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dc.contributor.author
Bauer, Chris
dc.contributor.author
Kleinjung, Frank
dc.contributor.author
Rutishauser, Dorothea
dc.contributor.author
Panse, Christian
dc.contributor.author
Chadt, Alexandra
dc.contributor.author
Dreja, Tanja
dc.contributor.author
Al-Hasani, Hadi
dc.contributor.author
Reinert, Knut
dc.contributor.author
Schlapbach, Ralph
dc.contributor.author
Schuchhardt, Johannes
dc.date.accessioned
2018-09-03T09:34:49Z
dc.date.available
2017-06-10T01:13:43Z
dc.date.available
2018-09-03T09:34:49Z
dc.date.issued
2012-02
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/1471-2105-13-34
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/49784
dc.identifier.doi
10.3929/ethz-b-000049784
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Protein Inference
en_US
dc.subject
Unique Peptide
en_US
dc.subject
Quantitative Proteomics
en_US
dc.subject
Quantitation Ratio
en_US
dc.subject
Peptide Profile
en_US
dc.title
PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
13
en_US
ethz.pages.start
34
en_US
ethz.size
12 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
004240301
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02207 - Functional Genomics Center Zurich / Functional Genomics Center Zurich
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::08828 - Schlapbach, Ralph (Tit.-Prof.)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02207 - Functional Genomics Center Zurich / Functional Genomics Center Zurich
ethz.date.deposited
2017-06-10T01:16:20Z
ethz.source
ECIT
ethz.identifier.importid
imp59364f44f1b7f51209
ethz.ecitpid
pub:81766
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-13T10:36:36Z
ethz.rosetta.lastUpdated
2021-02-15T01:30:06Z
ethz.rosetta.versionExported
true
ethz.COinS
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