TY - JOUR A1 - Dai, Chengxin A1 - Füllgrabe, Anja A1 - Pfeuffer, Julianus A1 - Solovyeva, Elizaveta M. A1 - Deng, Jingwen A1 - Moreno, Pablo A1 - Kamatchinathan, Selvakumar A1 - Kundu, Deepti Jaiswal A1 - George, Nancy A1 - Fexovy, Silvie A1 - Grüning, Björn A1 - Föll, Melanie Christine A1 - Griss, Johannes A1 - Vaudel, Marc A1 - Audain, Enrique A1 - Locard-Paulet, Marie A1 - Turewicz, Michael A1 - Eisenacher, Martin A1 - Uszkoreit, Julian A1 - Van Den Bossche, Tim A1 - Schwämmle, Veit A1 - Webel, Henry A1 - Schulze, Stefan A1 - Bouyssié, David A1 - Jayaram, Savita A1 - Duggineni, Vinay Kumar A1 - Samaras, Patroklos A1 - Wilhelm, Mathias A1 - Choi, Meena A1 - Wang, Mingxun A1 - Kohlbacher, Oliver A1 - Brazma, Alvis A1 - Papatheodorou, Irene A1 - Bandeira, Nuno A1 - Deutsch, Eric W. A1 - Vizcaíno, Juan Antonio A1 - Bai, Mingze A1 - Sachsenberg, Timo A1 - Levitsky, Lev I. A1 - Perez-Riverol, Yasset T1 - A proteomics sample metadata representation for multiomics integration and big data analysis JF - Nature Communications N2 - The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1038/s41467-021-26111-3 VL - 12 IS - 5854 ER - TY - JOUR A1 - Kontou, Eftychia E. A1 - Walter, Axel A1 - Alka, Oliver A1 - Pfeuffer, Julianus A1 - Sachsenberg, Timo A1 - Mohite, Omkar A1 - Nuhamunanda, Matin A1 - Kohlbacher, Oliver A1 - Weber, Tilmann T1 - UmetaFlow: An untargeted metabolomics workflow for high-throughput data processing and analysis JF - Journal of Cheminformatics N2 - Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. Y1 - 2023 U6 - https://doi.org/10.1186/s13321-023-00724-w VL - 15 ER -