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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 | url=http://bioinformatics.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=12855437}}</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
==Pairwise alignment==
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