ◽

Optimal Sequence ◽

Dynamic Programming Approach ◽

Probabilistic Network ◽

Rna Structural Alignment

Abstract Motivation For many RNA families, the secondary structure is known to be better conserved among the member RNAs compared to the primary sequence. For this reason, it is important to consider the underlying folding structures when aligning RNA sequences, especially for those with relatively low sequence identity. Given a set of RNAs with unknown structures, simultaneous RNA alignment and folding algorithms aim to accurately align the RNAs by jointly predicting their consensus secondary structure and the optimal sequence alignment. Despite the improved accuracy of the resulting alignment, the computational complexity of simultaneous alignment and folding for a pair of RNAs is O(N6), which is too costly to be used for large-scale analysis. Results In order to address this shortcoming, in this work, we propose a novel network-based scheme for pairwise structural alignment of RNAs. The proposed algorithm, TOPAS, builds on the concept of topological networks that provide structural maps of the RNAs to be aligned. For each RNA sequence, TOPAS first constructs a topological network based on the predicted folding structure, which consists of sequential edges and structural edges weighted by the base-pairing probabilities. The obtained networks can then be efficiently aligned by using probabilistic network alignment techniques, thereby yielding the structural alignment of the RNAs. The computational complexity of our proposed method is significantly lower than that of the Sankoff-style dynamic programming approach, while yielding favorable alignment results. Furthermore, another important advantage of the proposed algorithm is its capability of handling RNAs with pseudoknots while predicting the RNA structural alignment. We demonstrate that TOPAS generally outperforms previous RNA structural alignment methods on RNA benchmarks in terms of both speed and accuracy. Availability and implementation Source code of TOPAS and the benchmark data used in this paper are available at https://github.com/bjyoontamu/TOPAS.

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Using small samples to estimate neutral component size and robustness in the genotype–phenotype map of RNA secondary structure

Journal of The Royal Society Interface ◽

10.1098/rsif.2019.0784 ◽

2020 ◽

Vol 17 (166) ◽

pp. 20190784 ◽

Cited By ~ 1

Author(s):

Marcel Weiß ◽

Sebastian E. Ahnert

Keyword(s):

Secondary Structure ◽

Rna Secondary Structure ◽

Point Mutations ◽

Local Environment ◽

Small Samples ◽

Sequence Length ◽

Rna Sequences ◽

Component Size ◽

Naturally Occurring ◽

Non Coding Rna

In genotype–phenotype (GP) maps, the genotypes that map to the same phenotype are usually not randomly distributed across the space of genotypes, but instead are predominantly connected through one-point mutations, forming network components that are commonly referred to as neutral components (NCs). Because of their impact on evolutionary processes, the characteristics of these NCs, like their size or robustness, have been studied extensively. Here, we introduce a framework that allows the estimation of NC size and robustness in the GP map of RNA secondary structure. The advantage of this framework is that it only requires small samples of genotypes and their local environment, which also allows experimental realizations. We verify our framework by applying it to the exhaustively analysable GP map of RNA sequence length L = 15, and benchmark it against an existing method by applying it to longer, naturally occurring functional non-coding RNA sequences. Although it is specific to the RNA secondary structure GP map in the first place, our framework can probably be transferred and adapted to other sequence-to-structure GP maps.

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The formation of intramolecular secondary structure brings mRNA ends in close proximity

10.1101/289496 ◽

2018 ◽

Cited By ~ 2

Author(s):

Wan-Jung C. Lai ◽

Mohammad Kayedkhordeh ◽

Erica V. Cornell ◽

Elie Farah ◽

Stanislav Bellaousov ◽

...

Keyword(s):

Secondary Structure ◽

Random Coil ◽

Untranslated Regions ◽

Rna Sequences ◽

Sequence Complexity ◽

Translation Factors ◽

Close Proximity ◽

End To End ◽

Regulate Protein ◽

Mrna Length

ABSTRACTA number of protein factors regulate protein synthesis by bridging mRNA ends or untranslated regions (UTRs). Using experimental and computational approaches, we show that mRNAs from various organisms, including humans, have an intrinsic propensity to fold into structures in which the 5’ end and 3’ end are ≤ 7 nm apart irrespective of mRNA length. Computational estimates performed for ∼22,000 human transcripts indicate that the inherent proximity of the ends is a universal property of most, if not all, mRNA sequences. Only specific RNA sequences, which have low sequence complexity and are devoid of guanosines, are unstructured and exhibit end-to-end distances expected for the random coil conformation of RNA. Our results suggest that the intrinsic proximity of mRNA ends may facilitate binding of translation factors that bridge mRNA 5’ and 3’ UTRs. Furthermore, our studies provide the basis for measuring, computing and manipulating end-to-end distances and secondary structure in mRNAs in research and biotechnology.

