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LipidLynxX: a data transfer hub to support integration of large scale lipidomics datasets

Zhixu NiiD, Maria FedorovaiD

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Abstract

Modern high throughput lipidomics provides large-scale datasets reporting hundreds of lipid molecular species. However, cross-laboratory comparison, meta-analysis, and systems biology integration of in-house generated and published datasets remain challenging due to a high diversity of used lipid annotation systems, different levels of reported structural information, and shortage in links to data integration resources. To support lipidomics data integration and interoperability of experimental lipidomics with data integration tools, we developed LipidLynxX serving as a hub facilitating data flow from high-throughput lipidomics analysis to systems biology data integration. LipidLynxX provides the possibility to convert, cross-match, and link various lipid annotations to the tools supporting lipid ontology, pathway, and network analysis aiming systems-wide integration and functional annotation of lipidome dynamics in health and disease. LipidLynxX is a flexible, customizable open-access tool freely available for download at https://github.com/SysMedOs/LipidLynxX.

Abstract summary

Claude Opus 5.5 · xhigh effort · 18 Oct 2026 · from the abstract · CC BY-NC-ND 4.0

LipidLynxX converts and cross-matches the many incompatible lipid naming systems, then links the identifiers to ontology, pathway and network tools so lipidomics datasets can be pooled.

High-throughput lipidomics reports hundreds of molecular species, but laboratories use different annotation systems and report structures at different levels of detail, which blocks cross-laboratory comparison, meta-analysis and reuse of published datasets. LipidLynxX is offered as the translation layer between experimental lipid annotations and the resources used for systems-level interpretation.

Key results

  1. ClaimedLipid identifiers written in different annotation systems, and at different levels of structural detail, can be converted and cross-matched to one another by the tool.

    Described as the core function of the hub; the abstract names no specific nomenclature standards, reports no conversion accuracy or coverage figures, and shows no example dataset. · Abstract

  2. ClaimedConverted identifiers are linked out to resources for lipid ontology, pathway and network analysis, which is what makes downstream systems-biology integration possible.

    Stated as the intended data flow from high-throughput lipidomics to integration tools; no downstream analysis, use case or validation is reported in the abstract. · Abstract

  3. ClaimedThe tool is open access, customisable, and distributed for download.

    Freely available at the authors' repository; no licence terms, version, dependency list or installation requirements are given in the abstract. · Abstract

Methods, data and reproducibility

A software hub was built that takes lipid annotations produced by high-throughput lipidomics workflows, converts and cross-matches them between annotation systems, and links them to tools used for ontology, pathway and network analysis.

Methods & tools
LipidLynxX
Reproducibility gaps
No version, dependency or environment information, and no example or test dataset, is mentioned in the abstract.
Limitations (2)
  • No evaluation is reported: the abstract gives no accuracy, coverage or round-trip consistency figures for the identifier conversion, and no comparison with existing lipid nomenclature converters. · noted in this summary
  • Which annotation systems, ontologies and pathway resources are supported is not stated, so applicability to a given dataset cannot be judged from the abstract. · noted in this summary

Read this if you merge lipidomics datasets from different laboratories or pipelines and keep losing time to mismatched lipid nomenclature.

Citations
50

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