Feature-Based Molecular Networking Workflow

The FBMN method consists of two main steps: 1) LC-MS feature detection and alignment, then 2) a dedicated molecular networking workflow on GNPS. Our first prototype for FBMN was developed with the Optimus workflow^{7 (link),14} that uses OpenMS tools^{10 (link)}. Following step 1 (feature detection and alignment), two files are exported: a feature quantification table (.TXT format) and a MS²spectral summary (.MGF format). The feature quantification table contains information about LC-MS features across all considered samples including a unique identifier (Feature ID) for each feature, m/z value, retention time, and intensity. The MS²spectral summary contains a list of MS² spectra, with one representative MS² spectrum per feature. The mapping of information between the feature quantification table and the MS²spectral summary is stored in these files using the feature ID and scan number, respectively. This simple mapping enables to relate LC-MS feature information or statistically derived results to the molecular network nodes. This approach was also used for the integration of other tools with FBMN, and does not require third party software like it was proposed in the past^{22 ,23 (link)}. Finally, the FBMN workflow also supports the mzTab-M format^{6 (link)}, a standardized output format designed for the report of metabolomics MS-data processing results. In this case, the mzTab-M file is used instead of feature quantification table and requires the input of the mzML files instead of the MS²spectral summary file. Support for the mzTab-M format enables the possibility to perform FBMN with any existing and future processing tools that support this standardized format.
The FBMN workflow has been integrated into the GNPS ecosystem and thus benefits from the connection with other GNPS features, e.g. the possibility to perform automatic MS² spectral library search, the direct addition and curation of library entries, the search of a spectrum against public datasets with MASST^{20 (link)}, and the visualization of molecular networks directly in the web browser^{24 (link)} or with Cytoscape^{25 (link)}. The FBMN workflow is available on the GNPS platform (https://gnps.ucsd.edu/) via a web interface (See Supplementary Fig. 2). Jobs are computed and stored on the computational infrastructure of the Center for Computational Mass Spectrometry at the University of California San Diego. Each finished job is saved in the private user space for future examination and has a permanent static link that enables data sharing and collaborative analyses. We strongly recommend the sharing of this static link along with publications using GNPS workflows to facilitate results accessibility and data analysis reproducibility. Instructions to perform FBMN with the supported tools and input file format requirements are provided in the GNPS documentation (https://ccms-ucsd.github.io/GNPSDocumentation/featurebasedmolecularnetworking and Supplementary Fig. 3).

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Nothias L.F., Petras D., Schmid R., Dührkop K., Rainer J., Sarvepalli A., Protsyuk I., Ernst M., Tsugawa H., Fleischauer M., Aicheler F., Aksenov A., Alka O., Allard P.M., Barsch A., Cachet X., Caraballo M., Silva R.R., Dang T., Garg N., Gauglitz J.M., Gurevich A., Isaac G., Jarmusch A.K., Kameník Z., Kang K.B., Kessler N., Koester I., Korf A., Gouellec A.L., Ludwig M., H. C.M., McCall L.I., McSayles J., Meyer S.W., Mohimani H., Morsy M., Moyne O., Neumann S., Neuweger H., Nguyen N.H., Nothias-Esposito M., Paolini J., Phelan V.V., Pluskal T., Quinn R.A., Rogers S., Shrestha B., Tripathi A., van der Hooft J.J., Vargas F., Weldon K.C., Witting M., Yang H., Zhang Z., Zubeil F., Kohlbacher O., Böcker S., Alexandrov T., Bandeira N., Wang M, & Dorrestein P.C. (2020). Feature-Based Molecular Networking in the GNPS Analysis Environment. Nature methods, 17(9), 905-908.

Publication 2020

Ccms Ecosystem Library Mass spectrometry Retention Scan

Corresponding Organization :

Other organizations : University of California, San Diego, University of Münster, Friedrich Schiller University Jena, Eurac Research, Institute for Biomedicine, European Molecular Biology Laboratory, RIKEN Center for Sustainable Resource Science, University of Tübingen, University of Geneva, Bruker (Germany), Université Paris Cité, Centre National de la Recherche Scientifique, Délégation Paris 5, Georgia Institute of Technology, St Petersburg University, Waters (United States), Czech Academy of Sciences, Institute of Microbiology, Czech Academy of Sciences, Université Grenoble Alpes, Centre Hospitalier Universitaire de Grenoble, Institut polytechnique de Grenoble, Translational Innovation in Medicine and Complexity, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología, University of Oklahoma, Carnegie Mellon University, University of West Alabama, Leibniz Institute of Plant Biochemistry, Université de Corse Pascal Paoli, University of Montana, University of Colorado Denver, Whitehead Institute for Biomedical Research, Michigan State University, University of Glasgow, Helmholtz Zentrum München, Kangwon National University

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Variable analysis

independent variables

The FBMN method consists of two main steps: 1) LC-MS feature detection and alignment

dependent variables

Following step 1 (feature detection and alignment), two files are exported: a feature quantification table (.TXT format) and a MS^2 spectral summary (.MGF format).
The feature quantification table contains information about LC-MS features across all considered samples including a unique identifier (Feature ID) for each feature, m/z value, retention time, and intensity.
The MS^2 spectral summary contains a list of MS^2 spectra, with one representative MS^2 spectrum per feature.

control variables

No control variables were explicitly mentioned.

positive controls

No positive controls were explicitly mentioned.

negative controls

No negative controls were explicitly mentioned.

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