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Linking transcriptomic and proteomic data on the level of protein interaction networks
Author(s) -
Perco Paul,
Mühlberger Irmgard,
Mayer Gert,
Oberbauer Rainer,
Lukas Arno,
Mayer Bernd
Publication year - 2010
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200900775
Subject(s) - proteome , proteomics , transcriptome , computational biology , biology , context (archaeology) , metabolome , systems biology , phenotype , genome , bioinformatics , metabolomics , gene , genetics , gene expression , paleontology
Integration and joint analysis of omics profiles derived on the genome, transcriptome, proteome and metabolome levels is a natural next step in realizing a Systems Biology view of cellular processes. However, merging, e.g . mRNA concentration and protein abundance profiles, is not straightforward, as a direct overlap of differentially regulated/abundant features, resulting from transcriptomics and proteomics, is for various reasons limited. We present the procedures for integrating omics profiles at the level of protein interaction networks, exemplified by using transcriptomic and proteomic data sets characterizing chronic kidney disease. On the level of direct feature overlap, only a limited number of genes and proteins were found to be significantly affected following a separate transcript and protein profile analysis, including a collagen subtype and uromodulin, both being described in the context of renal failure. On the level of protein pathway and process categories, this minor overlap increases substantially, identifying cell structure, cell adhesion, as well as immunity and defense mechanisms as jointly populated with features individually identified as relevant in transcriptomics and proteomics experiments. Mapping diverse data sources characterizing a given phenotype under the analysis on directed and also undirected protein interaction networks serves in joint functional interpretation of omics data sets.