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Current Protocols in Nucleic Acid Chemistry 2016年第1期2016卷 11.2.1-11.2.19页

作者： Mathews, David H. Turner, Douglas H. Watson, Richard M. University of Rochester Rochester NY United States

In this unit, protocols are provided for predicting rna secondary structure with the user-friendly rnastructure desktop computer program and the rnastructure Web server. The minimum free energy structure and a set of suboptimal structures with similar free energies are predicted. prediction of high-affinity oligonucleotide binding sites to a structured rna target is also presented. © 2016 by John Wiley & Sons, Inc.

关键词： Binding affinity Free energy minimization Partition function rna folding rna secondary structure prediction Thermodynamics

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rna secondary structure prediction

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Current protocols in nucleic acid chemistry 2007年第1期Chapter 11卷 Unit 11.2页

作者： David H Mathews Douglas H Turner Michael Zuker University of Rochester Rochester New York USA.

Details for predicting secondary structure of rna sequences using free energy minimization are given. Protocols present the use of the rnastructure computer program (for PCs) and the mfold server (for Unix platforms). The minimum free energy structure and a set of suboptimal structures with similar free energies are predicted. prediction of high-affinity oligonucleotide binding sites to a structured rna target is also presented.

关键词： rna secondary structure prediction free energy minimization thermodynamics binding affinity rna folding partition function

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rna secondary structure prediction using Fruit Fly Optimization Algorithm

RNA secondary structure prediction using Fruit Fly Optimizat...

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IEEE-Region-10 Symposium (TENSYMP) - Technology for Impactful Sustainable Development

作者： Chatterjee, Sajib Rabeya, Sayla Parvin Halder, Setu Mondal, Madhab Sujana, Farjana Yesmin North Western Univ Dept Comp Sci & Engn Khulna Bangladesh

ISBN: (纸本)9781728173665

rna secondary structure prediction (RSSP) is an optimization problem, where a stable secondary structure is acquired from an rna primary sequence. Many exact, heuristic and metaheuristic algorithms established in recent years to solve the RSSP problem. We have resolved an accession based on metaheuristic algorithm named Fruit Fly Optimization algorithm to solve the rna secondary structure prediction problem. FOA is a population-based metaheuristic that works better than all other related existing algorithms and has been employed in different optimization problems. We have redesigned the operators of the FOA algorithm and calculated the minimum Gibbs free energy (Delta G) of the structure to solve the rna secondary structure problem. We have a Repair function which is known as novel operator that is used to verify and expel the repeated stem from rna sequence, which is very time-efficient. Every quality of the solutions and spending time are calculated in designing the operators and the repair function. The raised methodology gives efficiency, robustness, and effectiveness in solving the RSSP problem.

关键词： rna secondary structure prediction Fruit fly optimization algorithm Minimum Gibbs free energy Repair function efficiency

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rna secondary structure prediction Based on Long Short-Term Memory Model 14th

RNA Secondary Structure Prediction Based on Long Short-Term ...

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14th International Conference on Intelligent Computing (ICIC)

作者： Wu, Hongjie Tang, Ye Lu, Weizhong Chen, Cheng Huang, Hongmei Fu, Qiming Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Peoples R China Jiangsu Key Lab Intelligent Bldg Energy Efficienc Suzhou 215009 Jiangsu Peoples R China

ISBN: (纸本)9783319959290;9783319959306

rna secondary structure prediction is an important issue in structural bioinformatics. The difficulty of rna secondary structure prediction with pseudoknot is increased due to complex structure of the pseudoknot. Traditional machine learning methods, such as support vector machine, markov model and neural network, have been tried and their prediction accuracy are also increasing. The rna secondary structure prediction problem is transferred into the classification problem of base in the sequence to reduce computational complexity to a certain extent. A model based on LSTM deep recurrent neural network is proposed for rna secondary structure prediction. Subsequently, comparative experiments were conducted on the authoritative data set rna STRAND containing 1488 rna sequences with pseudoknot. The experimental results show that the SEN and PPV of this method are higher than the other two typical methods by 1% and 11%.

