A multi-objective fuzzy model based on enhanced artificial fish Swarm for multiple RNA sequences alignment

Ribonucleic Acid (RNA) sequence alignment is a fundamental operation in bioinformatics, essential for analyzing the physicochemical and functional characteristics of RNA molecules. Traditional cross-alignment methods have significant challenges, particularly in optimizing multiple objectives during RNA sequencing. One of the biggest challenges is working to balance speed and accuracy. Fast methods are accompanied by low accuracy, unlike accurate methods which take a long computational time. Consequently, the alignment task becomes increasingly
difficult as the number of RNA sequences grows, requiring tools that adequately handle these conflicting targets. To address these challenges, this study proposes an Enhanced Artificial Fish Swarm Algorithm (EAFSA) integrated with a fuzzy multi-objective model specifically designed for multiple RNA sequence alignment. The proposed EAFSA approach offers various advantages including significantly increased alignment accuracy, preservation of sequence integrity and the ability to search for similar fragments efficiently and quickly while reducing computational costs. Experimental comparisons of the proposed EAFSA with other relevant state-of-theart alignment tools on benchmark RNA datasets demonstrate the efficiency of the proposed method. The efficiency is also proved by various metrics such as alignment score analysis, time complexity, and accuracy. This work demonstrates the potential of the proposed EAFSA to enhance RNA sequence alignment methods, facilitating additional biological interpretations through sequence alignment applications in genomics.