Considering these issues, only a small fraction of the information represented in most metabolic datasets can be translated into interpretable evidence for further data analysis. Unfortunately, data analysis is a prominent bottleneck of all omics datasets, which requires computational tools to manage, process, and visualize data to support the interpretation of the substantial amount of data generated by current techniques . Pathway analysis is a commonly used approach for transcriptomics data analysis , which can also be used to discover the biological origin of biochemical markers or explore the molecular mechanisms influencing metabolic changes. However, the small data size remaining after processing raw metabolomics data into annotated compounds hinders biological interpretation, pathway statistics, and visualization of the data. Furthermore, connecting chemical compounds in pathways to the measured entities can be obscured by small annotation differences of chemical structures, e.g. fully defined stereochemistry versus uncertainties thereof. Last, knowledge of biochemical conversions is dispersed over multiple databases which can strongly affect results depending on the input data and parameters used .