关键词： rna secondary structure prediction Recurrent neural network Pseudoknots Classification

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rna secondary structure prediction algorithm based on combinatorial optimization algorithm and SVMs method

RNA secondary structure prediction algorithm based on combin...

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10th World Congress on Intelligent Control and Automation (WCICA)

作者： He Jing-yuan Mu Chao Huang Hai-hun Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9781467313988

new rna secondary structure prediction algorithm that can predict pseudoknots is proposed, it combines the stem-loop combinatorial optimization algorithm and SVMs (Support Vector Machines, SVMs) method. The algorithm firstly finds out the optimal stem-loop structure and suboptimum structures based on dynamic neighbor topology particle swarm optimization algorithm, and then puts these loops into SVMs. The output from SVMs can decide whether there exist a pseudoknot. The experimental results demonstrate the superiority of our algorithm over the other methods in terms of solution quality and convergence rates.

关键词： dynamic neighbor topology particle optimization algorithm SVMs rna secondary structure prediction

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Approximation and exact algorithms for rna secondary structure prediction and recognition of stochastic context-free languages

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JOUrnaL OF COMBINATORIAL OPTIMIZATION 1999年第2-3期3卷 321-336页

作者： Akutsu, T Univ Tokyo Inst Med Sci Ctr Human Genome Minato Ku Tokyo 1088639 Japan

For a basic version (i.e., maximizing the number of base-pairs) of the rna secondary structure prediction problem and the construction of a parse tree for a stochastic context-free language, O(n(3)) time algorithms were known. For both problems, this paper shows slightly improved O(n(3)(loglog n)(1/2)/(log n)(1/2)) time exact algorithms, which are obtained by combining Valiant's algorithm for context-free recognition with fast funny matrix multiplication. Moreover, this paper shows an O(n(2.776) + (1/epsilon)(O(1))) time approximation algorithm for the former problem and an O(n(2.976) log n + (1/epsilon)(O(1))) time approximation algorithm for the latter problem, each of which has a guaranteed approximation ratio 1 - epsilon for any positive constant epsilon, where the absolute value of the logarithm of the probability is considered as an objective value in the latter problem. The former algorithm is obtained from a non-trivial modification of the well-known O(n(3)) time dynamic programming algorithm, and the latter algorithm is obtained by combining Valiant's algorithm with approximate funny matrix multiplication. Several related results are shown too.

关键词： computational biology rna secondary structure prediction stochastic context-free grammar approximation algorithms

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StemP: A Fast and Deterministic Stem-Graph Approach for rna secondary structure prediction

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第5期20卷 3278-3291页

作者： Tang, Mengyi Hwang, Kumbit Kang, Sung Ha Georgia Inst Technol Sch Math Atlanta GA 30032 USA

We propose a new deterministic methodology to predict the secondary structure of rna sequences. What information of stem is important for structure prediction, and is it enough ? The proposed simple deterministic algorithm uses minimum stem length, Stem-Loop score, and co-existence of stems, to give good structure predictions for short rna and trna sequences. The main idea is to consider all possible stem with certain stem loop energy and strength to predict rna secondary structure. We use graph notation, where stems are represented as vertexes, and co-existence between stems as edges. This full Stem-graph presents all possible folding structure, and we pick sub-graph(s) which give the best matching energy for structure prediction. Stem-Loop score adds structure information and speeds up the computation. The proposed method can predict secondary structure even with pseudo knots. One of the strengths of this approach is the simplicity and flexibility of the algorithm, and it gives a deterministic answer. Numerical experiments are done on various sequences from Protein Data Bank and the Gutell Lab using a laptop and results take only a few seconds.

