Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction

We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class. Availability and implementation: All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter.

Supplementary data are available at Bioinformatics online.