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

Lennart F. Johansson; Steve Laurie; Dylan Spalding; Spencer Gibson; David Ruvolo; Coline Thomas; Davide Piscia; Fernanda de Andrade; Gerieke Been; Marieke Bijlsma; Han Brunner; Sandi Cimerman; Farid Yavari Dizjikan; Kornelia Ellwanger; Marcos Fernandez; Mallory Freeberg; Gert Jan van de Geijn; Roan Kanninga; Vatsalya Maddi; Mehdi Mehtarizadeh; Pieter Neerincx; Stephan Ossowski; Ana Rath; Dieuwke Roelofs-Prins; Marloes Stok-Benjamins; K. Joeri van der Velde; Colin Veal; Gerben van der Vries; Marc Wadsley; Gregory Warren; Birte Zurek; Thomas Keane; Holm Graessner; Sergi Beltran; Morris A. Swertz; Anthony J. Brookes; SOLVE-RD Consortium

doi:10.1093/gigascience/giae058

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

Lennart F. Johansson^*, Steve Laurie, Dylan Spalding, Spencer Gibson, David Ruvolo, Coline Thomas, Davide Piscia, Fernanda de Andrade, Gerieke Been, Marieke Bijlsma, Han Brunner, Sandi Cimerman, Farid Yavari Dizjikan, Kornelia Ellwanger, Marcos Fernandez, Mallory Freeberg, Gert Jan van de Geijn, Roan Kanninga, Vatsalya Maddi, Mehdi MehtarizadehPieter Neerincx, Stephan Ossowski, Ana Rath, Dieuwke Roelofs-Prins, Marloes Stok-Benjamins, K. Joeri van der Velde, Colin Veal, Gerben van der Vries, Marc Wadsley, Gregory Warren, Birte Zurek, Thomas Keane, Holm Graessner, Sergi Beltran, Morris A. Swertz, Anthony J. Brookes, SOLVE-RD Consortium

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	giae058
Journal	GigaScience
Volume	13
DOIs	https://doi.org/10.1093/gigascience/giae058
Publication status	Published - 2 Jan 2024

Keywords

bioinformatics
computational biology
fair data
genetics
infrastructure
rare disease

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10.1093/gigascience/giae058Licence: CC BY

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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., Dizjikan, F. Y., Ellwanger, K., Fernandez, M., Freeberg, M., van de Geijn, G. J., Kanninga, R., Maddi, V., ... SOLVE-RD Consortium (2024). An interconnected data infrastructure to support large-scale rare disease research. GigaScience, 13, Article giae058. https://doi.org/10.1093/gigascience/giae058

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title = "An interconnected data infrastructure to support large-scale rare disease research",

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 Caf{\'e} 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.",

keywords = "bioinformatics, computational biology, fair data, genetics, infrastructure, rare disease",

author = "Johansson, {Lennart F.} and Steve Laurie and Dylan Spalding and Spencer Gibson and David Ruvolo and Coline Thomas and Davide Piscia and {de Andrade}, Fernanda and Gerieke Been and Marieke Bijlsma and Han Brunner and Sandi Cimerman and Dizjikan, {Farid Yavari} and Kornelia Ellwanger and Marcos Fernandez and Mallory Freeberg and {van de Geijn}, {Gert Jan} and Roan Kanninga and Vatsalya Maddi and Mehdi Mehtarizadeh and Pieter Neerincx and Stephan Ossowski and Ana Rath and Dieuwke Roelofs-Prins and Marloes Stok-Benjamins and {van der Velde}, {K. Joeri} and Colin Veal and {van der Vries}, Gerben and Marc Wadsley and Gregory Warren and Birte Zurek and Thomas Keane and Holm Graessner and Sergi Beltran and Swertz, {Morris A.} and Brookes, {Anthony J.} and {SOLVE-RD Consortium}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024. Published by Oxford University Press GigaScience.",

year = "2024",

month = jan,

day = "2",

doi = "10.1093/gigascience/giae058",

language = "English",

volume = "13",

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Johansson, LF, Laurie, S, Spalding, D, Gibson, S, Ruvolo, D, Thomas, C, Piscia, D, de Andrade, F, Been, G, Bijlsma, M, Brunner, H, Cimerman, S, Dizjikan, FY, Ellwanger, K, Fernandez, M, Freeberg, M, van de Geijn, GJ, Kanninga, R, Maddi, V, Mehtarizadeh, M, Neerincx, P, Ossowski, S, Rath, A, Roelofs-Prins, D, Stok-Benjamins, M, van der Velde, KJ, Veal, C, van der Vries, G, Wadsley, M, Warren, G, Zurek, B, Keane, T, Graessner, H, Beltran, S, Swertz, MA, Brookes, AJ & SOLVE-RD Consortium 2024, 'An interconnected data infrastructure to support large-scale rare disease research', GigaScience, vol. 13, giae058. https://doi.org/10.1093/gigascience/giae058

TY - JOUR

T1 - An interconnected data infrastructure to support large-scale rare disease research

AU - Johansson, Lennart F.

AU - Laurie, Steve

AU - Spalding, Dylan

AU - Gibson, Spencer

AU - Ruvolo, David

AU - Thomas, Coline

AU - Piscia, Davide

AU - de Andrade, Fernanda

AU - Been, Gerieke

AU - Bijlsma, Marieke

AU - Brunner, Han

AU - Cimerman, Sandi

AU - Dizjikan, Farid Yavari

AU - Ellwanger, Kornelia

AU - Fernandez, Marcos

AU - Freeberg, Mallory

AU - van de Geijn, Gert Jan

AU - Kanninga, Roan

AU - Maddi, Vatsalya

AU - Mehtarizadeh, Mehdi

AU - Neerincx, Pieter

AU - Ossowski, Stephan

AU - Rath, Ana

AU - Roelofs-Prins, Dieuwke

AU - Stok-Benjamins, Marloes

AU - van der Velde, K. Joeri

AU - Veal, Colin

AU - van der Vries, Gerben

AU - Wadsley, Marc

AU - Warren, Gregory

AU - Zurek, Birte

AU - Keane, Thomas

AU - Graessner, Holm

AU - Beltran, Sergi

AU - Swertz, Morris A.

AU - Brookes, Anthony J.

AU - SOLVE-RD Consortium

PY - 2024/1/2

Y1 - 2024/1/2

N2 - 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.

AB - 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.

KW - bioinformatics

KW - computational biology

KW - fair data

KW - genetics

KW - infrastructure

KW - rare disease

U2 - 10.1093/gigascience/giae058

DO - 10.1093/gigascience/giae058

M3 - Article

VL - 13

JO - GigaScience

JF - GigaScience

M1 - giae058

ER -

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

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Keywords

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