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Irmler, M. ; Hartl, D.* ; Schmidt, T.* ; Schuchhardt, J.* ; Lach, C. ; Meyer, H.E.* ; Hrabě de Angelis, M. ; Klose, J.* ; Beckers, J.

An approach to handling and interpretation of ambiguous data in transcriptome and proteome comparisons.

Proteomics 8, 1165-1169 (2008)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
A major challenge towards a comprehensive analysis of biological systems is the integration of data from different omics sources and their interpretation at a functional level. Here we address this issue by analysing transcriptomic and proteomic datasets from mouse brain tissue at embryonic days 9.5 and 13.5. We observe a high concordance between transcripts and their corresponding proteins when they were compared at the level of expression ratios between embryonic stages. Absolute expression values show marginal correlation. We show in examples, that poor concordance between protein and transcript expression is in part explained by the fact, that single genes give rise to multiple transcripts and protein variants. The integration of transcriptomic and proteomic data therefore requires proper handling of such ambiguities. A closer inspection of such cases in our datasets suggests, that comparing gene expression at exon level instead of gene level could improve the comparability. To address the biological relevance of differences in expression profiles, literature-data mining and analysis of gene ontology terms are widely used. We show here, that this can be complemented by the inspection of physical properties of genes, transcripts, and proteins.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Mus musculus; Systems biology; Transcriptome
Language english
Publication Year 2008
HGF-reported in Year 2008
ISSN (print) / ISBN 1615-9853
e-ISSN 1615-9861
Journal Proteomics
Quellenangaben Volume: 8, Issue: 6, Pages: 1165-1169 Article Number: , Supplement: ,
Publisher Wiley
Reviewing status Peer reviewed
POF-Topic(s) 30201 - Metabolic Health
Research field(s) Genetics and Epidemiology
PSP Element(s) G-500600-003
PubMed ID 18283664
Scopus ID 41549118423
Erfassungsdatum 2008-04-02