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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.

SUBMITTER: Enjalbert B

PROVIDER: S-EPMC3120879 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Intuitive visualization and analysis of multi-omics data and application to Escherichia coli carbon metabolism.

Enjalbert Brice B Jourdan Fabien F Portais Jean-Charles JC

PloS one 20110622 6

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 us ...[more]

PMID: 21731702

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Project description:BACKGROUND:An integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, only a few simulation tools that simulate multiple types of omics data and model their relationships with disease status are available, and these tools have their limitations in simulating the multi-omics data. RESULTS:We developed the multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., single-nucleotide polymorphisms [SNPs] and copy number variations), epigenomics (i.e., bisulphite sequencing), transcriptomics (i.e., RNA sequencing), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as methylation quantitative trait loci (SNPs influencing methylation), expression quantitative trait loci (SNPs influencing gene expression), and expression quantitative trait methylations (methylations influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and runtime. We also used OmicsSIMLA to simulate a multi-omics dataset with a scale similar to an ovarian cancer multi-omics dataset. The neural network-based multi-omics analysis method ATHENA was applied to both the real and simulated data and the results were compared. Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and ATHENA showed the highest prediction accuracy when the effects of multi-omics features (e.g., SNPs, copy number variations, and gene expression levels) on the disease were strong. Furthermore, similar results can be obtained from ATHENA when analyzing the simulated and real ovarian multi-omics data. CONCLUSIONS:OmicsSIMLA will be useful to evaluate the performace of different multi-omics analysis methods. Sample sizes and power can also be calculated by OmicsSIMLA when planning a new multi-omics disease study.

Project description:BackgroundGlycerol has enhanced its biotechnological importance since it is a byproduct of biodiesel synthesis. A study of Escherichia coli physiology during growth on glycerol was performed combining transcriptional-proteomic analysis as well as kinetic and stoichiometric evaluations in the strain JM101 and certain derivatives with important inactivated genes.ResultsTranscriptional and proteomic analysis of metabolic central genes of strain JM101 growing on glycerol, revealed important changes not only in the synthesis of MglB, LamB and MalE proteins, but also in the overexpression of carbon scavenging genes: lamB, malE, mglB, mglC, galP and glk and some members of the RpoS regulon (pfkA, pfkB, fbaA, fbaB, pgi, poxB, acs, actP and acnA). Inactivation of rpoS had an important effect on stoichiometric parameters and growth adaptation on glycerol. The observed overexpression of poxB, pta, acs genes, glyoxylate shunt genes (aceA, aceB, glcB and glcC) and actP, suggested a possible carbon flux deviation into the PoxB, Acs and glyoxylate shunt. In this scenario acetate synthesized from pyruvate with PoxB was apparently reutilized via Acs and the glyoxylate shunt enzymes. In agreement, no acetate was detected when growing on glycerol, this strain was also capable of glycerol and acetate coutilization when growing in mineral media and derivatives carrying inactivated poxB or pckA genes, accumulated acetate. Tryptophanase A (TnaA) was synthesized at high levels and indole was produced by this enzyme, in strain JM101 growing on glycerol. Additionally, in the isogenic derivative with the inactivated tnaA gene, no indole was detected and acetate and lactate were accumulated. A high efficiency aromatic compounds production capability was detected in JM101 carrying pJLBaroG(fbr)tktA, when growing on glycerol, as compared to glucose.ConclusionsThe overexpression of several carbon scavenging, acetate metabolism genes and the absence of acetate accumulation occurred in JM101 cultures growing on glycerol. To explain these results it is proposed that in addition to the glycolytic metabolism, a gluconeogenic carbon recycling process that involves acetate is occurring simultaneously in this strain when growing on glycerol. Carbon flux from glycerol can be efficiently redirected in JM101 strain into the aromatic pathway using appropriate tools.

Dataset Information

Intuitive visualization and analysis of multi-omics data and application to Escherichia coli carbon metabolism.

Publications

Intuitive visualization and analysis of multi-omics data and application to Escherichia coli carbon metabolism.

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