The prediction of the optimal secondary structure for a given RNA sequence represents a challenging computational problem in bioinformatics. This challenge becomes harder especially with the discovery of different pseudoknot classes, which is a complex topology that plays diverse roles in biological processes. Many recent studies have been proposed to predict RNA secondary structure with some pseudoknot classes, but only a few of them have reached satisfying results in terms of both complexity and accuracy. Here we present RNAknot, a new method for predicting RNA secondary structure that contains the following components: stems, hairpin loops, multi-branched loops or multi-loops, bulge loops, and internal loops, in addition to two types of pseudoknots, H-type pseudoknot and Hairpin kissing. RNAknot is based on a genetic algorithm and Greedy Randomized Adaptive Search Procedure (GRASP), and it uses the free energy as fitness function to evaluate the obtained structures.In order to validate the performance of the presented method 131 tests have been performed using two datasets of 26 and 105 RNA sequences, which have been taken from the two data bases RNAstrand and Pseudobase respectively. The obtained results are compared with those of some RNA secondary structure prediction programs such as Vs_subopt, CyloFold, IPknot, Kinefold, RNAstructure, and Sfold. The results of this comparative study show that the prediction accuracy of our proposed approach is significantly improved compared to those obtained by the other programs. For the first dataset, RNAknot has the highest specificity (SP) (71.23%) and sensitivity (SN) (72.15%) averages compared to the other programs. Concerning the second dataset, the RNA secondary structure predictions obtained by the RNAknot correspond to the highest averages of SP (85.49%) and F-measure (79.97%) compared to the other programs. The program is available as a jar file in the link: www.bachmek.umi.ac.ma/wp-content/uploads/RNAknot.0.0.2.rar .

中文翻译：

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RNAknot：一种基于遗传算法和GRASP方法的RNA二级结构预测新算法

给定 RNA 序列的最佳二级结构的预测代表了生物信息学中具有挑战性的计算问题。这一挑战变得更加困难，特别是随着不同假结类的发现，这是一种复杂的拓扑结构，在生物过程中扮演着不同的角色。最近许多研究已经提出用一些假结类来预测 RNA 二级结构，但其中只有少数在复杂性和准确性方面取得了令人满意的结果。在这里，我们提出 RNAknot，一种预测 RNA 二级结构的新方法，它包含以下组件：茎、发夹环、多分支环或多环、凸起环和内部环，以及两种类型的假结，H-类型假结和发夹接吻。RNAknot 基于遗传算法和贪婪随机自适应搜索程序 (GRASP)，它使用自由能作为适应度函数来评估获得的结构。为了验证所提出方法的性能，使用两个数据集进行了 131 次测试分别取自两个数据库 RNAstrand 和 Pseudobase 的 26 和 105 个 RNA 序列。将所得结果与一些 RNA 二级结构预测程序如 Vs_subopt、CyloFold、IPknot、Kinefold、RNAstructure 和 Sfold 的结果进行比较。这项比较研究的结果表明，与其他程序获得的预测精度相比，我们提出的方法的预测精度显着提高。对于第一个数据集，RNAknot 具有最高的特异性 (SP) (71.23%) 和灵敏度 (SN) (72. 15%) 与其他程序相比的平均值。关于第二个数据集，与其他程序相比，RNAknot 获得的 RNA 二级结构预测对应于 SP（85.49%）和 F-measure（79.97%）的最高平均值。该程序在链接中以 jar 文件的形式提供：www.bachmek.umi.ac.ma/wp-content/uploads/RNAknot.0.0.2.rar。