RNA secondary structure prediction with coincidence algorithm

Supawadee Srikamdee, Warin Wattanapornprom, P. Chongstitvatana
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引用次数: 10

Abstract

The main function of RNA is to translate genetic information of DNA into proteins. Understanding structure of RNA helps understanding the mechanism of actions within the cell. It is widely believed that the functions of bimolecular are dictated by its structure. This paper employs a RNA primary structure as an input and predicts its secondary structure by finding all possible helices in a RNA sequence. The problem is modelled as a combinatorial optimization problem. The objective is to find the combination of helices that forms the RNA secondary structure that has minimum free energy calculated via Individual Nearest Neighbor Hydrogen Bond model (INN-HB). This paper proposes an effective evolutionary algorithm, Coincidence Algorithm, to solve this problem. The ten known RNA sequences, a variety of lengths and various different organisms, are used to test the proposed method in terms of specificity, sensitivity and F-measure. The results are compared with others heuristic methods: RnaPredict and SARNA-Predict. The experimental results show that, on average, the proposed method outperforms RnaPredict and SARNA-Predict in all measures.
基于重合算法的RNA二级结构预测
RNA的主要功能是将DNA的遗传信息翻译成蛋白质。了解RNA的结构有助于了解细胞内的作用机制。人们普遍认为,生物分子的功能是由其结构决定的。本文采用RNA一级结构作为输入,并通过寻找RNA序列中所有可能的螺旋来预测其二级结构。该问题被建模为一个组合优化问题。目的是找到通过最近邻氢键模型(INN-HB)计算的具有最小自由能的RNA二级结构的螺旋组合。本文提出了一种有效的进化算法——重合算法来解决这一问题。10个已知的RNA序列,不同的长度和不同的生物体,被用来测试所提出的方法的特异性,敏感性和F-measure。结果与其他启发式方法rnappredict和SARNA-Predict进行了比较。实验结果表明,平均而言,该方法在所有指标上都优于rnappredict和SARNA-Predict。
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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