With the advent of high throughput and high dimensional metabolomics data the question of the appropriate interpretation
and integration with other omics data became pivotal. Here we focus on the human model system since it is
the best characterized model system and thereby can be seen as a benchmark for the direction of developments for other
In addition the basics on which the selection and the mode how metabolomics data can be combined with other omics data
are reviewed. Genome, transcriptome and proteome data are discussed in respect to their inherent characteristics in terms
of measurement, data features, coverage and their relatedness to metabolome data. Due to its recently gained importance
we also review the specific features of serum metabolomics. On the metabolome side we discuss the emerging relevance
of flux and the often neglected tight interconnectedness of small molecules with signaling pathways. The contribution of
metabolome data to integrated pathway analyses is so far based on either only the combination, intrinsic correlation or detection
of overrepresentation. In order to overcome the shortcomings of these approaches data interpretation should be
performed by making use of a detailed pathway topology. This functional integration is crucial for the intended comprehensive
phenotyping and demands for specific precautionary means of data assessment and data evaluation.
Keywords: Data integration, genomics, metabolomics, proteomic, transcriptomics.
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