Content The MZmine section will teach basic methods for mass spectrometry data preprocessing, including feature detection, deisotoping, alignment, and gap filling. We will take advantage of the various visualization modules in MZmine to optimize the choice of parameters during these steps. We will also learn how to use various metabolite identification tools, including spectral database searching and machine learning-based prediction methods such as CSI-FingerID. Finally, we will export the results from MZmine for downstream statistical analyses and for molecular networking tools. Goals and learning points i) how to preprocess mass spectrometry data using MZmine ii) how to optimize preprocessing parameters using MZmine visualization modules iii) how to use metabolite identification tools iv) how to export MZmine-preprocessed data for further analyses and molecular networks