Title
A Markov-chain model for the analysis of high-resolution enzymatically O-18-labeled mass spectra A Markov-chain model for the analysis of high-resolution enzymatically O-18-labeled mass spectra
Author
Faculty/Department
Faculty of Sciences. Chemistry
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
[Berkeley, CA] :Berkeley Electronic Press ,
Subject
Mathematics
Chemistry
Biology
Computer. Automation
Source (journal)
Statistical applications in genetics and molecular biology. - [Berkeley, CA], 2002, currens
Volume/pages
10(2011) :1 , 38 p.
ISSN
1544-6115
1544-6115
Article Reference
1
Carrier
E-only publicatie
Target language
English (eng)
Full text (Publishers DOI)
Abstract
The enzymatic O-18-labeling is a useful quantification technique to account for between-spectrum variability of the results of mass spectrometry experiments. One of the important issues related to the use of the technique is the problem of incomplete labeling of peptide molecules, which may result in biased estimates of the relative peptide abundance. In this manuscript, we propose a Markov-chain model, which takes into account the possibility of incomplete labeling in the estimation of the relative abundance from the observed data. This allows for the use of less precise but faster labeling strategies, which should better fit in the high-throughput proteomic framework. Our method does not require extra experimental steps, as proposed in the approaches developed by Mirgorodskaya et al. (2000), Lopez-Ferrer et al. (2006) and Rao et al. (2005), while it includes the model proposed by Eckel-Passow et al. (2006) as a special case. The method estimates information about the isotopic distribution directly from the observed data and is able to account for biases induced by the different sulphur content in peptides as reported by Johnson and Muddiman (2004). The method is integrated in a statistically sound framework and allows for the calculation of the errors on the parameter estimates based on model theory. In this manuscript, we describe the methodology in a technical matter and assess the properties of the algorithm via a thorough simulation study. The method is also tested on a limited dataset; more intense validation and investigation of the operational characteristics is being scheduled.
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