About: Smith–Waterman algorithm

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The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings of nucleic acid sequences or protein sequences. Instead of looking at the entire sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.

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rdfs:label	Smith–Waterman algorithm (en)
rdfs:comment	The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings of nucleic acid sequences or protein sequences. Instead of looking at the entire sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure. (en)
sameAs	Smith–Waterman algorithm
dbp:wikiPageUsesTemplate	dbt:Citation_needed dbt:Mono dbt:Reflist dbt:Short_description dbt:Infobox_algorithm dbt:Bioinformatics dbt:Strings
Subject	Bioinformatics algorithms Computational phylogenetics Dynamic programming Sequence alignment algorithms
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Smith–Waterman_algorithm?oldid=1064412696&ns=0
Wikipage page ID	1606195 (xsd:integer)
page length (characters) of wiki page	36313 (xsd:nonNegativeInteger)
Wikipage revision ID	1064412696 (xsd:integer)
has abstract	The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings of nucleic acid sequences or protein sequences. Instead of looking at the entire sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure. The algorithm was first proposed by Temple F. Smith and Michael S. Waterman in 1981. Like the Needleman–Wunsch algorithm, of which it is a variation, Smith–Waterman is a dynamic programming algorithm. As such, it has the desirable property that it is guaranteed to find the optimal local alignment with respect to the scoring system being used (which includes the substitution matrix and the gap-scoring scheme). The main difference to the Needleman–Wunsch algorithm is that negative scoring matrix cells are set to zero, which renders the (thus positively scoring) local alignments visible. Traceback procedure starts at the highest scoring matrix cell and proceeds until a cell with score zero is encountered, yielding the highest scoring local alignment. Because of its quadratic complexity in time and space, it often cannot be practically applied to large-scale problems and is replaced in favor of less general but computationally more efficient alternatives such as (Gotoh, 1982), (Altschul and Erickson, 1986), and (Myers and Miller, 1988). (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Smith–Waterman_algorithm
is Wikipage redirect of	Smith-Waterman Smith-Waterman algorithm Local alignment Smith Waterman Smith Waterman algorithm Smith algorithm Smith waterman Smith–Waterman
is Link from a Wikipage to another Wikipage of	List of terms relating to algorithms and data structures 3-Base Periodicity Property Approximate string matching BFAST BLAST (biotechnology) BioJava CLC bio Gene prediction Jaro–Winkler distance Journal of Molecular Biology Damerau–Levenshtein distance List of protein tandem repeat annotation software List of sequence alignment software Dynamic programming Edit distance List of RNA-Seq bioinformatics tools List of algorithms List of alignment visualization software List of bioinformatics companies Michael Waterman Fiocruz Genome Comparison Project Local alignment FASTA HMMER Harmonic pitch class profiles JAligner Levenshtein distance Maximal unique match Multidimensional hierarchical toolkit Needleman–Wunsch algorithm Neutral network (evolution)
is known for of	Temple F. Smith Michael Waterman

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