Additional file 1 of UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Additional file 1. UmetaFlow: An untargeted metabolomics workflow for high-throughput data processing and analysis. Figure S1. A detailed overview of UmetaFlow. Table S1. Important instrument, method, and sample-specific parameters for UmetaFlow parameter optimization. Table S2. The optimal parameters for OpenMS (UmetaFlow) for feature detection, formula, and structural predictions of the in-house datasets. Table S3. Feature detection, structural and formula predictions for pyracrimycin A in Streptomyces sp. NBC 00162, Streptomyces sp. CA-210063 and Streptomyces eridani. Table S4. The optimal parameters for OpenMS (UmetaFlow) for feature detection, quantification, and marker selection of the MTBLS733 QE HF dataset. Table S5. Feature identification, quantification, and marker selection performance of different untargeted metabolomic data processing software using the benchmark dataset MTBLS733. Table S6. The optimal parameters for OpenMS (UmetaFlow) for feature detection, quantification, and marker selection of the MTBLS736 tripleTOF dataset. Table S7. Feature identification, quantification, and marker selection performance of different untargeted metabolomic data processing software using the benchmark dataset MTBLS736. Table S8. The optimal parameters for OpenMS (UmetaFlow) for feature detection and quantification of the MTBLS1129 and MTBLS1130 dataset. Figure S1. Plotted average metabolite intensities in normal and colon cancer tissue samples, detected and quantified with (a) XCMS and (b) UmetaFlow (dataset MTBLS1129).