Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR.
Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR.
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DOI：
10.1021/acs.analchem.1c03032
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发表时间：
2022-03-01
影响因子：
7.4
通讯作者：
Ebbels TMD
中科院分区：
文献类型：
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作者：
Graça G;Cai Y;Lau CE;Vorkas PA;Lewis MR;Want EJ;Herrington D;Ebbels TMD
非靶向代谢组学和脂质组学LC-MS实验产生复杂的数据集，通常包含来自数千种代谢物的数万个特征，其注释需要额外的MS/MS实验和专业知识。全离子裂解（AIF）LC-MS/MS采集提供裂解数据，无需额外的实验时间成本。然而，这样的数据集的分析需要重建的父母片段的关系和注释所得到的伪MS/MS谱。在这里，我们提出了一种新的方法，通过将基于相关性的亲本片段连接与分子片段匹配相结合，自动注释来自AIF LC-MS数据集的同位素体、加合物和源内片段。我们的工作流程专注于功能的子集，而不是试图注释完整的数据集，从而节省时间并简化流程。我们展示了三个人血清数据集包含599个功能手动注释的专家的工作流程。对于在最高等级分数（1-5）中发现的特征，分别获得了82-92%和82- 85%的精确度和召回率值。这些结果等于或优于使用MS-DIAL软件获得的结果，MS-DIAL软件是AIF数据注释的最新技术。对其他生物基质和不同仪器类型的进一步验证显示了可变的精确度（60-89%）和召回率（10-88%），特别是对于以非脂质代谢物为主的数据集。该工作流作为开源R包MetaboAnnotatoR以及Github（）的片段库免费提供。
Untargeted metabolomics and lipidomics LC–MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC–MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent–fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC–MS datasets by combining correlation-based parent–fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82–92% and 82–85%, respectively, were obtained for features found in the highest-rank scores (1–5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60–89%) and recall (10–88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github ().
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影响因子：
14.9
作者：
Allen F;Pon A;Wilson M;Greiner R;Wishart D
通讯作者：
Wishart D
影响因子：
7.4
作者：
Domingo-Almenara X;Montenegro-Burke JR;Benton HP;Siuzdak G
通讯作者：
Siuzdak G
影响因子：
7.4
作者：
Domingo-Almenara, Xavier;Montenegro-Burke, J. Rafael;Siuzdak, Gary
通讯作者：
Siuzdak, Gary
影响因子：
7.4
作者：
Broeckling, C. D.;Afsar, F. A.;Prenni, J. E.
通讯作者：
Prenni, J. E.
DOI：
10.1093/bioinformatics/bty080
发表时间：
2018-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
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作者：
通讯作者：
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