Automated mapping of metabolomic compounds onto metabolic networks using MetaNetMap
Cartographie automatisée des composés métabolomiques sur les réseaux métaboliques à l'aide de MetaNetMap
Résumé
Understanding biological systems requires integrative and multi-level approaches. Genome Scale Metabolic Networks (GSMNs), that are derived from genome annotation, capture the metabolic capabilities of an organism. In contrast, metabolomics gives an insight into what is really happening in an organism under specific conditions. Mapping molecules identified from metabolomic experiments onto GSMNs offers several advantages: mapped compounds can be used for visualisation or quality assessment of the GSMN; and conversly, unidentified metabolites highlight gaps in the network and create model curation opportunities. This is especially important for specialised metabolism that is currently largely overlooked in GSMNs. Such mapping is thus attractive but it remains cumbersome due to several challenges such as harmonisation and matching of identifiers between metabolomic annotation profiles and GSMNs, and dispersion of information across various knowledge bases and input files. Currently, mapping requires manual or semi-manual mapping, but it is quite fastidious and prone to errors. To overcome these challenges, we developed MetaNetMap, a Python package that automatically matches metabolite information between metabolomic annotations and GSMNs. It improves mapping rates through direct mapping taking into account metadata of input files, indirect matching by relying on conversion data tables built from third-party knowledge bases, and partial matching techniques. It offers an automatic solution for ambiguous mapping, providing relevant information for manual curation. By automating and harmonising metabolite mapping, MetaNetMap aims to overcome a major barrier in multi-omic integration, enabling more efficient and reproducible integration of metabolomic data onto GSMNs.
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