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Evaluating Performance of Different rna secondary structure prediction Programs Using Self-cleaving Ribozymes

Genomics, Proteomics & Bioinformatics 2024年第3期22卷 29-41页

作者： Fei Qi Junjie Chen Yue Chen Jianfeng Sun Yiting Lin Zipeng Chen Philipp Kapranov State Key Laboratory of Cellular Stress Biology School of Life SciencesFaculty of Medicine and Life SciencesXiamen UniversityXiamen 361102China Institute of Genomics School of MedicineHuaqiao UniversityXiamen 361021China Botnar Research Centre University of OxfordOxfordOX37LDUnited Kingdom

Accurate identification of the correct,biologically relevant rna structures is critical to understanding various aspects of rna biology since proper folding represents the key to the functionality of all types of rna molecules and plays pivotal roles in many essential biological ***,a plethora of approaches have been developed to predict,identify,or solve rna structures based on various computational,molecular,genetic,chemical,or physicochemical *** computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation,time,speed,cost,and throughput,but they strongly underperform in terms of accuracy that significantly limits their broader ***,the advantages of these methods led to a steady development of multiple in silico rna secondary structure prediction approaches including recent deep learning-based ***,we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico rna folding prediction tools with tasks of varying *** found that while many programs performed well in relatively simple tasks,their performance varied significantly in more complex rna folding ***,in general,a modern deep learning method outperformed the other programs in the complex tasks in predicting the rna secondary structures,at least based on the specific class of sequences tested,suggesting that it may represent the future of rna structure prediction algorithms.

关键词： rna secondary structure rna secondary structure prediction Ribozyme Deep learning Pseudoknot

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Evaluation of a sophisticated SCFG design for rna secondary structure prediction

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THEORY IN BIOSCIENCES 2011年第4期130卷 313-336页

作者： Nebel, Markus E. Scheid, Anika Univ Kaiserslautern Dept Comp Sci D-6750 Kaiserslautern Germany

Predicting secondary structures of rna molecules is one of the fundamental problems of and thus a challenging task in computational structural biology. Over the past decades, mainly two different approaches have been considered to compute predictions of rna secondary structures from a single sequence: the first one relies on physics-based and the other on probabilistic rna models. Particularly, the free energy minimization (MFE) approach is usually considered the most popular and successful method. Moreover, based on the paradigm-shifting work by McCaskill which proposes the computation of partition functions (PFs) and base pair probabilities based on thermodynamics, several extended partition function algorithms, statistical sampling methods and clustering techniques have been invented over the last years. However, the accuracy of the corresponding algorithms is limited by the quality of underlying physics-based models, which include a vast number of thermodynamic parameters and are still incomplete. The competing probabilistic approach is based on stochastic context-free grammars (SCFGs) or corresponding generalizations, like conditional log-linear models (CLLMs). These methods abstract from free energies and instead try to learn about the structural behavior of the molecules by learning (a manageable number of) probabilistic parameters from trusted rna structure databases. In this work, we introduce and evaluate a sophisticated SCFG design that mirrors state-of-the-art physics-based rna structure prediction procedures by distinguishing between all features of rna that imply different energy rules. This SCFG actually serves as the foundation for a statistical sampling algorithm for rna secondary structures of a single sequence that represents a probabilistic counterpart to the sampling extension of the PF approach. Furthermore, some new ways to derive meaningful structure predictions from generated sample sets are presented. They are used to compare the predictive

关键词： rna secondary structure prediction Random sampling Stochastic grammars

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Revolutions in rna secondary structure prediction

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JOUrnaL OF MOLECULAR BIOLOGY 2006年第3期359卷 526-532页

作者： Mathews, David H. Univ Rochester Ctr Med Dept Biochem & Biophys Rochester NY 14642 USA Univ Rochester Ctr Med Dept Biostat & Computat Biol Rochester NY 14642 USA Univ Rochester Ctr Med Ctr Pediat Biomed Res Rochester NY 14642 USA

rna structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： rna secondary structure prediction free energy partition function nearest neighbor parameters dynamic programming algorithm

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rnaDiffFold: generative rna secondary structure prediction using discrete diffusion models

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BRIEFINGS IN BIOINFORMATICS 2024年第1期26卷 bbae618页

