Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery
Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery
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DOI：
10.1021/ac0614846
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发表时间：
2007-02-01
影响因子：
7.4
通讯作者：
Stephanopoulos, Gregory N.
中科院分区：
文献类型：
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作者：
Styczynski, Mark P.;Moxley, Joel F.;Stephanopoulos, Gregory N.
气相色谱-质谱（GC/MS）测量的代谢组学分析数据通常依赖于代谢物质谱参考库来结构识别和跟踪代谢物。一般来说，枚举和跟踪未识别代谢物的技术是非系统的，需要人工管理。我们提出了一种方法和软件实现，可以在http://spectconnect.mit.edu上免费获得，它可以系统地检测样本中保守的组件，而不需要参考库或手动管理。我们通过正确识别已知混合物中的成分和加标混合物中的鉴别成分来验证这种方法。最后，我们通过对大肠杆菌代谢组的简要分析证明了这种方法的应用。通过在数据分析方法之前系统地编目保守的代谢物峰，我们的方法拓宽了代谢组学的范围，促进了生物标志物的发现。
Analysis of metabolomic profiling data from gas chromatography-mass spectrometry (GC/MS) measurements usually relies upon reference libraries of metabolite mass spectra to structurally identify and track metabolites. In general, techniques to enumerate and track unidentified metabolites are nonsystematic and require manual curation. We present a method and software implementation, freely available at http://spectconnect.mit.edu, that can systematically detect components that are conserved across samples without the need for a reference library or manual curation. We validate this approach by correctly identifying the components in a known mixture and the discriminating components in a spiked mixture. Finally, we demonstrate an application of this approach with a brief analysis of the Escherichia coli metabolome. By systematically cataloguing conserved metabolite peaks prior to data analysis methods, our approach broadens the scope of metabolomics and facilitates biomarker discovery.