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New bioinformatics resources for metabolomics.

John L Markley1, Mark E Anderson, Qiu Cui

  • 1Department of Biochemistry, University of Wisconsin-Madison, 433 Babcock Drive, Madison, Wisconsin 53706, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|November 10, 2007
PubMed
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Two new databases and a laboratory information system were developed to aid metabolomics researchers. These freely available tools streamline data analysis for mass spectrometry (MS) and nuclear magnetic resonance (NMR) studies.

Area of Science:

  • Metabolomics
  • Bioinformatics
  • Computational Chemistry

Background:

  • Metabolomics research generates large datasets requiring robust data management and analysis tools.
  • Existing resources may lack comprehensive spectral data or integrated experimental tracking.
  • Standardization of data handling is crucial for reproducibility in metabolomics.

Purpose of the Study:

  • To introduce novel, freely accessible resources for the metabolomics community.
  • To enhance data analysis capabilities for both MS and NMR-based metabolomics.
  • To provide integrated solutions for experimental data and protocol management.

Main Methods:

  • Developed a metabolomics extension to the BioMagResBank (BMRB) with NMR spectral data for over 270 compounds.

Related Experiment Videos

  • Created the Madison Metabolomics Consortium Database (MMCD) containing information on over 10,000 metabolites, emphasizing Arabidopsis.
  • Integrated a new module into the Sesame laboratory information management system to capture experimental protocols and data.

    • Main Results:

    • The BMRB extension offers access to peak lists, spectra, and time-domain data, searchable by name, mass, and chemical shift.
    • The MMCD database supports extensive and bulk queries using experimental MS and NMR data.
    • The Sesame module facilitates coordination and tracking of high-throughput metabolomics studies.

    Conclusions:

    • These developed resources significantly improve data accessibility and analysis for metabolomics studies.
    • The integrated approach aids in coordinating research efforts and ensuring data integrity.
    • The tools are expected to advance the field of metabolomics by facilitating data sharing and analysis.