Editing Sequence alignment (section)

==Alignment methods==
Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Various algorithms were devised to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: ''global alignments'' and ''local alignments''. Calculating a global alignment is a form of [[global optimization]] that "forces" the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity.<ref name="Polyanovsky2011">{{Cite journal
 | pmid = 22032267
| year = 2011
| last1 = Polyanovsky
| first1 = V. O.
| title = Comparative analysis of the quality of a global algorithm and a local algorithm for alignment of two sequences
| journal = Algorithms for Molecular Biology
| volume = 6
| issue = 1
| page = 25
| last2 = Roytberg
| first2 = M. A.
| last3 = Tumanyan
| first3 = V. G.
| doi = 10.1186/1748-7188-6-25
| pmc = 3223492
| s2cid = 2658261
| doi-access = free
}}</ref> A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like [[dynamic programming]]. These also include efficient, [[heuristic algorithm]]s or [[probability|probabilistic]] methods designed for large-scale database search, that do not guarantee to find best matches.