Biological data sequencing is a complicated operation because of the nature of the data. Every living organism's DNA, RNA, and proteins can benefit greatly from the sequence alignment strategy. Accurate and fast alignment helps to find common evolutionary and structural relationship which in turn helps to design drugs. Even after years of using the sequence alignment approach, each algorithm produces a different solution for the same alignment problem. An additional issue with sequence alignment is that the algorithms and data it uses are computationally complex. Protein and DNA alignments can take days to analyze in some circumstances. Control of various sequence alignment scores is enhanced by several stochastic approaches. Multiple sequence alignment improves the quality and this task is made easier by the swarm intelligence method. An investigation on swarm intelligence-based methods for analyzing multiple sequence alignment is presented here. MATLAB software and a well-known protein dataset are used in the research procedure, which assesses the algorithm's time complexity and scoring.

Multiple Sequence Alignment, Particle Swarm Optimization, DNA, RNA and Swarm Intelligence