Construction and Optimization of Regular Expressions in Genetic Sequence Analysis

In this paper a novel approach for generating and optimizing regular expressions for genetic sequences is presented. The proposed technique introduces a systematic process to create initial regular expressions, compress and optimize them, and validate their accuracy for sequence matching. The method ensures a precise and reliable match with the given sequences while reducing complexity through an efficient optimization process. The proposed approach is versatile, being applicable to DNA, RNA, protein sequences, or any formal language processing. The method has been tested using a set of sequences with lengths ranging from 64 to 512 characters, with 50-100 pseudo-random generated sequence pairs and 5-10 % mutations for each case. Although the regular expression optimization process consumes slightly more time compared to conventional methods, it significantly reduces the overall matching time. Additionally, the optimized regular expressions show a reduction in memory usage. The results demonstrate that the proposed approach is highly effective for Motif Discovery and Representation, Pattern Matching Algorithms, Comparative Genomics and Phylogenetic and Gene Regulation, and Promoter Analysis, making it suitable for applications in bioinformatics and computational linguistics.