Repository: Freie Universität Berlin, Math Department

A Geometric Approach for the Alignment of Liquid Chromatography-Mass Spectrometry Data

Lange, E. and Gröpl, C. and Schulz-Trieglaff, O. and Leinenbach, A. and Huber, Ch. and Reinert, K. (2007) A Geometric Approach for the Alignment of Liquid Chromatography-Mass Spectrometry Data. Oxford Journals, 23 (13). i273-i281. ISSN 1460-2059 (online), 1367-4803 (print)

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Motivation: Liquid chromatography coupled to mass spectrometry (LC-MS) and combined with tandem mass spectrometry (LC-MS/MS) have become a prominent tool for the analysis of complex proteomic samples. An important step in a typical workflow is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from different samples. To do so, a suitable mapping or alignment between the data sets needs to be estimated. The alignment has to correct for variations in mass and elution time which are present in all mass spectrometry experiments. Results: We propose a novel algorithm to align LC-MS samples and to match corresponding ion species across samples. Our algorithm matches landmark signals between two data sets using a geometric technique based on pose clustering. Variations in mass and retention time are corrected by an affine dewarping function estimated from matched landmarks. We use the pairwise dewarping in an algorithm for aligning multiple samples. We show that our pose clustering approach is fast and reliable as compared to previous approaches. It is robust in the presence of noise and able to accurately align samples with only few common ion species. In addition, we can easily handle different kinds of LC-MS data and adopt our algorithm to new mass spectrometry technologies. Availability: This algorithm is implemented as part of the OpenMS software library for shotgun proteomics and available under the Lesser GNU Public License (LGPL) at

Item Type:Article
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:1133
Deposited By: Anja Kasseckert
Deposited On:10 Apr 2012 14:34
Last Modified:10 Apr 2012 14:34

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