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

Research output: Contribution to journalResearch articleContributed

Contributors

  • University Medical Center Groningen
  • University of Barcelona
  • European Molecular Biology Laboratory (EMBL) Hinxton
  • University of Leicester
  • Maastricht University Medical Centre (UMC+)
  • University of Tübingen
  • INSERM - Institut national de la santé et de la recherche médicale

Abstract

The Solve-RD project brings together clinicians, scientists, and patient representatives from 51 institutes spanning 15 countries to collaborate on genetically diagnosing ("solving") rare diseases (RDs). The project aims to significantly increase the diagnostic success rate by co-analyzing data from thousands of RD cases, including phenotypes, pedigrees, exome/genome sequencing, and multiomics data. Here we report on the data infrastructure devised and created to support this co-analysis. This infrastructure enables users to store, find, connect, and analyze data and metadata in a collaborative manner. Pseudonymized phenotypic and raw experimental data are submitted to the RD-Connect Genome-Phenome Analysis Platform and processed through standardized pipelines. Resulting files and novel produced omics data are sent to the European Genome-Phenome Archive, which adds unique file identifiers and provides long-term storage and controlled access services. MOLGENIS "RD3" and Café Variome "Discovery Nexus" connect data and metadata and offer discovery services, and secure cloud-based "Sandboxes" support multiparty data analysis. This successfully deployed and useful infrastructure design provides a blueprint for other projects that need to analyze large amounts of heterogeneous data.

Details

Original languageEnglish
Article numbergiae058
JournalGigascience
Volume13
Publication statusPublished - 2 Jan 2024
Peer-reviewedNo

External IDs

PubMedCentral PMC11413801
Scopus 85204512062

Keywords

Keywords

  • Computational Biology/methods, Databases, Genetic, Genomics/methods, Humans, Metadata, Phenotype, Rare Diseases/genetics