On Optimizing the Non-metric Similarity Search in Tandem Mass Spectra by Clustering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10124006" target="_blank" >RIV/00216208:11320/12:10124006 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-30191-9_18" target="_blank" >http://dx.doi.org/10.1007/978-3-642-30191-9_18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-30191-9_18" target="_blank" >10.1007/978-3-642-30191-9_18</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Optimizing the Non-metric Similarity Search in Tandem Mass Spectra by Clustering

Popis výsledku v původním jazyce

Tandem mass spectrometry is a well-known technique for identification of protein sequences from an "in vitro" 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 databasesof 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 upthe 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 precis

Název v anglickém jazyce

On Optimizing the Non-metric Similarity Search in Tandem Mass Spectra by Clustering

Popis výsledku anglicky

Tandem mass spectrometry is a well-known technique for identification of protein sequences from an "in vitro" 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 databasesof 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 upthe 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 precis

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

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Svazek periodika

2012

Číslo periodika v rámci svazku

7292

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

12

Strana od-do

189-200

Kód UT WoS článku

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EID výsledku v databázi Scopus

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