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Bioinformatics 2006 22(14):e132-e140; doi:10.1093/bioinformatics/btl219
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
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Semi-supervised LC/MS alignment for differential proteomics

Bernd Fischer 1,*, Jonas Grossmann 2, Volker Roth 1, Wilhelm Gruissem 2, Sacha Baginsky 2 and Joachim M. Buhmann 1

1 Institute of Computational Science ETH Zurich, Switzerland
2 Institute of Plant Sciences ETH Zurich, Switzerland

*To whom correspondence should be addressed.

Motivation: Mass spectrometry (MS) combined with high-performance liquid chromatography (LC) has received considerable attention for high-throughput analysis of proteomes. Isotopic labeling techniques such as ICAT [5,6] have been successfully applied to derive differential quantitative information for two protein samples, however at the price of significantly increased complexity of the experimental setup. To overcome these limitations, we consider a label-free setting where correspondences between elements of two samples have to be established prior to the comparative analysis. The alignment between samples is achieved by nonlinear robust ridge regression. The correspondence estimates are guided in a semi-supervised fashion by prior information which is derived from sequenced tandem mass spectra.

Results: The semi-supervised method for finding correspondences was successfully applied to aligning highly complex protein samples, even if they exhibit large variations due to different biological conditions. A large-scale experiment clearly demonstrates that the proposed method bridges the gap between statistical data analysis and label-free quantitative differential proteomics.

Availability: The software will be available on the website http://people.inf.ethz.ch/befische/proteomics

Contact: bernd.fischer{at}inf.ethz.ch


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