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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis.

Henning Redestig1, Miyako Kusano, Atsushi Fukushima

  • 1Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan. henning@psc.riken.jp

BMC Bioinformatics
|April 30, 2010
PubMed
Summary

This study introduces MetMask, an open-source tool for metabolomics data analysis. It efficiently integrates metabolite identifiers from various sources, simplifying data cross-referencing for researchers.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput experiments require well-structured biomolecular metadata.
  • Metabolite data organization and cross-referencing are challenging due to incoherent chemical identifiers.
  • Existing online chemical databases lack a common primary key for reliable consolidation.

Purpose of the Study:

  • To develop a strategy and software tool for integrating metabolite identifiers from diverse sources.
  • To overcome the limitations of inconsistent chemical referencing schemes in metabolomics.

Main Methods:

  • Developed a strategy to group interconnected metabolite and analyte identifiers.
  • Constructed a local metabolite-centric SQLite database.
  • Implemented flexible querying from command line and R statistical environment.

Main Results:

  • Created a software tool (MetMask) for integrating local and public metabolite identifier databases.
  • Enabled mapping of in-house identifiers to external resources like KEGG.
  • Facilitated flexible, data set-tailored identifier mappings.

Conclusions:

  • Efficient cross-referencing of metabolite identifiers is crucial for metabolomics.
  • The MetMask tool provides a practical, flexible, and open-source solution.
  • MetMask simplifies the integration and analysis of metabolomics data.