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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F07%3A00206200%21RIV10-GA0-11320___
rdf:type
skos:Concept n21:Vysledek
dcterms:description
When searching databases of nucleotide or protein sequences, finding a local alignment of two sequences is one of the main tasks. Since the sizes of available databases grow constantly, the efficiency of retrieval methods becomes the critical issue. The sequence retrieval relies on finding sequences in the database which align best with the query sequence. However, an optimal alignment can be found in quadratic time (by use of dynamic programming) while this is infeasible when dealing with large databases. The existing solutions use fast heuristic methods (like BLAST, FASTA) which produce only an uncontrolled approximation of the best alignment and even do not provide any information about the alignment approximation error. In this paper we propose an approach of exact and approximate indexing using several metric access methods (MAMs) in combination with the TriGen algorithm, in order to reduce the number of alignments (distance computations) needed. When searching databases of nucleotide or protein sequences, finding a local alignment of two sequences is one of the main tasks. Since the sizes of available databases grow constantly, the efficiency of retrieval methods becomes the critical issue. The sequence retrieval relies on finding sequences in the database which align best with the query sequence. However, an optimal alignment can be found in quadratic time (by use of dynamic programming) while this is infeasible when dealing with large databases. The existing solutions use fast heuristic methods (like BLAST, FASTA) which produce only an uncontrolled approximation of the best alignment and even do not provide any information about the alignment approximation error. In this paper we propose an approach of exact and approximate indexing using several metric access methods (MAMs) in combination with the TriGen algorithm, in order to reduce the number of alignments (distance computations) needed.
dcterms:title
Index-based approach to similarity search in protein and nucleotide databases Index-based approach to similarity search in protein and nucleotide databases
skos:prefLabel
Index-based approach to similarity search in protein and nucleotide databases Index-based approach to similarity search in protein and nucleotide databases
skos:notation
RIV/00216208:11320/07:00206200!RIV10-GA0-11320___
n3:aktivita
n16:P n16:Z
n3:aktivity
P(GP201/05/P036), Z(MSM0021620838)
n3:dodaniDat
n5:2010
n3:domaciTvurceVysledku
n6:5851726 n6:9268421
n3:druhVysledku
n13:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
425966
n3:idVysledku
RIV/00216208:11320/07:00206200
n3:jazykVysledku
n10:eng
n3:klicovaSlova
Index-based; approach; similarity; search; protein; nucleotide; databases
n3:klicoveSlovo
n4:databases n4:approach n4:similarity n4:nucleotide n4:search n4:protein n4:Index-based
n3:kontrolniKodProRIV
[52B44C12DADA]
n3:mistoKonaniAkce
Neuveden
n3:nazevZdroje
DATESO 2007
n3:obor
n18:JC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n15:GP201%2F05%2FP036
n3:rokUplatneniVysledku
n5:2007
n3:tvurceVysledku
Hoksza, David Skopal, Tomáš
n3:typAkce
n8:EUR
n3:wos
000272455400007
n3:zahajeniAkce
2007-01-01+01:00
n3:zamer
n7:MSM0021620838
s:numberOfPages
14
n20:hasPublisher
Matfyz Press, Praha
n22:isbn
978-80-7378-002-9
n14:organizacniJednotka
11320