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dc.contributor.author
Bitton, Danny A.
dc.contributor.author
Okoniewski, Michał J.
dc.contributor.author
Connolly, Yvonne
dc.contributor.author
Miller, Crispin J.
dc.date.accessioned
2018-09-03T14:05:56Z
dc.date.available
2017-06-08T21:26:10Z
dc.date.available
2018-09-03T14:04:55Z
dc.date.available
2018-09-03T14:05:56Z
dc.date.issued
2008-02
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/1471-2105-9-118
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/14608
dc.identifier.doi
10.3929/ethz-b-000014608
dc.description.abstract
Background Previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. We hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing. Results Using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with Affymetrix Exon array data at the level of individual exons. We found significantly higher degrees of correlation than have been previously observed (r = 0.808). The study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance. Conclusion We conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. The approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome.
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
Proteomics Data
en_US
dc.subject
Log2 Fold Change
en_US
dc.subject
Exon Array
en_US
dc.subject
Transcript Location
en_US
dc.subject
Reporter Group
en_US
dc.title
Exon level integration of proteomics and microarray data
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
9
en_US
ethz.pages.start
118
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
publishedVersion
en_US
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.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
2017-06-08T21:26:25Z
ethz.source
ECIT
ethz.identifier.importid
imp59364c41e6d2197633
ethz.ecitpid
pub:26264
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-26T18:40:50Z
ethz.rosetta.lastUpdated
2021-02-15T01:30:37Z
ethz.rosetta.versionExported
true
ethz.COinS
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