Title
A strategy for the prior processing of high-resolution mass spectral data obtained from high-dimensional combined fractional diagonal chromatography A strategy for the prior processing of high-resolution mass spectral data obtained from high-dimensional combined fractional diagonal chromatography
Author
Faculty/Department
Faculty of Sciences. Chemistry
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Chichester ,
Subject
Physics
Chemistry
Biology
Source (journal)
Journal of mass spectrometry. - Chichester
Volume/pages
44(2009) :4 , p. 516-529
ISSN
1076-5174
ISI
000265912000009
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
Combined fractional diagonal chromatography (COFRADIC) is a novel suite of gel-free technologies for the identification of biomarkers in complex peptide mixtures. For this purpose, reversed-phase high performance liquid chromatography (HPLC) technology and, in this case, matrix assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometers are extensively used. The particular characteristic of COFRADIC mass spectrometry data is the high number of chromatographic fractions, over which a peptide can be scattered. This can obstruct the quantification of the peptide abundance in the biological sample, which is required for statistical analysis. On the other hand, because of the superior peptide sorting properties of the methodology, the mass spectra become less crowded. Consequently, each peptide appears in a mass spectrum as a series of peaks with peak heights proportional to the probability of occurrence of the isotopic variants of the peptide. In this manuscript, we propose an analysis strategy concerned with the preprocessing of COFRADIC mass spectra prior to a downstream statistical analysis. The preprocessing algorithm produces for each mass spectrum a peptide list by exploiting the characteristic features that should be associated with peaks corresponding to an isotopically resolved cluster of peptide peaks. This reduction step is necessary to facilitate the clustering used in a next step to assemble the validated monoisotopic peptide peaks found over several fractions into a single peptide abundance. To assess the performance of the algorithm, two technical experiments were conducted. The proposed strategy is memory and computationally efficient. Copyright (C) 2008 John Wiley & Sons, Ltd.
E-info
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