Poster ID
P-06
Poster Title
From Registry to Beacon: Standardizing Multi-Hub Clinical Registry Data into GA4GH Beacon
Authors
Abdullah Faqih Al Mubarok*
Afif Hanidar Maruf*
Renata Triwijaya*
Dzakwanil Hakim*
Rahma Rizky Alifia*
Irene Lorinda Indalao, PhD*
Ines Irene Atmosukarto, PhD*
Indri Rooslamiati Supriadi, MSc, Apt.*
*Center for Biomedical and Health Genomics
Afif Hanidar Maruf*
Renata Triwijaya*
Dzakwanil Hakim*
Rahma Rizky Alifia*
Irene Lorinda Indalao, PhD*
Ines Irene Atmosukarto, PhD*
Indri Rooslamiati Supriadi, MSc, Apt.*
*Center for Biomedical and Health Genomics
Abstract
Background. Indonesia's Biomedical and Genome Science Initiative (BGSI) collects rich phenotypic and specimen data through disease-based Hubs, each running its own case-report-form (CRF)-based clinical registry. In raw form, these registries are hub-specific, locale-encoded, and not directly queryable for cross-hub or international genomic discovery. BGSI's Trusted Research Environment (TRE), which is still under development, intends to host federated-compatible analysis. Consequently, standardization is a priority, and successful harmonization of registries will prevent deferring curation.
Objective. We built a Registry-to-Beacon pipeline that converts heterogeneous, multi-hub registry data into GA4GH Beacon v2-compliant documents, giving BGSI a standardized, ontology-anchored data layer.
Pipeline. For each Hub, registry records are extracted and transformed into entity-relationship data conforming to the GA4GH Beacon v2 framework and model. Records are standardized into two layers: collections and entities. Collections comprise the dataset (pooling records with DUO-encoded data-use conditions) and the cohort (grouping participants by study population); entities comprise individuals, biosamples, runs, analyses, and genomicVariations. To ensure seamless data sharing, custom tables map every field to recognized global ontologies (such as NCIT, ICD, or SNOMED) and resolve any regional encoding differences. Finally, all generated outputs are systematically validated against the Beacon v2 schema.
Status. The pipeline runs across BGSI's three active disease Hubs, producing schema-validated, hub-level Beacon v2 JSON ready schema.
Conclusion. By decoupling standardization from compute infrastructure readiness, this pipeline enables a national genomic initiative to begin GA4GH-compliant harmonization, offering a transferable pattern for other resource-constrained national initiatives building toward federated genomic discovery.
Keywords: GA4GH Beacon v2; genomic data sharing; data harmonization; ontology mapping; clinical registry; Data Use Ontology; federated genomic discovery; BGSI; Indonesia
Objective. We built a Registry-to-Beacon pipeline that converts heterogeneous, multi-hub registry data into GA4GH Beacon v2-compliant documents, giving BGSI a standardized, ontology-anchored data layer.
Pipeline. For each Hub, registry records are extracted and transformed into entity-relationship data conforming to the GA4GH Beacon v2 framework and model. Records are standardized into two layers: collections and entities. Collections comprise the dataset (pooling records with DUO-encoded data-use conditions) and the cohort (grouping participants by study population); entities comprise individuals, biosamples, runs, analyses, and genomicVariations. To ensure seamless data sharing, custom tables map every field to recognized global ontologies (such as NCIT, ICD, or SNOMED) and resolve any regional encoding differences. Finally, all generated outputs are systematically validated against the Beacon v2 schema.
Status. The pipeline runs across BGSI's three active disease Hubs, producing schema-validated, hub-level Beacon v2 JSON ready schema.
Conclusion. By decoupling standardization from compute infrastructure readiness, this pipeline enables a national genomic initiative to begin GA4GH-compliant harmonization, offering a transferable pattern for other resource-constrained national initiatives building toward federated genomic discovery.
Keywords: GA4GH Beacon v2; genomic data sharing; data harmonization; ontology mapping; clinical registry; Data Use Ontology; federated genomic discovery; BGSI; Indonesia
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