NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #41
Submission information
Submission Number: 41
Submission ID: 145273
Submission UUID: e3c93020-4ccd-4e9c-87e2-7106df727322
Submission URI: /nci/ods-data-jamboree/abstractsubmissions
Submission Update: /nci/ods-data-jamboree/abstractsubmissions?token=xE-rkhWZsA-7HzxgA78uRlhOCHheJQjOtgL9eouAwgg
Created: Tue, 06/24/2025 - 11:46
Completed: Tue, 06/24/2025 - 11:47
Changed: Wed, 06/25/2025 - 10:36
Remote IP address: 10.208.28.5
Submitted by: Anonymous
Language: English
Is draft: No
| First Name | Minghong |
|---|---|
| Middle Initial | |
| Last Name | Ward |
| Degree(s) | MS. Electrical and Computer Engineering |
| Position/Title/Career Status | Product Owner for dbGaP FHIR Product |
| Organization | NLM/NCBI |
| Organization Address | BETHESDA |
| wardming@nih.gov | |
| Other (Please Specify) | |
| Abstract Category | Methods to enable data interoperability |
| Abstract Keywords | dbGaP, FHIR, DRS, Cloud-computing, Interoperability |
| Abstract Title | Making dbGaP data interoperable and analysis-ready with FHIR and DRS API |
| Abstract Summary | The NIH’s database of Genotypes and Phenotypes (dbGaP) includes data from 3,000+ studies across 800 diseases/focuses, involving 4 million participants and 450,000+ phenotype variables. dbGaP has supported over 8,000 publications. As researchers increasingly rely on cloud platforms to conduct cross-study analysis, both interoperability and ease of access are urgently needed. We developed FHIR (Fast Healthcare Interoperability Resources) API for dbGaP to deliver both open-access and controlled-access dbGaP data via FHIR. The open-access API provides programmatic access to study metadata, enabling researchers to discover relevant datasets for data discovery. Controlled-access API deliver over 1.1 billion phenotypic observations and molecular sequence files through persistent URLs using the GA4GH Data Repository Service (DRS), another global standard. We will show how a simple Python script in a Jupyter notebook can perform phenotype-driven statistical analysis across multiple datasets and repositories using the FHIR API. This approach enhances data reuse, facilitates cohort building, and helps accelerate reproducible research at scale. |
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