title: PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios.
abstract: BACKGROUND
  Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins.
  
  
  RESULTS
  We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates.
  
  
  CONCLUSIONS
  Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.
authors.affiliation: MicroDiscovery GmbH, Marienburger Str, 1, 10405 Berlin, Germany. chris.bauer@microdiscovery.de
authors.forename: Chris
authors.initials: C
authors.lastname: Bauer
authors.forename: Frank
authors.initials: F
authors.lastname: Kleinjung
authors.forename: Dorothea
authors.initials: D
authors.lastname: Rutishauser
authors.forename: Christian
authors.initials: C
authors.lastname: Panse
authors.forename: Alexandra
authors.initials: A
authors.lastname: Chadt
authors.forename: Tanja
authors.initials: T
authors.lastname: Dreja
authors.forename: Hadi
authors.initials: H
authors.lastname: Al-Hasani
authors.forename: Knut
authors.initials: K
authors.lastname: Reinert
authors.forename: Ralph
authors.initials: R
authors.lastname: Schlapbach
authors.forename: Johannes
authors.initials: J
authors.lastname: Schuchhardt
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D020869:Gene Expression Profiling; D051379:Mice; D008820:Mice, Obese; D009765:Obesity / Q000235:genetics; D010455:Peptides / Q000032:analysis; D011506:Proteins / Q000032:analysis / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012269:Ribosomal Proteins / Q000032:analysis
chemical_list: D010455:Peptides; D011506:Proteins; D012269:Ribosomal Proteins; C040469:ribosomal protein S30