Spectral deconvolution for gas chromatography mass spectrometry-based metabolomics: current status and future perspectives.
Spectral deconvolution for gas chromatography mass spectrometry-based metabolomics: current status and future perspectives.
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DOI：
10.5936/csbj.201301013
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发表时间：
2013
影响因子：
6
通讯作者：
Zeisel SH
中科院分区：
文献类型：
--
作者：
Du X;Zeisel SH
质谱-气相色谱联用技术（GC-MS）已广泛应用于代谢组学领域。该应用的成功极大地得益于处理复杂的原始质谱数据并提取代谢物的定性和定量信息的计算工作流程。在工作流程中的计算算法中，去卷积是至关重要的，因为它为质谱仪观察到的每个组分重建纯质谱。基于纯光谱，最终可以识别和定量相应的组分。由于共洗脱的存在，反卷积具有挑战性。在这篇综述中，我们专注于已取得的进展，在去卷积算法的发展，并提供未来的发展思路，将扩大GC-MS在代谢组学中的应用。
Mass spectrometry coupled to gas chromatography (GC-MS) has been widely applied in the field of metabolomics. Success of this application has benefited greatly from computational workflows that process the complex raw mass spectrometry data and extract the qualitative and quantitative information of metabolites. Among the computational algorithms within a workflow, deconvolution is critical since it reconstructs a pure mass spectrum for each component that the mass spectrometer observes. Based on the pure spectrum, the corresponding component can be eventually identified and quantified. Deconvolution is challenging due to the existence of co-elution. In this review, we focus on progress that has been made in the development of deconvolution algorithms and provide thoughts on future developments that will expand the application of GC-MS in metabolomics.