2026/01/05 by Coralie Muller, Juliette Audemard, Sylvain Prigent +1 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Machine Learning in Bioinformatics #Metabolomics and Mass Spectrometry Studies #Microbial Metabolic Engineering and Bioproduction
paper · pdf · doi:10.64898/2026.01.05.697697
openalex created_date 2025/12/17 · openalex publication_date 2026/01/05 · openalex updated_date 2026/08/01
Motivation Metabolic 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. Results MetaNetMap 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 Implementation MetaNetMap 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 .