关键词： Graph representation maximal clique rna secondary structure prediction structure alignment

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rnaPredict - An evolutionary algorithm for rna secondary structure prediction

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2008年第1期5卷 25-41页

作者： Wiese, Kay C. Deschenes, Alain A. Hendriks, Andrew G. Simon Fraser Univ Sch Comp Sci Surrey BC V3T 0A3 Canada Chem Comp Grp Inc Montreal PQ H3A 2R7 Canada

This paper presents two in-depth studies on rnaPredict, an evolutionary algorithm for rna secondary structure prediction. The first study is an analysis of the performance of two thermodynamic models, Individual Nearest Neighbor (INN) and Individual Nearest Neighbor Hydrogen Bond (INN-HB). The correlation between the free energy of predicted structures and the sensitivity is analyzed for 19 rna sequences. Although some variance is shown, there is a clear trend between a lower free energy and an increase in true positive base pairs. With increasing sequence length, this correlation generally decreases. In the second experiment, the accuracy of the predicted structures for these 19 sequences are compared against the accuracy of the structures generated by the mfold dynamic programming algorithm (DPA) and also to known structures. rnaPredict is shown to outperform the minimum free energy structures produced by mfold and has comparable performance when compared to suboptimal structures produced by mfold.

关键词： rna secondary structure prediction evolutionary computation rnaPredict

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SArna-Predict: Accuracy Improvement of rna secondary structure prediction Using Permutation-Based Simulated Annealing

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2010年第4期7卷 727-740页

作者： Tsang, Herbert H. Wiese, Kay C. Simon Fraser Univ Bioinformat Res Lab Sch Comp Sci Surrey BC V3T 0A3 Canada

Ribonucleic acid (rna), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same rna strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of rna molecules is the key to their function, algorithms for the prediction of rna structure are of great value. In this article, we demonstrate the usefulness of SArna-Predict, an rna secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SArna-Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art rna prediction algorithms: mfold, Pseudoknot(pknotsRE), NUPACK, pknotsRG-mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic, dynamic programming, and statistical sampling techniques. An evaluation for the performance of SArna-Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven rna classes (trna, viral rna, antigenomic HDV, telomerase rna, tmrna, rrna, rnaseP, 5S rrna, Group I intron 23S rrna, Group I intron 16S rrna, and 16S rrna) were performed. The results presented in this paper demonstrate that SArna-Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2).

关键词： rna secondary structure prediction rna folding ribonucleic acid permutation simulated annealing

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The unified accelerator architecture for rna secondary structure prediction on FPGA

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JOUrnaL OF SUPERCOMPUTING 2012年第3期61卷 826-855页

作者： Xia, Fei Dou, Yong Jin, Guoqing NUDT Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Naval Univ Engn Elect Engn Coll Wuhan Peoples R China

In the field of rna secondary structure prediction, MFE, SCFG and the homologous comparative sequence analysis are three kinds of classical computation analysis approaches. However, the parallel efficiency of many implementations on general-purpose computers are greatly limited by complicated data dependency and tight synchronization. Additionally, large scale parallel computers are too expensive to be used easily for many research institutes. Recently, FPGA chips provide a new approach to accelerate those algorithms by exploiting fine-grained custom design. We propose a unified parallelism schemes and logic circuit architecture for three classical algorithms-Zuker, rnaalifold and CYK, based on a systolic-like master-slave PE (Processing Element) array for fine-grained hardware implementation on FPGA. We partition tasks by columns and assign them to PEs for load balance. We exploit data reuse schemes to reduce the need to load matrix from external memory. The experimental results show a factor of 12-14x speedup over the three software versions running on a PC platform with AMD Phenom 9650 Quad CPU. The computational power of our prototype is comparable to a PC cluster consisting of 20 Intel-Xeon CPUs for rna secondary structure prediction;however, the power consumption is only about 10% of the latter.

关键词： Bioinformatics rna secondary structure prediction Fine-grained parallelism FPGA Hardware accelerator

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