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Exponentially few RNA structures are designable

10.1101/652313 ◽

2019 ◽

Author(s):

Hua-Ting Yao ◽

Mireille Regnier ◽

Cedric Chauve ◽

Yann Ponty

Keyword(s):

Secondary Structure ◽

Secondary Structures ◽

Rna Structures ◽

Folding Model ◽

Rna Sequences ◽

Rna Sequence ◽

Energy Models ◽

Rna Design ◽

Additional Constraints ◽

Alternative Structure

ABSTRACTThe problem of RNA design attempts to construct RNA sequences that perform a predefined biological function, identified by several additional constraints. One of the foremost objective of RNA design is that the designed RNA sequence should adopt a predefined target secondary structure preferentially to any alternative structure, according to a given metrics and folding model. It was observed in several works that some secondary structures are undesignable, i.e. no RNA sequence can fold into the target structure while satisfying some criterion measuring how preferential this folding is compared to alternative conformations.In this paper, we show that the proportion of designable secondary structures decreases exponentially with the size of the target secondary structure, for various popular combinations of energy models and design objectives. This exponential decay is, at least in part, due to the existence of undesignable motifs, which can be generically constructed, and jointly analyzed to yield asymptotic upper-bounds on the number of designable structures.

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Prediction of RNA Secondary Structure Using Quantum-inspired Genetic Algorithms

Current Bioinformatics ◽

10.2174/1574893614666190916154103 ◽

2020 ◽

Vol 15 (2) ◽

pp. 135-143

Author(s):

Sha Shi ◽

Xin-Li Zhang ◽

Le Yang ◽

Wei Du ◽

Xian-Li Zhao ◽

...

Keyword(s):

Quantum Computing ◽

Secondary Structure ◽

Rna Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Prediction Accuracy ◽

Secondary Structure Prediction ◽

Minimum Free Energy ◽

Rna Sequences ◽

New Strategy

Background: The prediction of RNA secondary structure using optimization algorithms is key to understand the real structure of an RNA. Evolutionary algorithms (EAs) are popular strategies for RNA secondary structure prediction. However, compared to most state-of-the-art software based on DPAs, the performances of EAs are a bit far from satisfactory. Objective: Therefore, a more powerful strategy is required to improve the performances of EAs when applied to the prediciton of RNA secondary structures. Methods: The idea of quantum computing is introduced here yielding a new strategy to find all possible legal paired-bases with the constraint of minimum free energy. The sate of a stem pool with size N is encoded as a population of QGA, which is represented by N quantum bits but not classical bits. The updating of populations is accomplished by so-called quantum crossover operations, quantum mutation operations and quantum rotation operations. Results: The numerical results show that the performances of traditional EAs are significantly improved by using QGA with regard to not only prediction accuracy and sensitivity but also complexity. Moreover, for RNA sequences with middle-short length, QGA even improves the state-of-art software based on DPAs in terms of both prediction accuracy and sensitivity. Conclusion: This work sheds an interesting light on the applications of quantum computing on RNA structure prediction.

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Speeding up simultaneous alignment and folding of RNA sequences

Open Computer Science ◽

10.2478/s13537-014-0217-2 ◽

2014 ◽

Vol 4 (3) ◽

Author(s):

Mária Šimalová ◽

Gabriela Andrejková

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Secondary Structure Prediction ◽

Optimal Solution ◽

Rna Sequences ◽

Multiple Sequence ◽

Rna Secondary Structure Prediction ◽

Folding Algorithm ◽

Alignment Problem

AbstractIn the paper, we describe and develop more effective solutions of two important problems in bioinformatics. The first problem is the multiple sequence alignment problem and the second problem is RNA secondary structure prediction (folding) problem. Each of these problems should be solved with better results if we know the solution of the other one, but usually we only have sequences and we know neither the alignment nor the secondary structure. Precise algorithms solving both of these problems simultaneously are computationally pretentious according to the big length of RNA sequences. In this paper, we have described the method of speeding up the Sankoff’s simultaneous alignment and folding algorithm using the Carrillo-Lipman approach to cut off those computations, that can never lead to an optimal solution.

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