Estimating the Contributions of Selection and Self-Organization in RNA Secondary Structure

In addition to characteristic structural properties imposed by evolutionary modification, evolved, single-stranded RNAs also display characteristic structural properties imposed by intrinsic physical constraints on RNA polymer folding. The balance of intrinsic and functionally selected characters in the folded conformation of evolved secondary structures was determined by comparing the predicted secondary structures of evolved and unevolved (random) RNA sequences. Though evolved conformations are significantly more ordered than conformations of random-sequence RNA, this analysis demonstrates that the majority of conformational order within evolved structures results not from evolutionary optimization but from constraints imposed by rules intrinsic to RNA polymer folding. Received: 25 November 1998 / Accepted: 12 February 1999     

Strong Eukaryotic IRESs Have Weak Secondary Structure

Background

The objective of this work was to investigate the hypothesis that eukaryotic Internal Ribosome Entry Sites (IRES) lack secondary structure and to examine the generality of the hypothesis.

Methodology/Principal Findings

IRESs of the yeast and the fruit fly are located in the 5′UTR immediately upstream of the initiation codon. The minimum folding energy (MFE) of 60 nt RNA segments immediately upstream of the initiation codons was calculated as a proxy of secondary structure stability. MFE of the reverse complements of these 60 nt segments was also calculated. The relationship between MFE and empirically determined IRES activity was investigated to test the hypothesis that strong IRES activity is associated with weak secondary structure. We show that IRES activity in the yeast and the fruit fly correlates strongly with the structural stability, with highest IRES activity found in RNA segments that exhibit the weakest secondary structure.

Conclusions

We found that a subset of eukaryotic IRESs exhibits very low secondary structure in the 5′-UTR sequences immediately upstream of the initiation codon. The consistency in results between the yeast and the fruit fly suggests a possible shared mechanism of cap-independent translation initiation that relies on an unstructured RNA segment.     

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。     

RNA-SSPT: RNA Secondary Structure Prediction Tools

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.     

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)。     

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.     

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。     

RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database

Background  

The ability to access, search and analyse secondary structures of a large set of known RNA molecules is very important for deriving improved RNA energy models, for evaluating computational predictions of RNA secondary structures and for a better understanding of RNA folding. Currently there is no database that can easily provide these capabilities for almost all RNA molecules with known secondary structures.     

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。     

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。     

Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction

Chemical and enzymatic footprinting experiments, such as shape (selective 2′-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the -hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be ‘correct’, in as much as the shape data is ‘correct’. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.     

基于局部茎搜索的RNA二级结构预测算法

RNA的二级结构预测是生物信息学中一个已经有30多年历史的经典问题,基于最小自由能模型(MFE)的优化算法是使用最为广泛的方法．但RNA结构中假结的存在使MFE问题理论上成为一个NP-hard问题,即使采用动态规划等优化算法也会面临时间复杂度高的困难,同时研究还发现,由于受RNA折叠动力学机制以及环境因素的影响,真实的RNA二级结构往往并不处于自由能最小状态．根据RNA折叠的特点,提出了一种启发式搜索算法来预测带假结的RNA二级结构．该算法以RNA的茎为基本单元,采用启发式搜索策略在茎的组合空间中搜索自由能最小并且出现频率最高的RNA二级结构,该算法不仅能显著降低搜索RNA二级结构的时间复杂度,还有助于弥补单纯依赖能量预测RNA二级结构的不足．在多种类型的RNA标准数据集上进行了检验,结果表明,该算法在预测的精度上优于目前国际上几个著名的RNA二级结构预测算法并且具有较高的运行效率．     

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。     

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.