Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30308, United States.
Department of Civil and Environmental Engineering, Tufts University , Medford, Massachusetts 02153, United States.
Anal Chem. 2017 Jan 17;89(2):1063-1067. doi: 10.1021/acs.analchem.6b01214. Epub 2017 Jan 4.
Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps.
改进的分析技术和数据提取算法使通过液相色谱-高分辨率质谱检测到 >10000 个可重复的信号成为可能，这在化学鉴定方面造成了瓶颈。原则上，如果有算法可以利用原始质谱数据，特别是低丰度代谢物，则有可能测量超过一百万种化学物质。在这里，我们描述了一种自动计算框架，该框架使用多阶段聚类算法对离子进行注释，以确定其可能的化学特性，其中代谢途径关联与强度分布、保留时间特性、质量缺陷和同位素/加合物模式一起使用。该算法使用具有共同特性的一系列样品的高分辨率质谱数据以及公开的化学、代谢和环境数据库，为注释结果分配置信水平。评估结果表明，该算法在具有已知靶标的数据集上的 F1 度量达到 0.8，并且在数据库大小远大于实际代谢物数量的情况下，比以前报告的结果更稳健。在非靶向代谢组学人类数据集使用 xMSannotator 注释的一组随机选择的 210 种代谢物的 MS/MS 评估表明，具有高或中置信度得分的特征中有 80%具有与 xMSannotator 注释一致的离子解离模式。该算法已被纳入 R 包 xMSannotator 中，该包包括用于查询本地或在线数据库（如 ChemSpider、KEGG、HMDB、T3DB 和 LipidMaps）的实用程序。
