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

BMC Bioinformatics

Research Article

Takeshi Bamba¹  Yuki Tsujimoto¹  Hiroshi Tsugawa¹  Eiichiro Fukusaki¹  Masanori Arita² 

[1] Department of Bioengineering, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, 565-0871, Osaka, Japan;Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-003, Tokyo, Japan;

关键词: Reference Library;    Unknown Peak;    Residual Standard Deviation;    Fatty Acid Group;    Data Mining System;   

DOI  :  10.1186/1471-2105-12-131

 received in 2010-11-08, accepted in 2011-05-04,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundThe 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".ResultWe 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.ConclusionsOur 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.

【 授权许可】

Unknown   
© Tsugawa et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.