Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Sarah von Löhneysen, Thomas Spicher, Yuliia Varenyk, Hua-Ting Yao, Ronny Lorenz, Ivo Hofacker, Peter F Stadler
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引用次数: 0

Abstract

Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.

系统发育和化学探测信息作为 RNA 二级结构预测的软约束。
基于热力学的 RNA 折叠算法可以伪能的形式纳入外部实验信息。在系统发育相关序列的排列中可以检测到 RNA 二级结构元素的进化保守性,这为某些碱基对的存在提供了证据,而这些碱基对也可以转化为伪能量贡献。我们的研究表明,通过 RNAalifold 等共识折叠模型计算出的中心碱基对可大幅提高单序列的预测准确性。事实证明,对于配对状态的保持,特定碱基对的证据比位置轮廓更有参考价值。此外,与化学探针数据的比较也有力地表明,系统发育碱基配对数据比化学探针实验中获得的特定位置(非)配对数据更有参考价值。在这种情况下,我们还证明了可以使用热力学结构预测作为参考,而不是已知的 RNA 结构,将探测数据中的信号转换为伪能量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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