PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios
1 MicroDiscovery GmbH, Marienburger Str. 1, 10405 Berlin, Germany
2 Functional Genomics Center, UNI ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
3 German Institute of Human Nutrition, Department of Pharmacology, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
4 German Diabetes-Center at the Heinrich-Heine-University Düsseldorf
5 Free University of Berlin, Department Computer Science and Mathematics, Berlin, Germany
BMC Bioinformatics 2012, 13:34 doi:10.1186/1471-2105-13-34Published: 16 February 2012
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.
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.
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.