Anderson Benton J, Brademan Dain R, He Yuchen, Overmyer Katherine A, Coon Joshua J
Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
Morgridge Institute for Research, Madison, Wisconsin 53715, United States.
Anal Chem. 2024 Apr 30;96(17):6715-6723. doi: 10.1021/acs.analchem.4c00359. Epub 2024 Apr 19.
As lipidomics experiments increase in scale and complexity, data processing tools must support workflows for new liquid chromatography-mass spectrometry (LC-MS) methods while simultaneously supporting quality controls to maximize the confidence in lipid identifications. LipiDex 2 improves lipidomics data processing algorithms from LipiDex 1 and introduces new tools for spectral matching and peak annotation functions, with improvements in speed and user-friendliness. spectral library generation now supports tandem mass spectral (MS) tree-based fragmentation methods, and the LipiDex 2 workflow fully integrates the fragmentation logic into the data processing steps to enable lipid identification at the appropriate level of structural resolution. Finally, LipiDex 2 features new modules for automated quality control checks that also allow users to visualize data quality in a data dashboard user interface.
随着脂质组学实验规模和复杂性的增加，数据处理工具必须支持新的液相色谱-质谱（LC-MS）方法的工作流程，同时支持质量控制，以最大限度地提高脂质鉴定的可信度。LipiDex 2改进了LipiDex 1的脂质组学数据处理算法，并引入了用于光谱匹配和峰注释功能的新工具，在速度和用户友好性方面都有所提升。光谱库生成现在支持基于串联质谱（MS）树的碎裂方法，并且LipiDex 2工作流程将碎裂逻辑完全集成到数据处理步骤中，以在适当的结构分辨率水平上实现脂质鉴定。最后，LipiDex 2具有用于自动质量控制检查的新模块，还允许用户在数据仪表板用户界面中可视化数据质量。
