RNA二级结构预测系统构建

运用下列RNA二级结构预测算法:碱基最大配对方法、Zuker极小化自由能方法、螺旋区最优堆积、螺旋区随机堆积和所有可能组合方法与基于一级螺旋区的RNA二级结构绘图技术, 构建了RNA二级结构预测系统Rnafold. 另外, 通过随机选取20个tRNA序列, 从自由能和三叶草结构两个方面比较了前4种二级结构预测算法, 并运用t检验方法分析了自由能的统计学差别. 从三叶草结构来看, 以随机堆积方法最好, 其次是螺旋区最优堆积方法和Zuker算法, 以碱基最大配对方法最差. 最后, 分析了两种极小化自由能方法之间的差别.     

RNA二级结构数据库

RNA分子众多、结构复杂、功能重要,已经成为当前重要的研究热点之一。RNA的功能与结构密切相关,伴随RNA分子及功能的发现,建立了有关RNA二级结构的数据库,一方面有助于理解RNA功能的结构基础,一方面有助于开发各种有关RNA结构的预测模型。本文对近年常见的RNA二级结构数据库作一概述,希望有助于相关工作者更好地了解与应用相关数据。     

基于支持向量机分类的RNA共同二级结构预测

比较序列分析作为RNA二级结构预测的最可靠途径, 已经发展出许多算法。将基于此方法的结构预测视为一个二值分类问题: 根据序列比对给出的可用信息, 判断比对中任意两列能否构成碱基对。分类器采用支持向量机方法, 特征向量包括共变信息、热力学信息和碱基互补比例。考虑到共变信息对序列相似性的要求, 通过引入一个序列相似度影响因子, 来调整不同序列相似度情况下共变信息和热力学信息对预测过程的影响, 提高了预测精度。通过49组Rfam-seed比对的验证, 显示了该方法的有效性, 算法的预测精度优于多数同类算法, 并且可以预测简单的假节。     

Secondary structure prediction for aligned RNA sequences

Most functional RNA molecules have characteristic secondary structures that are highly conserved in evolution. Here we present a method for computing the consensus structure of a set aligned RNA sequences taking into account both thermodynamic stability and sequence covariation. Comparison with phylogenetic structures of rRNAs shows that a reliability of prediction of more than 80% is achieved for only five related sequences. As an application we show that the Early Noduline mRNA contains significant secondary structure that is supported by sequence covariation.     

Simulating the Folding Pathway of RNA Secondary Structure Using the Modified Ant Colony Algorithm

<正> A new method for simulating the folding pathway of RNA secondary structure using the modified ant colony algorithmis proposed.For a given RNA sequence,the set of all possible stems is obtained and the energy of each stem iscalculated and stored at the initial stage.Furthermore,a more realistic formula is used to compute the energy ofmulti-branch loop in the following iteration.Then a folding pathway is simulated,including such processes as constructionof the heuristic information,the rule of initializing the pheromone,the mechanism of choosing the initial andnext stem and the strategy of updating the pheromone between two different stems.Finally by testing RNA sequences withknown secondary structures from the public databases,we analyze the experimental data to select appropriate values forparameters.The measure indexes show that our procedure is more consistent with phylogenetically proven structures thansoftware RNAstructure sometimes and more effective than the standard Genetic Algorithm.     

