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RNAknot2S: a New RNA Secondary Structure Prediction Method that Combines the Comparative Approach with Genetic Algorithms


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DOI: https://doi.org/10.15866/ireaco.v16i6.24279

Abstract


The RNA secondary structure is one of the extensively explored subjects in bioinformatics due to its pivotal involvement in various biological processes. Several methods have been developed for predicting the optimal secondary structure of a given RNA sequence. However, only a limited number of these methods have produced satisfactory results, especially when dealing with complex secondary structure components such as pseudoknot classes. This paper introduces RNAknot2S, a novel program for RNA secondary structure prediction. RNAknot2S is based on the genetic algorithm and combines two main approaches, the comparative approach and the thermodynamic approach. It takes three inputs,  the first is the target RNA sequence (S1) for which the secondary structure is to be determined, the second is another RNA sequence (S2) expected to be homologous to S1, and the third element is the secondary structure (Ss) of the sequence S2 presented in dot-bracket format. RNAknot2S produces as output a secondary structure that includes various components such as stems, hairpin loops, multi-branched loops or multi-loops, bulge loops, internal loops, H-type pseudoknots, and hairpin kissing interactions. In order to evaluate the performance of the proposed algorithm, a comparative study has been performed using some of the most popular methods for RNA secondary structure prediction and the database Rfam. The obtained results clearly demonstrate a significant enhancement in the prediction accuracy of the proposed method when compared to other existing programs. RNAknot2S exhibits the highest average values for specificity (SP) at 55.22%, sensitivity (SN) at 58.29%, and F-measure (F-M) at 55.59% when compared to its counterparts.
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Keywords


Ribonucleic Acid (RNA); Bioinformatics; RNA Secondary Structure; Genetic Algorithm; Thermodynamic Approach; Comparative Approach

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References


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