LipiDex 2 Integrates MS n Tree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics.
Benton J AndersonDain R BrademanYuchen HeKatherine A OvermyerJoshua J CoonPublished in: Analytical chemistry (2024)
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. In silico spectral library generation now supports tandem mass spectral (MS n ) 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.
Keyphrases
- mass spectrometry
- quality control
- electronic health record
- liquid chromatography
- big data
- optical coherence tomography
- machine learning
- multiple sclerosis
- ms ms
- deep learning
- high throughput
- magnetic resonance imaging
- data analysis
- quality improvement
- fatty acid
- high performance liquid chromatography
- capillary electrophoresis
- high resolution mass spectrometry
- dual energy