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Semi-supervised LC/MS alignment for differential proteomics


Fischer, B; Grossmann, J; Roth, V; Gruissem, W; Baginsky, S; Buhmann, J M (2006). Semi-supervised LC/MS alignment for differential proteomics. Bioinformatics, 22(14):e132-40.

Abstract

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.

Abstract

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.

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44 citations in Web of Science®
60 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Functional Genomics Center Zurich
08 University Research Priority Programs > Systems Biology / Functional Genomics
08 University Research Priority Programs > Systems Biology / Functional Genomics
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:15 July 2006
Deposited On:18 Dec 2009 08:33
Last Modified:06 Dec 2017 22:05
Publisher:Oxford University Press
ISSN:1367-4803
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/bioinformatics/btl219
PubMed ID:16873463

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