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.
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.
Authors: Faviel F Gonzalez-Galarza; Craig Lawless; Simon J Hubbard; Jun Fan; Conrad Bessant; Henning Hermjakob; Andrew R Jones Journal: OMICS Date: 2012-07-17
Authors: Keith Richardson; Richard Denny; Chris Hughes; John Skilling; Jacek Sikora; Michał Dadlez; Angel Manteca; Hye Ryung Jung; Ole Nørregaard Jensen; Virginie Redeker; Ronald Melki; James I Langridge; Johannes P C Vissers Journal: OMICS Date: 2012-08-07