xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.
xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.
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DOI：
10.1021/acs.analchem.6b01214
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发表时间：
2017-01-17
影响因子：
7.4
通讯作者：
Jones DP
中科院分区：
文献类型：
--
作者：
Uppal K;Walker DI;Jones DP
改进的分析技术和数据提取算法使液相色谱高分辨率质谱法能够检测到100万个可重复的信号，从而成为化学鉴定的瓶颈。原则上，如果有便于利用原始质谱数据的算法，特别是低丰度代谢物，就有可能测量100多万种化学物质。在这里，我们描述了一个自动化的计算框架来注释离子可能的化学特性，使用多阶段聚类算法，其中代谢途径关联与强度分布、保留时间特征、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度。评估结果表明，对于具有已知目标的数据集，该算法达到了0.8的f1测量值，并且在数据库大小远远大于实际代谢物数量的情况下，该算法比先前报道的结果更加稳健。用xMSannotator在非靶向代谢组学人类数据集中随机选择210种代谢物进行MS/MS评估，结果显示，80%的高或中等置信度特征具有与xMSannotator注释一致的离子解离模式。该算法已被整合到一个R包xMSannotator中，其中包括用于查询本地或在线数据库的实用程序，如ChemSpider、KEGG、HMDB、T3DB和LipidMaps。
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 dataset 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 dataset 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.