Medical Journals

Integration of Omics Data: How Well Does It Work for Bacteria?

Authors:
  • De Keersmaecker Sigrid C J
  • Thijs Inge M V
  • Vanderleyden Jos
  • Marchal Kathleen

From: Centre of Microbial and Plant Genetics (CMPG) Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, Belgium.

Molecular microbiology

  • Publish Date: Dec 2006
  • ISSN: 0950-382X
  • Volume: 62
  • Issue: 5
  • Pages: 1239-50
  • Medium: Print
  • Language: English
  • Citation (JAMA): De Keersmaecker Sigrid C J, Thijs Inge M V, Vanderleyden Jos, et al. Integration of Omics Data: How Well Does It Work for Bacteria?. Mol. Microbiol. Dec 2006;62:1239-50

Abstract

In the current omics era, innovative high-throughput technologies allow measuring temporal and conditional changes at various cellular levels. Although individual analysis of each of these omics data undoubtedly results into interesting findings, it is only by integrating them that gaining a global insight into cellular behaviour can be aimed at. A systems approach thus is predicated on data integration. However, because of the complexity of biological systems and the specificities of the data-generating technologies (noisiness, heterogeneity, etc.), integrating omics data in an attempt to reconstruct signalling networks is not trivial. Developing its methodologies constitutes a major research challenge. Besides for their intrinsic value towards health care, environment and industry, prokaryotes are ideal model systems to further develop these methods because of their lower regulatory complexity compared with eukaryotes, and the ease with which they can be manipulated. Several successful examples outlined in this review already show the potential of the systems approach for both fundamental and industrial applications, which would be time-consuming or impossible to develop solely through traditional reductionist approaches.

Mesh Headings (Keywords): Bacteria, Genome, Bacterial, Genomics, Proteome, Proteomics


Check for Full Text / PubMed Unique Identifier (PMID): 17040488


This abstract is part of PubMed, a service of the U.S. National Library of Medicine. PubMed includes more than 17 million citations from MEDLINE and other life science journals for biomedical articles. See Copyright and Disclaimers.

Linked medical terms appearing on this page are added by Healia to help readers find more information and are not part of the original PubMed document.

The data herein was last updated on July 8th, 2008 and may not reflect the most current and accurate data available from NLM.


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