Sequence Alignment

Sequence alignment finds correspondences between ordered items, such as protein residues, words, musical notes, or time-series measurements, even when sequences differ in length or contain errors. Classic dynamic programming methods such as Needleman–Wunsch and Smith–Waterman handle global and local matches, while edit distance, multiple-sequence alignment, and dynamic time warping address other comparison settings. Newer approaches use differentiable or context-aware alignment, learned embeddings, and uncertainty estimates, with applications evaluated on protein structure and function tasks, speech recognition, and time-series benchmarks such as UCR. A central challenge is balancing efficient, interpretable matching with robustness to noise and meaningful variation, since the best alignment may depend on context rather than exact symbol matches alone.