Global and Local Alignments
See also:
Sequence alignments are either local or global. Local alignments find the best matching segments between two sequences and form the basis of the database search programs. Global alignments find the best match over the total length of both sequences. Alignments may be performed on a pairwise basis, across multiple sequences or they can involve the alignment of a sequence to a previously aligned set of sequences (sometimes called a profile).
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 end in gaps.) A general global alignment technique is
called the Needleman-Wunsch algorithm
and 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 also based on dynamic programming.
With sufficiently similar sequences, there is no difference between
local and global alignments.
Hybrid methods, known as semiglobal or "glocal" methods, attempt to
find the best possible alignment that includes the start and end of one
or the other sequence. 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.[2]
This article is licensed under the GNU Free Documentation License. It uses material from Wikipedia Encyclopedia article "Sequence Alignment"
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