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RD-OMICS: An Integrative Multi-Omics Data Inventory in Rare Diseases.
Huanfei Wang1, Shixue Sun1, Ewy A Mathé1
1Informatics Core, Division of Preclinical Innovations, National Center for Advancing Translational Sciences, The National Institutes of Health, Rockville, USA.
Biorxiv : the Preprint Server for Biology
|July 10, 2026
Summary
RD-OMICS integrates fragmented omics data into a knowledge graph, enabling rare disease research and drug discovery. This resource facilitates the development of new treatments for rare conditions.
Area of Science:
- Bioinformatics
- Genomics
- Data Science
Background:
- Rare diseases affect millions, but treatment options are scarce due to research challenges.
- Fragmented and inconsistently annotated omics data hinder rare disease research and therapeutic development.
Purpose of the Study:
- To present RD-OMICS, a novel data inventory and knowledge graph for integrated rare disease omics data.
- To overcome limitations in rare disease research caused by data fragmentation and heterogeneity.
Main Methods:
- Developed a metadata harmonization pipeline using rule-based mapping and large language model (LLM)-assisted semantic categorization.
- Created a graph-based data model to integrate diverse omics data types (diseases, experiments, samples, platforms, projects, publications).
- Processed 11,049 Gene Expression Omnibus (GEO) series for 126 rare diseases.
Main Results:
- Integrated 11,049 GEO series, 375,930 biospecimen samples, 1,578 platforms, and 10,938 projects into the RD-OMICS knowledge graph.
- Demonstrated RD-OMICS utility in rare disease research, omics cohort construction, and drug repurposing for amyotrophic lateral sclerosis (ALS).
Conclusions:
- RD-OMICS provides a scalable, structured, and interoperable resource for rare disease omics data.
- Facilitates therapeutic development and translational discoveries by transforming fragmented data into actionable insights.
