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

• Published in: Gigascience Vol. 13
• Main Authors: Johansson, Lennart F, Laurie, Steve, Spalding, Dylan, Gibson, Spencer, Ruvolo, David, Thomas, Coline, Piscia, Davide, de Andrade, Fernanda, Been, Gerieke, Bijlsma, Marieke, Brunner, Han, Cimerman, Sandi, Dizjikan, Farid Yavari, Ellwanger, Kornelia, Fernandez, Marcos, Freeberg, Mallory, van de Geijn, Gert-Jan, Kanninga, Roan, Maddi, Vatsalya, Mehtarizadeh, Mehdi, Neerincx, Pieter, Ossowski, Stephan, Rath, Ana, Roelofs-Prins, Dieuwke, Stok-Benjamins, Marloes, van der Velde, K Joeri, Veal, Colin, van der Vries, Gerben, Wadsley, Marc, Warren, Gregory, Zurek, Birte, Keane, Thomas, Graessner, Holm, Beltran, Sergi, Swertz, Morris A, Brookes, Anthony J
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 02.01.2024
• Subjects: Cloud computing; Collaboration; Computational Biology - methods; Data analysis; Databases, Genetic; Gene sequencing; Genomes; Genomic analysis; Genomics - methods; Humans; Infrastructure; Metadata; Phenotype; Phenotypes; Rare diseases; Rare Diseases - genetics; Whole genome sequencing; computational biology; fair data; rare disease; genetics; infrastructure; bioinformatics
• ISSN: 2047-217X; 2047-217X
• DOI: 10.1093/gigascience/giae058

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.