Local alignment is a method used in bioinformatics to identify similar regions between two sequences, such as DNA, RNA, or proteins. Unlike global alignment, which compares entire sequences, local alignment focuses on finding the most similar subsequences. This is particularly useful when sequences have regions of high similarity interspersed with areas of divergence. The most common algorithm for local alignment is the Smith-Waterman algorithm, which uses dynamic programming to score and align segments of sequences. This approach helps researchers identify functional or evolutionary relationships between sequences, aiding in tasks like gene identification and protein function prediction.