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Ehrhart F, Willighagen EL, Kutmon M, et al. A resource to explore the discovery of rare diseases and their causative genes. Sci Data. 2021;8(1):124doi: 10.1038/s41597-021-00905-y.
Ehrhart, F., Willighagen, E. L., Kutmon, M., van Hoften, M., Curfs, L. M. G., & Evelo, C. T. (2021). A resource to explore the discovery of rare diseases and their causative genes. Scientific data, 8(1), 124. https://doi.org/10.1038/s41597-021-00905-y
Ehrhart, Friederike, et al. "A resource to explore the discovery of rare diseases and their causative genes." Scientific data vol. 8,1 (2021): 124. doi: https://doi.org/10.1038/s41597-021-00905-y
Ehrhart F, Willighagen EL, Kutmon M, van Hoften M, Curfs LMG, Evelo CT. A resource to explore the discovery of rare diseases and their causative genes. Sci Data. 2021 May 04;8(1):124. doi: 10.1038/s41597-021-00905-y. PMID: 33947870; PMCID: PMC8096966.
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Sci Data. 2021 May 04;8(1):124. doi: 10.1038/s41597-021-00905-y.

A resource to explore the discovery of rare diseases and their causative genes.

Scientific data

Friederike Ehrhart, Egon L Willighagen, Martina Kutmon, Max van Hoften, Leopold M G Curfs, Chris T Evelo

Affiliations

  1. Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands. [email protected].
  2. Governor Kremers Centre - Rett Expertise Centre, Maastricht University Medical Center, Maastricht, The Netherlands. [email protected].
  3. Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.
  4. Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
  5. Governor Kremers Centre - Rett Expertise Centre, Maastricht University Medical Center, Maastricht, The Netherlands.

PMID: 33947870 PMCID: PMC8096966 DOI: 10.1038/s41597-021-00905-y

Abstract

Here, we describe a dataset with information about monogenic, rare diseases with a known genetic background, supplemented with manually extracted provenance for the disease itself and the discovery of the underlying genetic cause. We assembled a collection of 4166 rare monogenic diseases and linked them to 3163 causative genes, annotated with OMIM and Ensembl identifiers and HGNC symbols. The PubMed identifiers of the scientific publications, which for the first time described the rare diseases, and the publications, which found the genes causing the diseases were added using information from OMIM, PubMed, Wikipedia, whonamedit.com, and Google Scholar. The data are available under CC0 license as spreadsheet and as RDF in a semantic model modified from DisGeNET, and was added to Wikidata. This dataset relies on publicly available data and publications with a PubMed identifier, but by our effort to make the data interoperable and linked, we can now analyse this data. Our analysis revealed the timeline of rare disease and causative gene discovery and links them to developments in methods.

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