Abstract

MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

Highlights

  • MetaNetX/MNXref provides computed cross-references between metabolites as well as biochemical reactions, to reconcile major public biochemical databases and a selection of public genome-scale metabolic networks (GSMN) from a few dedicated resources [1,2,3,4,5,6,7,8,9,10,11,12]

  • This paper focuses on the current status of the resource and the recent improvements accomplished through the complete redesign and rewriting of its production pipeline

  • By design a GSMN is a model of the metabolism of low molecular weight compounds, where mass conservation and thermodynamics apply [34, 35]

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Summary

Introduction

MetaNetX/MNXref provides computed cross-references between metabolites as well as biochemical reactions, to reconcile major public biochemical databases and a selection of public genome-scale metabolic networks (GSMN) from a few dedicated resources [1,2,3,4,5,6,7,8,9,10,11,12]. The initial motivation for creating this resource ten years ago was to add molecular structures to existing GSMNs. More generally, the goal was to establish cross-links between symbols in GSMNs published by different groups and the molecules found in the major biochemical databases. Merging metabolites in a metabolic network may lead to the merging of reactions, possibly altering the model properties, and the predictions that could be made using it

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