TY - JOUR
T1 - UmetaFlow
T2 - an untargeted metabolomics workflow for high-throughput data processing and analysis
AU - Kontou, Eftychia E.
AU - Walter, Axel
AU - Alka, Oliver
AU - Pfeuffer, Julianus
AU - Sachsenberg, Timo
AU - Mohite, Omkar S.
AU - Nuhamunada, Matin
AU - Kohlbacher, Oliver
AU - Weber, Tilmann
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023
Y1 - 2023
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. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets. Graphical Abstract: [Figure not available: see fulltext.]
AB - 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. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets. Graphical Abstract: [Figure not available: see fulltext.]
KW - Analysis
KW - High-throughput workflow
KW - Processing
KW - Software
KW - Untargeted metabolomics
U2 - 10.1186/s13321-023-00724-w
DO - 10.1186/s13321-023-00724-w
M3 - Journal article
C2 - 37173725
AN - SCOPUS:85159212067
SN - 1758-2946
VL - 15
JO - Journal of Cheminformatics
JF - Journal of Cheminformatics
M1 - 52
ER -