Sequence alignment is a fundamental bioinformatics technique used to compare and analyze the similarities and differences between DNA, RNA, or protein sequences. The goal of sequence alignment is to identify regions of similarity and divergence between sequences in order to understand their evolutionary relationships, functional similarities, and predict biological function. There are two main types of sequence alignment: global alignment and local alignment. Global alignment aligns the entire length of two sequences, while local alignment identifies regions of similarity within sequences that may be interrupted by gaps or mismatches. Sequence alignment algorithms, such as Needleman-Wunsch and Smith-Waterman, use scoring systems to assign values to matches, mismatches, and gaps in order to find the optimal alignment between sequences. These algorithms are widely used in fields such as genomics, evolutionary biology, and drug discovery to analyze biological data and make predictions about gene function, protein structure, and evolutionary relationships.