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Journal of Biomedicine and Biotechnology
Volume 2010, Article ID 131505, 9 pages
http://dx.doi.org/10.1155/2010/131505
Research Article

Improved Label-Free LC-MS Analysis by Wavelet-Based Noise Rejection

1Department of Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
2Department of Pharmaceutical Science, University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
3Department of Immunotechnology, Lund University, BMC D13, 221 84 Lund, Sweden
4Ophthalmology Unit, University of Bologna S.Orsola-Malpighi Hospital, Via Massarenti 9, 40138 Bologna, Italy

Received 10 July 2009; Revised 25 September 2009; Accepted 29 October 2009

Academic Editor: Kai Tang

Copyright © 2010 Salvatore Cappadona 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.

Abstract

Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects the results of all downstream analyses. In this paper we show how the inclusion of a preprocessing step of background subtraction in a common laboratory pipeline can lead to an enhanced inclusion list of peptides selected for fragmentation and consequently to better protein identification.