Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis

Masahiro Sugimoto, Masato Kawakami, Martin Robert, Tomoyoshi Soga, Masaru Tomita

Research output: Contribution to journalArticlepeer-review

237 Citations (Scopus)


Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

Original languageEnglish
Pages (from-to)96-108
Number of pages13
JournalCurrent Bioinformatics
Issue number1
Publication statusPublished - 2012 Mar


  • Bioinformatics
  • Mass spectrometry
  • Metabolome
  • Metabolomics
  • Software development
  • Statistical analysis
  • Systems biology

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics


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