Untargeted feature detection unavoidably leads to missing values that represent undetected and low-quality features (e.g., missing intensity or mass trace length thresholds). To overcome this problem, a feature intensity value must be imputed, which is implemented in a lot of metabolomics tools by gap filling . Here, we introduce an optional step where if a feature has at least one missing value across all samples, re-quantification is performed to all files (Figure ). In gap filling or any re-quantification step, a secondary feature detection algorithm is used that searches for signals across the mzML files. In UmetaFlow, this step replaces all values across the samples instead of solely the missing one, to maintain comparability of the feature intensities across all samples by using a single quantification algorithm. Using the pre-processed consensus file, a library of features is built from the ones that have at least one missing value along all feature files.