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An interconnected data infrastructure to support large-scale rare disease research.

Lennart F Johansson1, Steve Laurie2,3, Dylan Spalding4

  • 1Department of Genetics, University of Groningen, University Medical Center Groningen, HPC CB50, P.O. Box 30001, Groningen, 9700 RB, The Netherlands.

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Summary

The Solve-RD project developed a data infrastructure to improve rare disease diagnosis by analyzing diverse patient data. This collaborative platform enables researchers to store, connect, and analyze genetic and phenotypic information, increasing diagnostic success rates.

Keywords:
bioinformaticscomputational biologyfair datageneticsinfrastructurerare disease

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Area of Science:

  • Genomics
  • Bioinformatics
  • Rare Diseases

Background:

  • Rare diseases (RDs) present diagnostic challenges due to their complexity and heterogeneity.
  • Collaborative efforts are crucial for increasing the diagnostic success rate of rare diseases.

Purpose of the Study:

  • To design and implement a robust data infrastructure for the Solve-RD project.
  • To facilitate the co-analysis of diverse data types for rare disease diagnosis.

Main Methods:

  • Development of a collaborative data infrastructure integrating pseudonymized phenotypic, pedigree, exome/genome sequencing, and multiomics data.
  • Utilized the RD-Connect Genome-Phenome Analysis Platform for standardized data processing.
  • Leveraged the European Genome-Phenome Archive for data storage and access.
  • Employed MOLGENIS "RD3" and Café Variome "Discovery Nexus" for data and metadata connection and discovery.
  • Established secure cloud-based "Sandboxes" for multiparty data analysis.

Main Results:

  • Successfully deployed a functional data infrastructure supporting collaborative analysis of large, heterogeneous datasets.
  • Enabled secure storage, retrieval, connection, and analysis of rare disease data.
  • Facilitated standardized processing of omics data and integration with existing archives.

Conclusions:

  • The developed infrastructure provides a scalable and effective model for rare disease data co-analysis.
  • This blueprint can guide other large-scale projects dealing with complex, multi-modal data.
  • The infrastructure enhances the potential for solving rare diseases through collaborative data science.