MetaNetMap: automatic mapping of metabolomic data onto metabolic networks

MotivationMetabolic networks represent genome-derived information about the biochemical reactions that cells are capable of performing. Mapping omic data onto these networks is important to refine model simulations. However, metabolomic data mapping remains very challenging due to difficulties in identifier reconciliation between annotation profiles and metabolic networks. ResultsMetaNetMap is a Python package designed to automatise the process of mapping metabolomic data onto metabolic networks. It includes several layers of identifier matching, the use of customisable databases, and molecular ontology integration to suggest the most matches between experimentally-identified metabolites and molecules defined in the network. We demonstrate its usability and the quality of automated mapping using two datasets. Availability and ImplementationMetaNetMap is an open source python package and publicly available under the GPLv3 licence. Source code is freely available on GitHub: https://github.com/coraliemuller/metanetmap. Data and code used in the application cases of this paper can be found at: https://doi.org/10.57745/ESFLR8.