UmetaFlow was further benchmarked using the dataset generated also by Zhucui Li et al , with the MetaboLights accession MTBLS736, analyzed with an AB SCIEX TripleTOF 6600 instrument. After parameter optimization (Table ), UmetaFlow could annotate 874 features out of the 970 that were detected using a targeted approach. The workflow could compete with widely used untargeted metabolomics tools (MarkerView, MS-Dial, MZmine2 and XCMS) when compared for feature detection (90.1% true feature ID rate), quantification rate (81.7% accurately quantified features) and discriminating marker selection (59 out of 68 true and 1 false discriminating marker) (Figure -5, Table ). All benchmarking was performed on a MacBook Pro 2020 with 2 GHz Quad-Core Intel Core i5-1038NG7 with 16 GB RAM. groups (Gd1-Gd6) are specified in Figure of the paper by Zhucui Li et al . . Feature detection, quantification, and marker selection performance between different untargeted metabolomic data processing software using the benchmark dataset MTBLS736 . UmetaFlow is compared with and without the re-quantification step. Refer to Table for further details.