title: Semi-supervised LC/MS alignment for differential proteomics.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The software will be available on the website http://people.inf.ethz.ch/befische/proteomics.
authors.affiliation: Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch
authors.forename: Bernd
authors.initials: B
authors.lastname: Fischer
authors.forename: Jonas
authors.initials: J
authors.lastname: Grossmann
authors.forename: Volker
authors.initials: V
authors.lastname: Roth
authors.forename: Wilhelm
authors.initials: W
authors.lastname: Gruissem
authors.forename: Sacha
authors.initials: S
authors.lastname: Baginsky
authors.forename: Joachim M
authors.initials: JM
authors.lastname: Buhmann
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D002853:Chromatography, Liquid / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D010449:Peptide Mapping / Q000379:methods*; D020543:Proteome / Q000032:analysis* / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D020543:Proteome