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When less can yield more – Computational preprocessing of MS/MS spectra for peptide identification
Author(s) -
Renard Bernhard Y.,
Kirchner Marc,
Monigatti Flavio,
Ivanov Alexander R.,
Rappsilber Juri,
Winter Dominic,
Steen Judith A. J.,
Hamprecht Fred A.,
Steen Hanno
Publication year - 2009
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200900326
Subject(s) - preprocessor , mascot , identification (biology) , spurious relationship , computer science , database search engine , software , mass spectrometry , proteomics , data pre processing , sensitivity (control systems) , mass spectrum , data mining , chemistry , artificial intelligence , chromatography , search engine , machine learning , biology , information retrieval , engineering , operating system , biochemistry , botany , electronic engineering , political science , gene , law
The effectiveness of database search algorithms, such as Mascot, Sequest and ProteinPilot is limited by the quality of the input spectra: spurious peaks in MS/MS spectra can jeopardize the correct identification of peptides or reduce their score significantly. Consequently, an efficient preprocessing of MS/MS spectra can increase the sensitivity of peptide identification at reduced file sizes and run time without compromising its specificity. We investigate the performance of 25 MS/MS preprocessing methods on various data sets and make software for improved preprocessing of mgf/dta-files freely available from http://hci.iwr.uni-heidelberg.de/mip/proteomics or http://www.childrenshospital.org/research/steenlab.