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Current progress in computational metabolomics.

David S Wishart1

  • 1Department of Computing Science, Department of Biological Sciences and National Institute for Nanotechnology (NRC-NINT), University of Alberta, Edmonton Alberta, Canada T6G 2E8. david.wishart@ualberta.ca

Briefings in Bioinformatics
|July 13, 2007
PubMed
Summary
This summary is machine-generated.

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Metabolomics, a new

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Cheminformatics

Background:

  • Metabolomics is an emerging field within the 'omics' sciences.
  • It requires integrating chemical data with biological consequences.
  • This necessitates combining bioinformatics and cheminformatics approaches.

Purpose of the Study:

  • To introduce readers to the field of metabolomics.
  • To outline computational needs and challenges in metabolomics.
  • To review recent progress in computational metabolomics.

Main Methods:

  • Review of existing literature and computational tools.
  • Focus on four key areas: databases, LIMS, spectral analysis, and metabolic modeling.
  • Integration of bioinformatics and cheminformatics principles.

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Main Results:

  • Identified significant computational challenges due to the separate evolution of bioinformatics and cheminformatics.
  • Highlighted the need for specialized computational tools and skill sets.
  • Summarized progress in developing metabolomics databases, LIMS, spectral analysis, and metabolic modeling.

Conclusions:

  • Computational metabolomics requires a unique blend of bioinformatics and cheminformatics.
  • Addressing these computational needs is crucial for the advancement of metabolomics research.
  • Ongoing progress in key areas is paving the way for more robust metabolomic analyses.