Evidence map›Paper›PMID 22340093›Full record

ArticleBMC bioinformatics2012

PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios.

Chris Bauer, Frank Kleinjung, Dorothea Rutishauser, Christian Panse, Alexandra Chadt, Tanja Dreja, Hadi Al-Hasani, Knut Reinert, Ralph Schlapbach, Johannes Schuchhardt

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Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Chris BauerMicroDiscovery GmbH, Marienburger Str, 1, 10405 Berlin, Germany. chris.bauer@microdiscovery.de
Frank Kleinjung
Dorothea Rutishauser
Christian Panse
Alexandra Chadt
Tanja Dreja
Hadi Al-Hasani
Knut Reinert
Ralph Schlapbach
Johannes Schuchhardt

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecent 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.

resultsWe 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.

conclusionsRegarding 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.

Indexed as

AnimalsCluster AnalysisDiabetes Mellitus, Type 2Gene Expression ProfilingMiceMice, ObeseObesityPeptidesProteinsProteomicsReproducibility of ResultsRibosomal ProteinsPeptidesProteinsRibosomal Proteinsribosomal protein S30

Identifiers

PMID22340093
PMCPMC3368728

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.