The article was removed by YellowAssessmentMonkey 00:47, 1 September 2009 [1].
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I am nominating this featured article for review because of 1c concerns. The article has been edited extensively since it passed FAC in July 2006 and there are whole sections that are unreferenced. It has been tagged for {{Refimprove|date=March 2009}} —Mattisse (Talk) 00:56, 2 August 2009 (UTC)[reply]
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. Instead, human knowledge is applied in constructing algorithms 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. A variety of computational algorithms have been applied to the sequence alignment problem, including slow but formally optimizing methods like dynamic programming, and efficient, but not as thorough heuristic algorithms or probabilistic methods designed for large-scale database search.
—Mattisse (Talk) 18:58, 2 August 2009 (UTC)[reply]