MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

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• 作者：Liang Li ; Ronghong Li ; Jianjun Zhou ; Azeret Zuniga ; Avalyn E. Stanislaus ; Yiman Wu ; Tao Huan ; Jiamin Zheng ; Yi Shi ; David S. Wishart ; Guohui Lin
• 刊名：Analytical Chemistry
• 出版年：2013
• 出版时间：March 19, 2013
• 年：2013
• 卷：85
• 期：6
• 页码：3401-3408
• 全文大小：361K
• 年卷期：v.85,no.6(March 19, 2013)
• ISSN：1520-6882

文摘

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8鈥?21 known human endogenous metabolites and their predicted metabolic products (375鈥?09 compounds from one metabolic reaction and 10鈥?83鈥?01 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries.