Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, Kannapolis, North Carolina 28081, USA.
Anal Chem. 2012 Aug 7;84(15):6619-29. doi: 10.1021/ac300898h. Epub 2012 Jul 12.
ADAP-GC 2.0 has been developed to deconvolute coeluting metabolites that frequently exist in real biological samples of metabolomics studies. Deconvolution is based on a chromatographic model peak approach that combines five metrics of peak qualities for constructing/selecting model peak features. Prior to deconvolution, ADAP-GC 2.0 takes raw mass spectral data as input, extracts ion chromatograms for all the observed masses, and detects chromatographic peak features. After deconvolution, it aligns components across samples and exports the qualitative and quantitative information of all of the observed components. Centered on the deconvolution, the entire data analysis workflow is fully automated. ADAP-GC 2.0 has been tested using three different types of samples. The testing results demonstrate significant improvements of ADAP-GC 2.0, compared to the previous ADAP 1.0, to identify and quantify metabolites from gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) data in untargeted metabolomics studies.
ADAP-GC 2.0 旨在对代谢组学研究中真实生物样本中经常共流出的代谢物进行解卷积。解卷积基于色谱模型峰方法，该方法结合了五个峰质量指标，用于构建/选择模型峰特征。在解卷积之前，ADAP-GC 2.0 以原始质谱数据作为输入，提取所有观察到的质量的离子色谱图，并检测色谱峰特征。解卷积后，它在样品之间对齐成分，并输出所有观察到的成分的定性和定量信息。以解卷积为中心，整个数据分析工作流程完全自动化。ADAP-GC 2.0 已经使用三种不同类型的样本进行了测试。测试结果表明，与之前的 ADAP 1.0 相比，ADAP-GC 2.0 在识别和定量气相色谱/飞行时间质谱 (GC/TOF-MS) 数据中的代谢物方面有显著的改进，用于非靶向代谢组学研究。
