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 MS
2 spectra, with one representative MS
2 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 MS
2 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).
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.
