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Updated: Jul 12, 2026

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

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.

Keywords:

data harmonization knowledge graph large language models (LLMs)multi-omics data integration rare diseases

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Published on: April 4, 2018

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Last Updated: Jul 12, 2026

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

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.