Pre-processing is a crucial step in metabolomics data mining for transforming the raw data to a table of metabolic features . This part of the workflow uses OpenMS algorithms for feature detection, adduct annotation, feature alignment and clustering (Figure ). Initially, the mzML files are processed with the OpenMS tool HighResPrecursorMassCorrector, which corrects for mistakenly assigned precursors of MS 2 spectra, by selecting the intact mass spectrum level (MS 1 ) peak with the highest intensity using RT and mass range information. This algorithm is useful for Data-Dependent Acquisition (DDA) mode, where the most intense ions in a spectrum are selected for fragmentation but can be ignored for other acquisition methods. The feature detection algorithm FeatureFinderMetabo detects mass traces of similar m/z along the RT dimension, deconvolves (partially) overlapping chromatographic peaks and assembles co-eluting, single mass traces to metabolite features for data reduction . The most important parameters for feature detection are the mass error and noise threshold, defined by the instrument and method that is used to analyze the samples, as well as the peak width, which is directly correlated to the chromatographic system (Table ). The feature maps generated by FeatureFinderMetabo are containers that include information on each feature, such as m/z, RT, charge, and intensity, and are stored as featureXML files, an OpenMS file format for LC-MS data. Those, together with the corresponding mzML files, are then processed by HighResPrecursorMassCorrector, which corrects for mistakenly assigned MS 2 parent ions to monoisotopic masses. Next, MapAlignerPoseClustering performs a linear RT alignment between the featureXML files to correct for any chromatographic RT shifts (Figure ). The file used as a reference for alignment is fetched automatically by the algorithm, if not specified by the user, and it is the file with the highest number of features (e.g., a pooled quality control sample). The mzML files are also introduced to MapRTTransformer for RT alignment, using transformation description files (.trafoXML) generated from MapAlignerPoseClustering . The aligned feature maps are subjected to analysis with MetaboliteAdductDecharger, which is used for adduct annotation (Figure ), as well as to convert the charged features to neutral masses, and cluster features that originate from the same metabolite . This algorithm is important for information reduction, formula, and structural predictions, as well as for FBMN. Here, the most important parameter is the list of adducts that are possibly generated by the instrument, in positive or negative ionization, and the probability of their occurrence. IDMapper annotates the features that have MS 2 information to contain necessary metadata for the GNPS-export step. All feature files are finally linked by FeatureLinkerUnlabeledKD to match corresponding features over several runs by m/z and RT and store all feature information in a single consensus map (Figure ). Finally, the consensus map is converted to a table of features with information about m/z, RT, adduct, as well as presence and intensity of each feature in each input file (Tables SI2, SI3 and SI4) in a tabseparated format (.tsv).