Metabolomics Workbench 血液代谢组数据重分析：代谢物类别、生物标志物与药物的关联
Metabolomics workbench: data-driven classification and correlation analysis between metabolite classes, biomarkers, and drugs
研究团队对 Metabolomics Workbench 中所有使用 Orbitrap 或 QTOF 质谱的人体血液代谢组研究做了基于元数据的系统重分析，共纳入 44 165 个样本。作者用 PubChem 标识符统一代谢物、以 SMILES 表示做嵌入，构建了包含 6752 个血液代谢物、25 个结构簇的图谱，并分析这些簇与生物标志物、药物之间的相关性。在至少 10 项研究中检测到的 337 120 对代谢物中，结构相关的簇丰度呈相关（ρ 最高 0.6）；已知临床生物标志物之间也出现候选代谢关联，部分 ρ > 0.7。
来源：Briefings in Bioinformatics · doi.org
作者对 Metabolomics Workbench 全部人体血液代谢组数据做系统重分析，给出可复用的方法与开源软件。
Public repositories now accumulate an unprecedented amount of metabolomics data. Despite the opportunities for large-scale meta-analyses opened by these repositories, the deposited data rarely becomes systematically re-analyzed. We conducted a systematic metadata-based re-analysis of all human blood metabolomics studies in the Metabolomics Workbench performed using Orbitrap or QTOF mass spectrometry, encompassing 44 165 samples. After harmonizing metabolites with PubChem identifiers and embedding them using SMILES representations, we generated a map of 6752 blood metabolites forming 25 structural clusters, and subsequently performed correlation analysis between these clusters, biomarkers, and drugs. Analysis of 337 120 metabolite pairs detected in at least 10 studies showed that structurally related clusters display correlated abundances (ρ up to 0.6). Correlation patterns among established clinical biomarkers revealed candidate metabolic correlates, several with ρ > 0.7. We also developed open-source software enabling similar analyses for any metabolite across the Workbench data explored. Additionally, curating drug-containing datasets enabled meta-analyses for compounds such as acetaminophen, metoprolol, and atenolol, identifying both known and novel metabolic signatures. Besides the biological insights, we have found that Orbitrap and QTOF tend to detect different metabolites, but they give quantitatively coherent results on overlapping compounds. Overall, the present study provides the methodology and software to perform large-scale mining of public metabolomics data enabling its re-analysis to obtain novel biomedical insights.