title: Intuitive visualization and analysis of multi-omics data and application to Escherichia coli carbon metabolism.
abstract: Combinations of 'omics' investigations (i.e, transcriptomic, proteomic, metabolomic and/or fluxomic) are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work.
authors: Université de Toulouse, INSA, UPS, INP, Toulouse, France.
authors: Brice
authors: B
authors: Enjalbert
authors: Fabien
authors: F
authors: Jourdan
authors: Jean-Charles
authors: JC
authors: Portais
mesh_terms: D002244:Carbon / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000378:metabolism*; D020407:Internet; D053858:Metabolic Networks and Pathways; D012984:Software; D013223:Statistics as Topic; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors; D002244:Carbon