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Updated: Feb 19, 2026

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data.

Chao Zhang1, Jochem Bijlard1,2, Christine Staiger3

  • 1Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, Netherlands.

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|November 11, 2017
PubMed
Summary

A new data ecosystem links raw and interpreted molecular profiling data using the European Genome-phenome Archive (EGA) and tranSMART. This enables efficient data management and reanalysis, promoting data reuse through FAIR persistent identifiers.

Keywords:
EGAFAIRGalaxydata managementreproducibilitytranSMARTtranslational researchworkflows

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Area of Science:

  • Bioinformatics
  • Data Science
  • Genomics

Background:

  • High-throughput molecular profiling generates complex data requiring advanced computational interpretation.
  • Explosive data growth necessitates robust data management for efficient organization and integration.
  • Existing systems often lack seamless integration between raw and interpreted data.

Purpose of the Study:

  • To design and implement a data ecosystem for linking raw and interpreted molecular profiling data.
  • To establish a framework for efficient data management and computational workflow execution.
  • To facilitate data discovery, traceability, and reanalysis for clinical research.

Main Methods:

  • Established an ELIXIR implementation study in collaboration with the Translational research IT (TraIT) programme.
  • Utilized the European Genome-phenome Archive (EGA) for raw data storage.
  • Employed tranSMART for interpreted and clinical data, and Galaxy for computational workflows.
  • Integrated data by systematically linking repositories and using FAIR persistent identifiers.

Main Results:

  • Developed a data ecosystem integrating EGA, tranSMART, and Galaxy.
  • Successfully structured TraIT Cell Line Use Case (TraIT-CLUC) data for storage and cross-referencing.
  • Enabled data flow from EGA to Galaxy for raw data reanalysis.
  • Demonstrated user ability to select cohorts in tranSMART, trace to raw data, and perform reanalysis in Galaxy.

Conclusions:

  • The proposed data ecosystem effectively links raw and interpreted molecular profiling data.
  • FAIR persistent identifiers are crucial for stable data linkage and reuse across different data ontology levels.
  • This approach enhances data discoverability, traceability, and reanalysis capabilities in clinical research.