Sequence alignment: Difference between revisions

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Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. (This does not mean global alignments cannot start and/or end in gaps.) A general global alignment technique is the [[Needleman–Wunsch algorithm]], which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The [[Smith–Waterman algorithm]] is a general local alignment method based on the same dynamic programming scheme but with additional choices to start and end at any place.<ref name="Polyanovsky2011"/>

Hybrid methods, known as semi-global or "glocal" (short for '''glo'''bal-lo'''cal''') methods, search for the best possible partial alignment of the two sequences (in other words, a combination of one or both starts and one or both ends is stated to be aligned). This can be especially useful when the downstream part of one sequence overlaps with the upstream part of the other sequence. In this case, neither global nor local alignment is entirely appropriate: a global alignment would attempt to force the alignment to extend beyond the region of overlap, while a local alignment might not fully cover the region of overlap.<ref name=brudno>{{cite journal|author1=Brudno M |author2=Malde S |author3=Poliakov A |author4=Do CB |author5=Couronne O |author6=Dubchak I |author7=Batzoglou S | year=2003 | title=Glocal alignment: finding rearrangements during alignment | journal= Bioinformatics | volume=Suppl 1| issue=90001| pages=i54–62| series=19 | pmid = 12855437| doi = 10.1093/bioinformatics/btg1005 | doi-access=free }}</ref> Another case where semi-global alignment is useful is when one sequence is short (for example a gene sequence) and the other is very long (for example a chromosome sequence). In that case, the short sequence should be globally (fully) aligned but only a local (partial) alignment is desired for the long sequence.

Fast expansion of genetic data challenges speed of current DNA sequence alignment algorithms. Essential needs for an efficient and accurate method for DNA variant discovery demand innovative approaches for parallel processing in real time. [[Optical computing]] approaches have been suggested as promising alternatives to the current electrical implementations, yet their applicability remains to be tested [https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.201900227].