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Bioinformatics 2007 23(13):i273-i281; doi:10.1093/bioinformatics/btm209
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A geometric approach for the alignment of liquid chromatography—mass spectrometry data

Eva Lange 1,*, Clemens Gröpl 1, Ole Schulz-Trieglaff 1,2, Andreas Leinenbach 3, Christian Huber 3 and Knut Reinert 1

1Free University Berlin, Department of Mathematics and Computer Science 2Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany3Saarland University, Department of Chemistry, Instrumental Analysis and Bioanalysis, Saarbrücken

*To whom correspondence should be addressed.


  Abstract

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 www.openms.de

Contact: lange{at}inf.fu-berlin.de


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