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Enhancing semantic interoperability in precision medicine: converting OMOP CDM to Beacon v2 in the Spanish IMPaCT-Data project

This is the companion repository for our BMC Medical Informatics and Decision Making article. It provides a tutorial for the paper's file-based workflow: converting OMOP CDM 5.4 SQL exports or CSV tables into JSON for the individuals entity of the GA4GH Beacon v2 Models.

The paper also describes an on-the-fly architecture that connects a PostgreSQL OMOP CDM database directly to a Beacon v2 API. That architecture is outside the scope of this tutorial.

Tutorial

The notebook uses synthetic EUNOMIA data and covers:

  • installation of the tested Convert-Pheno 0.34 release;
  • SQL-dump conversion and export of parsed OMOP tables as CSV;
  • standard and memory-efficient streaming conversion;
  • selection of specific OMOP CDM tables; and
  • calculation of conversion-completeness statistics.

Run the interactive version in Google Colab:

Open in Google Colab

The version tracked in this repository is nb/omop_cdm_2_beacon_v2_tutorial.ipynb.

The notebook is designed for a Linux-based Google Colab runtime: it uses /content, apt-get, and google.colab. To use it in a local Jupyter environment, adapt those Colab-specific paths and cells. Generated files are written to /content and are removed when the Colab runtime is recycled.

Caution

The tutorial data are synthetic. Do not upload confidential, personal, or patient-identifiable data to a managed Colab runtime unless that use has been approved by your institution and complies with the applicable governance and data-protection requirements. Use an appropriately controlled environment for sensitive data.

Convert-Pheno

Convert-Pheno performs the file-based transformation demonstrated in the notebook:

Citation

If you use this tutorial in your work, please cite:

Rueda M, Ramírez-Anguita JM, López-Sánchez V, et al. Enhancing semantic interoperability in precision medicine: converting OMOP CDM to Beacon v2 in the Spanish IMPaCT-Data project. BMC Medical Informatics and Decision Making. 2026.

https://doi.org/10.1186/s12911-026-03649-0

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