Zhu Qi
A Bayesian Model Averaging Approach to the Quantification of Overlapping Peptides in an MALDI-TOF Mass Spectrum
Qi, Zhu; Kasim, Adetayo; Dirk, Valkenborg; Tomasz, Burzykowski
Authors
Adetayo Kasim
Valkenborg Dirk
Burzykowski Tomasz
Abstract
In a high-resolution MALDI-TOF mass spectrum, a peptide produces multiple peaks, corresponding to the isotopic variants of the molecules. An overlap occurs when two peptides appear in the vicinity of the mass coordinate, resulting in the difficulty of quantifying the relative abundance and the exact masses of these peptides. To address the problem, two factors need to be considered: (1) the variability pertaining to the abundances of the isotopic variants (2) extra information content needed to supplement the information contained in data. We propose a Bayesian model for the incorporation of prior information. Such information exists, for example, for the distribution of the masses of peptides and the abundances of the isotopic variants. The model we develop allows for the correct estimation of the parameters of interest. The validity of the modeling approach is verified by a real-life case study from a controlled mass spectrometry experiment and by a simulation study.
Citation
Qi, Z., Kasim, A., Dirk, V., & Tomasz, B. (2011). A Bayesian Model Averaging Approach to the Quantification of Overlapping Peptides in an MALDI-TOF Mass Spectrum. International Journal of Proteomics, 2011, Article 928391. https://doi.org/10.1155/2011/928391
Journal Article Type | Article |
---|---|
Publication Date | Jan 1, 2011 |
Deposit Date | Nov 23, 2011 |
Publicly Available Date | Jun 6, 2012 |
Journal | International Journal of Proteomics |
Print ISSN | 2090-2166 |
Electronic ISSN | 2090-2174 |
Publisher | Hindawi |
Peer Reviewed | Peer Reviewed |
Volume | 2011 |
Article Number | 928391 |
DOI | https://doi.org/10.1155/2011/928391 |
Public URL | https://durham-repository.worktribe.com/output/1525346 |
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Copyright Statement
Copyright © 2011 Qi Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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