RNA二级结构在微生物系统发育分析上的应用

RNA二级结构用于微生物分类和系统发育分析在近年来逐渐受到注意并发展起来。文章就此方面的发展做了简要综述,其中对在系统发育分析上的应用部分作了较重点介绍。     

Secondary structure prediction of interacting RNA molecules

Computational tools for prediction of the secondary structure of two or more interacting nucleic acid molecules are useful for understanding mechanisms for ribozyme function, determining the affinity of an oligonucleotide primer to its target, and designing good antisense oligonucleotides, novel ribozymes, DNA code words, or nanostructures. Here, we introduce new algorithms for prediction of the minimum free energy pseudoknot-free secondary structure of two or more nucleic acid molecules, and for prediction of alternative low-energy (sub-optimal) secondary structures for two nucleic acid molecules. We provide a comprehensive analysis of our predictions against secondary structures of interacting RNA molecules drawn from the literature. Analysis of our tools on 17 sequences of up to 200 nucleotides that do not form pseudoknots shows that they have 79% accuracy, on average, for the minimum free energy predictions. When the best of 100 sub-optimal foldings is taken, the average accuracy increases to 91%. The accuracy decreases as the sequences increase in length and as the number of pseudoknots and tertiary interactions increases. Our algorithms extend the free energy minimization algorithm of Zuker and Stiegler for secondary structure prediction, and the sub-optimal folding algorithm by Wuchty et al. Implementations of our algorithms are freely available in the package MultiRNAFold.     

Prediction of geometrically feasible three-dimensional structures of pseudoknotted RNA through free energy estimation

Accurate free energy estimation is essential for RNA structure prediction. The widely used Turner''s energy model works well for nested structures. For pseudoknotted RNAs, however, there is no effective rule for estimation of loop entropy and free energy. In this work we present a new free energy estimation method, termed the pseudoknot predictor in three-dimensional space (pk3D), which goes beyond Turner''s model. Our approach treats nested and pseudoknotted structures alike in one unifying physical framework, regardless of how complex the RNA structures are. We first test the ability of pk3D in selecting native structures from a large number of decoys for a set of 43 pseudoknotted RNA molecules, with lengths ranging from 23 to 113. We find that pk3D performs slightly better than the Dirks and Pierce extension of Turner''s rule. We then test pk3D for blind secondary structure prediction, and find that pk3D gives the best sensitivity and comparable positive predictive value (related to specificity) in predicting pseudoknotted RNA secondary structures, when compared with other methods. A unique strength of pk3D is that it also generates spatial arrangement of structural elements of the RNA molecule. Comparison of three-dimensional structures predicted by pk3D with the native structure measured by nuclear magnetic resonance or X-ray experiments shows that the predicted spatial arrangement of stems and loops is often similar to that found in the native structure. These close-to-native structures can be used as starting points for further refinement to derive accurate three-dimensional structures of RNA molecules, including those with pseudoknots.     

图论在RNA二级结构中的应用

图论是以图为研究对象的数学分支,是一门研究事物对象在图表示法中的特征与性质的学科。鉴于RNA二级结构在功能基因组研究中的重要地位,已发展了用二维图解表示法描述RNA二级结构。文章介绍了用于RNA二级结构图解表示法的两种图,即树图和对偶图的构造规则。并在树图表示基础上产生Laplacian矩阵和相应本征值谱。以有害突变预测和类RNA模体设计的例子,说明图论在RNA二级结构中的应用,同时对可能存在的一些问题做了讨论。     

随机文法模型在RNA二级结构预测中的应用

RNA二级结构的研究是当今计算分子生物学的一个重要课题,基于比较序列分析方法的随机文法模型预测RNA二级结构具有准确率高,能对假结建模,但不易实施等特点,本文通过分析随机文法对RNA二级结构建模的过程,提出了一种综合利用比较序列方法,随机文法方法,词条方法预测RNA二级结构的方案.     

RNA二级子结构的计数

多核苷酸的二级结构可视为一类顶点标号平面图,通常通过枚举每类RNA二级结构图的各种子图来计算其递推公式．本文给出了若干限制端环长度的RNA二级子结构的递推公式及渐近值。     

Search for Conserved Secondary Structures of RNA

We suggest a new algorithm to search a given set of the RNA sequences for conserved secondary structures. The algorithm is based on alignment of the sequences for potential helical strands. This procedure can be used to search for new structured RNAs and new regulatory elements. It is efficient for the genome-scale analysis. The results of various tests run with this algorithm are shown.     

