A Statistical Comparison of SimTandem with State-of-the-Art Peptide Identification Tools
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10139043" target="_blank" >RIV/00216208:11320/13:10139043 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-00578-2_14" target="_blank" >http://dx.doi.org/10.1007/978-3-319-00578-2_14</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-00578-2_14" target="_blank" >10.1007/978-3-319-00578-2_14</a>
Alternative languages
Result language
angličtina
Original language name
A Statistical Comparison of SimTandem with State-of-the-Art Peptide Identification Tools
Original language description
The similarity search in theoretical mass spectra generated from protein sequence databases is a widely accepted approach for identification of peptides from query mass spectra generated by shotgun proteomics. Since query spectra contain many inaccuracies and the sizes of databases grow rapidly in recent years, demands on more accurate mass spectra similarities and on the utilization of database indexing techniques are still desirable. We propose a statistical comparison of parameterized Hausdorff distance with freely available tools OMSSA, X!Tandem and with the cosine similarity. We show that a precursor mass filter in combination with a modification of previously proposed parameterized Hausdorff distance outperforms state-of-the-art tools in both - the speed of search and the number of identified peptide sequences (even though the q-value is only 0.001). Our method is implemented in the freely available application SimTandem which can be used in the framework TOPP based on OpenMS.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP202%2F11%2F0968" target="_blank" >GAP202/11/0968: Large-scale Nonmetric Similarity Search in Complex Domains</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Advances in Intelligent Systems and Computing
ISSN
1867-5662
e-ISSN
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Volume of the periodical
2013
Issue of the periodical within the volume
222
Country of publishing house
DE - GERMANY
Number of pages
9
Pages from-to
101-109
UT code for WoS article
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EID of the result in the Scopus database
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