This library contains exact mass, charge and RT information and is used as a list of targets for FeatureFinderMetaboIdent, a tool that detects and extracts features, commonly used for targeted analysis. The re-quantified feature files are then merged with the previously pre-processed feature files that have no missing values. The merged files are then introduced to MetaboliteAdductDecharger (Figure ), IDMapper, and finally, to FeatureLinkerUnlabeledKD (Figure ) for clustering. The resulting file is converted to a tab-separated table (.tsv) of metabolic features. Depending on the dataset to be processed, re-quantification of the feature intensities can be very beneficial for the imputation of missing values, especially when dealing with samples that include identical metabolites in varying concentrations. On the contrary, in a case such as the one of our in-house datasets that were used for validation, where there are very few common metabolites and most true features are present in high concentrations, requantification can lead to false positive signals.