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Introduction <p>Today, metabolomics literature suffers from ambiguity in metabolites’ nomenclature, making difficult intercomparison between publications and can lead to misinterpretations. Progress in the implementation of FAIR principles in metabolomics in various&#xa0;scientific communities is&#xa0;therefore&#xa0;imperative for&#xa0;successful comparisons across studies and for moving towards more large-scale metabolomics. </p> Objectives <p>In this context, the aim of the present work was to explore the potential ambiguities that may be introduced during metabolite contextualization and reporting, and finally provide operational guidelines for metabolite name and identifier conversion to increase interoperability in metabolomics. </p> Methods <p>From a list of 100 frequently annotated metabolites in human plasma, but also relevant for plant sciences, several workflows based on different existing identifier conversion tools were set up and evaluated, using two alternative approaches, one from an experimenter and the other from a data scientist's perspective. </p> Results <p>Findings showed a high level of mismatches using metabolite names as input, whereas starting from identifiers showed heterogeneity in the conversion consistency, depending on the association between input identifiers, algorithm of the selected tool, their respective versions, as well as versions of databases used for mapping. Errors in cross-reference databases were also highlighted. Despite these facts, InChIKeys were found to provide the highest quality results using all identifier conversion tools. </p> Conclusion <p>From these results, operational guidelines were proposed using a curation process based on computational iterations, testing the stability and consistency of this conversion process, thus guaranteeing future metabolite contextualisation (e.g. links with pathways or phenotypes) and the interoperability of result reports.</p>

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Metabolite names and identifiers: how far are we from interoperability?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月20日

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  • Ghina Hajjar,
  • Franck Giacomoni,
  • Mathieu Umec,
  • Marie Lefebvre,
  • Mariana P. C. Pimentel,
  • Sylvain Prigent,
  • Cécile Cabasson,
  • Monica Chagoyen,
  • Pierre Pétriacq,
  • Blandine Comte,
  • Estelle Pujos-Guillot

摘要

Introduction

Today, metabolomics literature suffers from ambiguity in metabolites’ nomenclature, making difficult intercomparison between publications and can lead to misinterpretations. Progress in the implementation of FAIR principles in metabolomics in various scientific communities is therefore imperative for successful comparisons across studies and for moving towards more large-scale metabolomics.

Objectives

In this context, the aim of the present work was to explore the potential ambiguities that may be introduced during metabolite contextualization and reporting, and finally provide operational guidelines for metabolite name and identifier conversion to increase interoperability in metabolomics.

Methods

From a list of 100 frequently annotated metabolites in human plasma, but also relevant for plant sciences, several workflows based on different existing identifier conversion tools were set up and evaluated, using two alternative approaches, one from an experimenter and the other from a data scientist's perspective.

Results

Findings showed a high level of mismatches using metabolite names as input, whereas starting from identifiers showed heterogeneity in the conversion consistency, depending on the association between input identifiers, algorithm of the selected tool, their respective versions, as well as versions of databases used for mapping. Errors in cross-reference databases were also highlighted. Despite these facts, InChIKeys were found to provide the highest quality results using all identifier conversion tools.

Conclusion

From these results, operational guidelines were proposed using a curation process based on computational iterations, testing the stability and consistency of this conversion process, thus guaranteeing future metabolite contextualisation (e.g. links with pathways or phenotypes) and the interoperability of result reports.