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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F12%3A10124006%21RIV13-GA0-11320___
rdf:type
n18:Vysledek skos:Concept
rdfs:seeAlso
http://dx.doi.org/10.1007/978-3-642-30191-9_18
dcterms:description
Tandem mass spectrometry is a well-known technique for identification of protein sequences from an %22in vitro%22 sample. To identify the sequences from spectra captured by a spectrometer, the similarity search in a database of hypothetical mass spectra is often used. For this purpose, a database of known protein sequences is utilized to generate the hypothetical spectra. Since the number of sequences in the databases grows rapidly over the time, several approaches have been proposed to index the databases of mass spectra. In this paper, we improve an approach based on the non-metric similarity search where the M-tree and the TriGen algorithm are employed for fast and approximative search. We show that preprocessing of mass spectra by clustering speeds up the identification of sequences more than 100x with respect to the sequential scan of the entire database. Moreover, when the protein candidates are refined by sequential scan in the postprocessing step, the whole approach exhibits precision similar to that of sequential scan over the entire database (over 90%). Tandem mass spectrometry is a well-known technique for identification of protein sequences from an %22in vitro%22 sample. To identify the sequences from spectra captured by a spectrometer, the similarity search in a database of hypothetical mass spectra is often used. For this purpose, a database of known protein sequences is utilized to generate the hypothetical spectra. Since the number of sequences in the databases grows rapidly over the time, several approaches have been proposed to index the databases of mass spectra. In this paper, we improve an approach based on the non-metric similarity search where the M-tree and the TriGen algorithm are employed for fast and approximative search. We show that preprocessing of mass spectra by clustering speeds up the identification of sequences more than 100x with respect to the sequential scan of the entire database. Moreover, when the protein candidates are refined by sequential scan in the postprocessing step, the whole approach exhibits precision similar to that of sequential scan over the entire database (over 90%).
dcterms:title
On Optimizing the Non-metric Similarity Search in Tandem Mass Spectra by Clustering On Optimizing the Non-metric Similarity Search in Tandem Mass Spectra by Clustering
skos:prefLabel
On Optimizing the Non-metric Similarity Search in Tandem Mass Spectra by Clustering On Optimizing the Non-metric Similarity Search in Tandem Mass Spectra by Clustering
skos:notation
RIV/00216208:11320/12:10124006!RIV13-GA0-11320___
n18:predkladatel
n19:orjk%3A11320
n3:aktivita
n16:S n16:P
n3:aktivity
P(GAP202/11/0968), P(GD201/09/H057), P(GPP202/12/P297), S
n3:cisloPeriodika
7292
n3:dodaniDat
n6:2013
n3:domaciTvurceVysledku
n9:3885364 n9:9268421 n9:1536311 n9:5851726
n3:druhVysledku
n15:J
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
156288
n3:idVysledku
RIV/00216208:11320/12:10124006
n3:jazykVysledku
n12:eng
n3:klicovaSlova
spectral clustering; protein sequences identification; non-metric access methods; similarity search; tandem mass spectrometry
n3:klicoveSlovo
n10:tandem%20mass%20spectrometry n10:protein%20sequences%20identification n10:non-metric%20access%20methods n10:similarity%20search n10:spectral%20clustering
n3:kodStatuVydavatele
DE - Spolková republika Německo
n3:kontrolniKodProRIV
[883758329391]
n3:nazevZdroje
Lecture Notes in Computer Science
n3:obor
n5:IN
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:projekt
n11:GAP202%2F11%2F0968 n11:GD201%2F09%2FH057 n11:GPP202%2F12%2FP297
n3:rokUplatneniVysledku
n6:2012
n3:svazekPeriodika
2012
n3:tvurceVysledku
Skopal, Tomáš Novák, Jiří Hoksza, David Lokoč, Jakub
s:issn
0302-9743
s:numberOfPages
12
n14:doi
10.1007/978-3-642-30191-9_18
n17:organizacniJednotka
11320