作者： Wang, Zhen Feng, Yizhen Tian, Qingwen Liu, Ziqi Yan, Pengju Li, Xiaolin Chinese Acad Sci Hangzhou Inst Med Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310014 Zhejiang Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310014 Zhejiang Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Hangzhou 310024 Zhejiang Peoples R China

Ribonucleic acid (rna) molecules are essential macromolecules that perform diverse biological functions in living beings. Precise prediction of rna secondary structures is instrumental in deciphering their complex three-dimensional architecture and functionality. Traditional methodologies for rna structure prediction, including energy-based and learning-based approaches, often depict rna secondary structures from a static perspective and rely on stringent a priori constraints. Inspired by the success of diffusion models, in this work, we introduce rnaDiffFold, an innovative generative prediction approach of rna secondary structures based on multinomial diffusion. We reconceptualize the prediction of contact maps as akin to pixel-wise segmentation and accordingly train a denoising model to refine the contact maps starting from a noise-infused state progressively. We also devise a potent conditioning mechanism that harnesses features extracted from rna sequences to steer the model toward generating an accurate secondary structure. These features encompass one-hot encoded sequences, probabilistic maps generated from a pre-trained scoring network, and embeddings and attention maps derived from rna foundation model. Experimental results on both within- and cross-family datasets demonstrate rnaDiffFold's competitive performance compared with current state-of-the-art methods. Additionally, rnaDiffFold has shown a notable proficiency in capturing the dynamic aspects of rna structures, a claim corroborated by its performance on datasets comprising multiple conformations.

关键词： rna secondary structure prediction deep learning discrete diffusion model

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The four ingredients of single-sequence rna secondary structure prediction. A unifying perspective

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rna BIOLOGY 2013年第7期10卷 1185-1196页

作者： Rivas, Elena Howard Hughes Med Inst Ashburn VA USA

Any method for rna secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as the model. The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural rna. Here, I make several unifying observations drawn from looking at more than 40 years of methods for rna secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling rna secondary structure by using intrinsic sequence-based plausible foldability will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem.

关键词： rna secondary structure prediction context-free grammars thermodynamic parameters probabilistic models statistical training

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rna secondary structure prediction Using Self-Adaptive Evolutionary Extreme Learning Machine

RNA Secondary Structure Prediction Using Self-Adaptive Evolu...

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2014全国理论计算机科学学术年会

作者： Tianhang Liu Jianping Yin School of Computer Science National University of Defense TechnologyChangsha 410073China

rna secondary structure prediction is of great importance for the recognition of rna functions,understanding genetic diseases and creating *** a great deal of work has been done in this field until now,it is an open problem yet to be fully investigated in computational molecular *** learning machine (ELM) for single-hidden-layer feedforward neural networks (SLFN) is a powerful machine learning technique attracting plenty of attentions for its fast training ***,an improved ELM called self-adaptive evolutionary extreme learning machine (SaE-ELM) is proposed to overcome a deficiency of ELM that it may have redundant hidden *** this paper,we present an effective and fast method for rna secondary structure prediction using SaE-ELM through comparative sequences analysis,which is the golden standard method when given homologous sequence *** covariation score and the fraction of complementary nucleotides are selected as the components of the feature *** results on 68 rna sequence alignment families from Rfam (version 11.0) show that the proposed method can achieve faster training speed and higher accuracy than support vector machine (SVM),which has been verified as an effective method in rna secondary structure prediction,and generate a more compact network than ELM while keeping the performance have a moderate degradation.

关键词： rna secondary structure prediction Comparative sequence analysis Covariation score Self-adaptive evolutionary extreme learning machine

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rna secondary structure prediction from Multi-Aligned Sequences

RNA Secondary Structure Prediction from Multi-Aligned Sequen...

