A unified data infrastructure to support large-scale rare disease research
Johansson, L. F.; Laurie, S.; Spalding, D.; Gibson, S.; Ruvolo, D.; Thomas, C.; Piscia, D.; de Andrade, F.; Been, G.; Bijlsma, M.; Brunner, H.; Cimerman, S.; Yavari Dizjikan, F.; Ellwanger, K.; Fernandez, M.; Freeberg, M.; van de Geijn, G.-J.; Kanninga, R.; Maddi, V.; Medtarizadeh, M.; Neerincx, P.; Ossowski, S.; Rath, A.; Roelofs-Prins, D.; Stok-Benjamins, M.; van der Velde, K. J.; Veal, C.; van der Vries, G.; Wadsley, M.; Warren, G.; Zurek, B.; Keane, T.; Graessner, H.; Solve-RD, ; Beltran, S.; Swertz, M. A.; Brookes, A. J.
Show 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-analysing data from thousands of RD cases, including phenotypes, pedigrees, exome/genome sequencing and multi-omics 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 analyse data and metadata in a collaborative manner. Pseudonymised phenotypic and raw experimental data are submitted to the RD-Connect Genome-Phenome Analysis Platform and processed through standardised 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 Cafe Variome "Discovery Nexus" connect data and metadata and offer discovery services, and secure cloud-based "Sandboxes" support multi-party data analysis. This proven infrastructure design provides a blueprint for other projects that need to analyse large amounts of heterogeneous data.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.