DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics.
DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics.
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DOI：
10.1038/s41467-022-29006-z
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发表时间：
2022-03-15
影响因子：
16.6
通讯作者：
Röst HL
中科院分区：
文献类型：
--
作者：
Alka O;Shanthamoorthy P;Witting M;Kleigrewe K;Kohlbacher O;Röst HL
从高通量质谱数据中提取有意义的生物学知识依赖于将错误发现限制在可管理的数量。对于代谢组学的靶向方法来说，一个主要的挑战是在数据独立采集结果的低信噪比范围内检测假阳性代谢特征并对其进行滤波。另一个因素是，用于数据独立采集分析的测定文库的创建和提取离子色谱图的处理在代谢组学中尚未实现自动化。在这里，我们提出了一个全自动的高通量代谢组学的开源工作流程，它结合了数据依赖和数据独立的获取，用于库的生成、分析和统计验证，严格控制错误发现率，同时匹配人工分析的量化准确性。使用基于参考物质的实验特定数据依赖采集文库，可以从低浓度的数据独立采集数据中准确识别化合物和标记物，从而促进生物标记物的定量。从高通量质谱数据中提取有意义的生物学知识依赖于将错误发现限制在可管理的数量。在这里，作者建立了一个自动化的、错误发现率控制的目标分析工作流程，用于数据独立的获取，使稳健的FDR估计能够提高代谢组学领域结果的可比性。
The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification. The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. Here the authors establish an automated, false discovery rate-controlled targeted analysis workflow for data-independent acquisition that enables a robust FDR estimation improving the comparability of results in the metabolomics field.
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影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
3.4
作者：
Roest, Hannes L.;Schmitt, Uwe;Malmstroem, Lars
通讯作者：
Malmstroem, Lars
影响因子：
48
作者：
Reiter, Lukas;Rinner, Oliver;Aebersold, Ruedi
通讯作者：
Aebersold, Ruedi
影响因子：
48
作者：
Roest, Hannes L.;Sachsenberg, Timo;Kohlbacher, Oliver
通讯作者：
Kohlbacher, Oliver
影响因子：
4.4
作者：
Bielow, Chris;Ruzek, Silke;Reinert, Knut
通讯作者：
Reinert, Knut