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作者： Michiaki Hamada

It has been well accepted that the rna secondary structures of most functional non-coding rnas (ncrnas) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in rna bioinformatics; the methods are useful not only to further functional analyses of ncrnas but also to improve the accuracy of secondary structure predictions and to find novel functional rnas from the genome. In this review, I focus on common secondary structure prediction from a given aligned rna sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions. less

关键词： Multiple Sequence Alignment rna secondary structure prediction structure prediction Machine Learning High-Throughput Sequencing Human immunodeficiency virus 1 Mutual information Phylogenetic tree Energy model Common/consensus secondary structures Comparative methods Covariation Probabilistic model Maximum expected gain (MEG) estimators

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rna secondary structure prediction Based on Energy Models

RNA Secondary Structure Prediction Based on Energy Models

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作者： Manato Akiyama Kengo Sato

This chapter introduces the rna secondary structure prediction based on the nearest neighbor energy model, which is one of the most popular architectures of modeling rna secondary structure without pseudoknots. We discuss the parameterization and the parameter determination by experimental and machine learning-based approaches as well as an integrated approach that compensates each other’s shortcomings. Then, folding algorithms for the minimum free energy and the maximum expected accuracy using the dynamic programming technique are introduced. Finally, we compare the prediction accuracy of the method described so far with benchmark datasets. less

关键词： rna secondary structure prediction Machine Learning Thermodynamic parameters Minimum free energy Maximum expected accuracy Nearest neighbor model

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SArna-Predict: Using Adaptive Annealing Schedule and Inversion Mutation Operator for rna secondary structure prediction

SARNA-Predict: Using Adaptive Annealing Schedule and Inversi...

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2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)

作者： Grypma, Peter Tsang, Herbert H. Trinity Western Univ Fac Nat & Appl Sci Appl Res Lab Langley BC Canada

ISBN: (纸本)9781479944675

Ribonucleic Acid (rna) plays a crucial role in many cellular functions including the synthesis of proteins. The structure of rna is essential for it to serve its purposes within the cell. SArna-Predict, which has previously been implemented using Simulated Annealing (SA), has shown excellent results predicting the secondary structure of rna molecules. SA is effective in solving many different optimization problems and for being able to approximate global minima in a solution space. SArna-Predict uses permutation based SA to heuristically search for rna secondary structures with close to the minimum free energy with given constraints. A key step in the annealing process is the mutation of the predicted secondary structure in order to search for other potentially lower energy structures. The mutation changes the structure so as to avoid a local minimum and subsequently the free energy of the new structure is evaluated. The purpose of this paper is to evaluate the new inversion mutation operator and compare its use in terms of prediction accuracy to the percentage swap mutation operator previously used in SArna-Predict. Different annealing schedules used in the SA process are also compared to find the optimal annealing schedule to use for each mutation operator.

关键词： rna molecular biophysics molecular configurations proteins simulated annealing rna molecules rna secondary structure prediction SArna-Predict adaptive annealing schedule cellular functions heuristical search inversion mutation operator optimal annealing schedule optimization ribonucleic acid Annealing Heuristic algorithms prediction algorithms Schedules Sensitivity Simulated annealing rna simulated annealing molecular biology prediction algorithms Molecular Conformation Heuristic algorithms annealing schedule secondary Protein structure annealing Proteins Inversion Mutation Abnormality Sarna Cell Physiological Phenomena

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A Matrix Algorithm for rna secondary structure prediction

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5th International Conference on Pattern Recognition in Bioinformatics

作者： Krishnan, S. P. T. Khurshid, Mushfique Junayed Veeravalli, Bharadwaj Inst Infocomm Res Connexis S Tower21-01 1 Fusionopolis Way Singapore 138632 Singapore Natl Univ Singapore Singapore 119077 Singapore

ISBN: (纸本)9783642160004

In this paper we propose a novel high-performance algorithm, referred to as MARSs (Matrix Algorithm for rna secondary structure prediction), for predicting rna secondary structures with or without pseudoknots. The algorithm is capable of operating in both serial and parallel modes. The algorithm will take complete advantage of the explicit hardware parallelism increasingly available in today g multi-core processors resulting in execution speedups. Unlike Dynamic Programming based algorithms, MARSs is non-recursive by design and therefore eliminates some of the disadvantages of Dynamic Programming based algorithms. We performed a large-scale experiment on a multi-core hardware using real sequences with verified structures. We detail and discuss the results from these experiments using metrics such as performance gains, run-times and prediction accuracy. This is one of the first attempts of its kind to provide a complete flexibility in evolving a rna secondary structure with or without pseudoknots using a matrix-based approach.

关键词： rna secondary structure prediction parallel computing high performance computing multi-core

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