基于启发式算法预测含假结RNA二级结构的研究进展

RNA二级结构的预测算法研究已有近40年的发展历程,研究假结也将近30年的历史。在此期间,RNA二级结构的预测算法取得了很大进步,但假结预测的正确率依然偏低。其中启发式算法能较好地处理复杂假结,使其成为率先解决假结预测难题可能性最大的算法。迄今为止,未见系统地专门总结预测假结的各种启发式算法及其优点与缺点的报道。本文详细介绍了近年来国际上流行的贪婪算法、遗传算法、ILM算法、HotKnots算法以及FlexStem算法等五种算法,并总结分析了每种算法的优点与不足,最后提出在未来一段时期内,利用启发式算法提高假结预测准确度应从建立更完善的假结模型、加入更多影响因素、借鉴不同算法的优势等方面入手。为含假结RNA二级结构预测的研究提供参考。     

RNA二级结构的最小自由能算法

RNA(即tRNA,rRNA,mRNA和SnRNA)有两大主要功能：一是某些病毒的遗传物质;二是参与蛋白质的合成,这些与细胞分化、代谢、记忆的储存等有重要关系,这些功能与RNA二级结构的稳定性。自由能密切相关．常用的计算自由能的方法有热力学微扰法及热力学微积分法等．本文以寻找最小自由能二级结构为目的,给出了RNA二级结构的最小自由能算法,该算法的时间复杂性不超过O(n^4)。     

文法推断RNA二级结构的研究进展

生物序列可看成是一种语言,通过计算语言学的方法理解生物序列的内涵是近年来研究的热点,本文综述了文法推断RNA二级结构的基本原理,研究历史和现状,阐述了文法推断RNA二级结构的理论模型和算法,列举了一些有代表性的预测方法,总结了存在的问题并展望了研究的趋势。     

简单的一致性方法预测蛋白质二级结构

蛋白质二级结构预测对于我们了解蛋白质空间结构是至关重要的一步。文章提出了一种简单的二级结构预测方法,该方法采用多数投票法将现有的3种较好的二级结构预测方法的预测结果汇集形成一致性预测结果。从PDB数据库中随机选取近两年新测定结构的57条相似性小于30%的蛋白质,对该方法的预测结果进行测试,其Q3准确率比3种独立的方法提高了1.12—2.29%,相关系数及SOV准确率也有相应的提高。并且各项准确率均比同样采用一致性方法的Jpred二级结构预测程序准确率要高。这种预测方法虽然原理简单,但无须使用额外的参数,计算量小,易于实现,最重要的前提就是必须选用目前准确性比较出色的蛋白质二级结构预测方法。     

Fuzzy Kernel Clustering of RNA Secondary Structure Ensemble Using a Novel Similarity Metric

Abstract

Measuring the (dis)similarity between RNA secondary structures is critical for the study of RNA secondary structures and has implications to RNA functional characterization. Although a number of methods have been developed for comparing RNA structural similarities, their applications have been limited by the complexity of the required computation. In this paper, we present a novel method for comparing the similarity of RNA secondary structures generated from the same RNA sequence, i.e., a secondary structure ensemble, using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices. We have mapped the feature vectors of the singular values to a kernel space, where (dis)similarities among the mapped feature vectors become more evident, making clustering of RNA secondary structures easier to handle. The pair-wise comparison of RNA structures is achieved through computing the distance between the singular value vectors in the kernel space. We have applied a fuzzy kernel clustering method, using this similarity metric, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.     

多目标遗传算法的含假结RNA二级结构预测

假结是RNA中一种重要的结构,由于建模的困难导致它更难被预测。通过碱基之间的配对概率来预测含假结RNA二级结构的Prob Knot算法具有很高的精度,但该算法仅用了配对概率作为预测依据,导致阴性配对大量出现,因此精度中的特异性较低。实验结合Prob Knot算法中碱基配对概率模型,通过使用多目标遗传算法,从而提高预测含假结RNA二级结构的特异性,以此促进总体精度的提高。实验过程中,首先计算出每个碱基成为单链的概率,作为新增的预测依据,然后使用遗传算法对RNA二级结构进行交叉、变异和迭代,最后得到Pareto最优解,进一步得出最高的最大期望精度。实验结果表明,在使用的RNA案例中,采用该方法比现有方法精度平均提高约4%。