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SIMARD: A simulated annealing based RNA design algorithm with quality pre-selection strategies

机译:SIMARD:具有质量预选策略的基于模拟退火的RNA设计算法

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Most of the biological processes including expression levels of genes and translation of DNA to produce proteins within cells depend on RNA sequences, and the structure of the RNA plays vital role for its function. RNA design problem refers to the design of an RNA sequence that folds into given secondary structure. However, vast number of possible nucleotide combinations make this an NP-Hard problem. To solve the RNA design problem, a number of researchers have tried to implement algorithms using local stochastic search, context-free grammars, global sampling or evolutionary programming approaches. In this paper, we examine SIMARD, an RNA design algorithm that implements simulated annealing techniques. We also propose QPS, a mutation operator for SIMARD that pre-selects high quality sequences. Furthermore, we present experiment results of SIMARD compared to eight other RNA design algorithms using the Rfam datset. The experiment results indicate that SIMARD shows promising results in terms of Hamming distance between designed sequence and the target structure, and outperforms ERD in terms of free energy.
机译:大多数生物过程,包括基因的表达水平和DNA的翻译,以在细胞内产生蛋白质,都取决于RNA序列,而RNA的结构对其功能起着至关重要的作用。 RNA设计问题是指折叠成给定二级结构的RNA序列的设计。然而,大量可能的核苷酸组合使之成为NP-Hard问题。为了解决RNA设计问题,许多研究人员尝试使用局部随机搜索,无上下文语法,全局采样或进化编程方法来实现算法。在本文中,我们研究了SIMARD,一种实现模拟退火技术的RNA设计算法。我们还提出了QPS,一种SIMARD的突变算子,它可以预先选择高质量的序列。此外,与使用Rfam数据集的其他八种RNA设计算法相比,我们提供了SIMARD的实验结果。实验结果表明,SIMARD在设计序列与目标结构之间的汉明距离方面显示出令人鼓舞的结果,并且在自由能方面优于ERD。

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