Academic Journal
An interconnected data infrastructure to support large-scale rare disease research.
| العنوان: | An interconnected data infrastructure to support large-scale rare disease research. |
|---|---|
| المؤلفون: | 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 |
| المصدر: | GigaScience; 2024, Vol. 13 Issue 1, p1-14, 14p |
| مصطلحات موضوعية: | COMPUTATIONAL biology, RARE diseases, NUCLEOTIDE sequencing, MULTIOMICS, GENETICS, METADATA |
| مستخلص: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of GigaScience is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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