The Experimental Natural Products Knowledge Graph (ENPKG) workflow aims at integrating experimental LC-MS/MS DDA metabolomics data into a Wikidata-connected knowledge graph. To allow for the iterative addition of samples over time, data from each sample is processed individually.

Data are processed in different independent modules (for molecular networking, structural annotations, etc.) before being formatted as interoperable sample-specific RDF knowledge graphs.

We recommend you start with enpkg_full for an overview of the whole process.

If you use it, please cite the following:
Gaudry, A., Pagni, M., Mehl, F., Moretti, S., Quiros-Guerrero, L.-M., Cappelletti, L., Rutz, A., Kaiser, M., Marcourt, L., Queiroz, E. F., Ioset, J.-R., Grondin, A., David, B., Wolfender, J.-L., & Allard, P.-M. (2024). A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery. In ACS Central Science. American Chemical Society (ACS)

... and the different tools-specific papers (see the different repositories)!