GC/MS based metabolomics: Development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA)

Hiroshi Tsugawa, Yuki Tsujimoto, Masanori Arita, Takeshi Bamba, Eiichiro Fukusaki

研究成果: ジャーナルへの寄稿学術誌査読

146 被引用数 (Scopus)

抄録

Background: The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns".Result: We present an algorithm that acquires more extensive metabolite information. Pearson's product-moment correlation coefficient and the Soft Independent Modeling of Class Analogy (SIMCA) method were combined to automatically identify and annotate unknown peaks, which tend to be missed in routine studies that employ manual processing.Conclusions: Our data mining system can offer a wealth of metabolite information quickly and easily, and it provides new insights, particularly into food quality evaluation and prediction.

本文言語英語
論文番号131
ジャーナルBMC bioinformatics
12
DOI
出版ステータス出版済み - 5月 4 2011
外部発表はい

!!!All Science Journal Classification (ASJC) codes

  • 構造生物学
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 応用数学

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