ArticleMetabolomics : Official journal of the Metabolomic Society2020
Improving lipid mapping in Genome Scale Metabolic Networks using ontologies.
Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 22 citations in OpenAlex.
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Authors and funding
17 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTo interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids.
objectivesTo help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver.
methodsOur matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network. For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology.
resultsWe applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation.
conclusionThis new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.
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