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Intuitive Visualization and Analysis of Multi-Omics Dataand Application to Escherichia coli Carbon Metabolism
- Source :
- PLoS ONE, PLoS ONE, Public Library of Science, 2011, 6 (6), pp.e21318. ⟨10.1371/journal.pone.0021318⟩, Plos One 6 (6), e21318. (2011), PLoS ONE, Public Library of Science, 2011, 6 (6), pp.e21318. 〈10.1371/journal.pone.0021318〉, PLoS ONE, 2011, 6 (6), pp.e21318. ⟨10.1371/journal.pone.0021318⟩, PLoS ONE, Vol 6, Iss 6, p e21318 (2011)
- Publication Year :
- 2011
- Publisher :
- HAL CCSD, 2011.
-
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.
- Subjects :
- Computer science
Microarrays
[SDV]Life Sciences [q-bio]
Statistics as Topic
Protein metabolism
lcsh:Medicine
Bioinformatics
Central carbon metabolism
computer.software_genre
Biochemistry
Transcriptome
chemistry.chemical_compound
Transcriptional regulation
lcsh:Science
Regulation of gene expression
0303 health sciences
Multidisciplinary
Systems Biology
030302 biochemistry & molecular biology
Representation (systemics)
Genomics
Complex network
Functional Genomics
Carbohydrate Metabolism
Data mining
Metabolic Pathways
Metabolic Networks and Pathways
Research Article
Carbon metabolism
Biological Data Management
External Data Representation
Microbiology
Set (abstract data type)
03 medical and health sciences
Metabolic Networks
Metabolomics
Data visualization
Escherichia coli
Biology
030304 developmental biology
Microbial Metabolism
Regulatory Networks
Internet
[ SDV ] Life Sciences [q-bio]
business.industry
lcsh:R
Computational Biology
Omics
Carbon
Visualization
Metabolic pathway
Metabolism
chemistry
lcsh:Q
Metagenomics
business
Genome Expression Analysis
computer
Biological network
Software
Transcription Factors
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, PLoS ONE, Public Library of Science, 2011, 6 (6), pp.e21318. ⟨10.1371/journal.pone.0021318⟩, Plos One 6 (6), e21318. (2011), PLoS ONE, Public Library of Science, 2011, 6 (6), pp.e21318. 〈10.1371/journal.pone.0021318〉, PLoS ONE, 2011, 6 (6), pp.e21318. ⟨10.1371/journal.pone.0021318⟩, PLoS ONE, Vol 6, Iss 6, p e21318 (2011)
- Accession number :
- edsair.doi.dedup.....e660a8ff7fbfc1d17fc90a0b705ef8d1