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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >SARNA-Predict: Accuracy Improvement of RNA Secondary Structure Prediction Using Permutation-Based Simulated Annealing
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SARNA-Predict: Accuracy Improvement of RNA Secondary Structure Prediction Using Permutation-Based Simulated Annealing

机译：SARNA预测：使用基于置换的模拟退火提高RNA二级结构预测的准确性。

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摘要

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{hbox{-}}Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA{hbox{-}}Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot (pknotsRE), NUPACK, pknotsRG{hbox{-}}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{hbox{-}}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{hbox{-}}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).
机译：核糖核酸（RNA）是一种单链线性分子，对于所有生物系统都是必不可少的。同一RNA链的不同区域将通过碱基对相互作用折叠在一起，从而形成复杂的二级和三级结构，从而指导生物体内的关键稳态过程。由于RNA分子的结构是其功能的关键，因此预测RNA结构的算法具有重要的价值。在本文中，我们演示了SARNA {hbox {-}} Predict的有用性，这是一种基于模拟退火（SA）的RNA二级结构预测算法。通过与八种最新的RNA预测算法（mfold，假结（pknotsRE），NUPACK，pknotsRG {hbox {-}}）进行比较，对SARNA {hbox {-}}的性能进行了预测准确性的预测。 mfe，Sfold，HotKnots，ILM和STAR。这些算法来自三个不同的类别：启发式，动态编程和统计采样技术。使用天然结构验证了SARNA {hbox {-}} Predict在预测准确性方面的性能评估。对来自11种RNA类（tRNA，病毒RNA，反基因组HDV，端粒酶RNA，tmRNA，rRNA，RNaseP，5S rRNA，I组内含子23S rRNA，I组内含子16S rRNA和16S rRNA）的33种已知结构进行了实验。本文提供的结果表明，在预测准确性方面，SARNA {hbox {-}} Predict可以胜过其他最新算法。此外，通过合并更复杂的热力学模型（efn2），可以大大提高预测精度。

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