Abstract
Untargeted lipidomics is a widely utilized subset of lipidomics that focuses specifically on the breadth of lipid identification within samples. Lipidomics approaches are often coupled with the use of mass spectrometry, generating large amounts of raw data. To make sense of the raw data, steps involving peak detection, noise filtering, retention time alignment, and deisotoping are accomplished through the use of data preprocessing software. This chapter used a dataset from Metabolomics Workbench to compare the functionality and performance of four specific data preprocessing software programs: Lipid Search, MS-DIAL, MZmine, and Compound Discoverer in untargeted lipidomics. MS-DIAL performed the best with regards to the number of features detected and the number of unique lipids identified following data consolidation by sum. However, a personalized approach is advised for researchers intending to use the four aforementioned software programs for untargeted lipidomics studies, depending on the subject and aims of the study.