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DISGENET database incorporates data from:

1. DTO

Drug Target Ontology. Lin, Yu, et al. “Drug Target Ontology to Classify and Integrate Drug Discovery Data”. Journal of Biomedical Semantics 2017 8:50.

https://doi.org/10.1186/s13326-017-0161-x

DTO can be downloaded from GitHub at https://github.com/DrugTargetOntology/DTO

Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/)

2. HPO

Human Phenotype Ontology (data-version: hp/releases/2022-06-11).

Find out more at http://www.human-phenotype-ontology.org

Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, Danis D, Balagura G, Baynam G, Brower AM, Callahan TJ, Chute CG, Est JL, Galer PD, Ganesan S, Griese M, Haimel M, Pazmandi J, Hanauer M, Harris NL, Hartnett MJ, Hastreiter M, Hauck F, He Y, Jeske T, Kearney H, Kindle G, Klein C, Knoflach K, Krause R, Lagorce D, McMurry JA, Miller JA, Munoz-Torres MC, Peters RL, Rapp CK, Rath AM, Rind SA, Rosenberg AZ, Segal MM, Seidel MG, Smedley D, Talmy T, Thomas Y, Wiafe SA, Xian J, Yüksel Z, Helbig I, Mungall CJ, Haendel MA, Robinson PN. The Human Phenotype Ontology in 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D1207-D1217. doi: 10.1093/nar/gkaa1043. PMID: 33264411; PMCID: PMC7778952.

3. Uniprot

The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2023, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D523–D531, https://doi.org/10.1093/nar/gkac1052

Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/)

All databases and documents in the UniProt FTP directory may be copied and redistributed freely, without advance permission, provided this copyright statement is reproduced with each copy. https://ftp.uniprot.org/pub/databases/uniprot/

4. Orphanet
Free access data from Orphanet. © INSERM 1999. Available on https://www.orphadata.com

Data version (date=”2022-06-14 16:27:47″ version=”1.3.16 / 4.1.7 [2022-01-26] (orientdb version)”).

5. GWASCAT

Carey V (2022). gwascat: representing and modeling data in the EMBL-EBI GWAS catalog. R package version 2.30.0.

https://www.ebi.ac.uk/about/terms-of-use

6. NLM MEDLINE

Courtesy of the U.S. National Library of Medicine.

7. CLINICATRIAL.GOV

Data accessed on 19/01/2022, ClinicalTrials.gov from United States Government.

8. MeSH

MSH2023_2022_07_27 – Medical Subject Headings (MeSH) 2023 U.S. National Library of Medicine; July 27, 2022; Bethesda, MD.

Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004 Jan 1; 32 (Database issue): D267-70. doi: 10.1093/nar/gkh061. PubMed PMID: 14681409; PubMed Central PMCID: PMC308795.

Some material in the UMLS Metathesaurus is from copyrighted sources of the respective copyright holders. Users of the UMLS Metathesaurus are solely responsible for compliance with any copyright, patent or trademark restrictions and are referred to the copyright, patent or trademark notices appearing in the original sources, all of which are hereby incorporated by reference.

9. UMLS

UMLS® Metathesaurus® National Library of Medicine, Department of Health and Human Services (IDENTIFY SPECIFICS) 2022 AB November 7, 2022.

Some material in the UMLS Metathesaurus is from copyrighted sources of the respective copyright holders. Users of the UMLS Metathesaurus are solely responsible for compliance with any copyright, patent or trademark restrictions andare referred to the copyright, patent or trademark notices appearing in the original sources, all of which are hereby incorporated by reference.

https://uts.nlm.nih.gov/uts/assets/LicenseAgreement.pdf

10. NCIT

The NCI Thesaurus™ is produced by the Enterprise Vocabulary Services group of the Center for Biomedical Informatics and Information Technology, National Cancer Institute, Maryland, USA. https://ncithesaurus.nci.nih.gov/ncitbrowser/

Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/)

11. SIO

The Semanticscience Integrated Ontology (SIO).

Michel Dumontier, Christopher JO Baker, Joachim Baran, Alison Callahan, Leonid Chepelev, José Cruz-Toledo, Nicholas R Del Rio, Geraint Duck, Laura I Furlong, Nichealla Keath, Dana Klassen, Jamie P McCusker, Núria Queralt-Rosinach, Matthias Samwald, Natalia Villanueva-Rosales, Mark D Wilkinson & Robert Hoehndorf.

https://github.com/MaastrichtU-IDS/semanticscience

Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/)

12. DCMI Metadata

Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/)

13. MONDO

Unifying diseases for the world, by the world, Nicole A Vasilevsky, Nicolas A Matentzoglu, Sabrina Toro, Joseph E Flack IV, Harshad Hegde, Deepak R Unni, Gioconda F Alyea, Joanna S Amberger, Larry Babb, James P Balhoff, Taylor I Bingaman, Gully A Burns, Orion J Buske, Tiffany J Callahan, Leigh C Carmody, Paula Carrio Cordo, Lauren E Chan, George S Chang, Sean L Christiaens, Louise C Daugherty, Michel Dumontier, Laura E Failla, May J Flowers, H. Alpha Garrett Jr., Jennifer L Goldstein, Dylan Gration, Tudor Groza, Marc Hanauer, Nomi L Harris, Jason A Hilton, Daniel S Himmelstein, Charles Tapley Hoyt, Megan S Kane, Sebastian Köhler, David Lagorce, Abbe Lai, Martin Larralde, Antonia Lock, Irene López Santiago, Donna R Maglott, Adriana J Malheiro, Birgit H M Meldal, Monica C Munoz-Torres, Tristan H Nelson, Frank W Nicholas, David Ochoa, Daniel P Olson, Tudor I Oprea, David Osumi-Sutherland, Helen Parkinson, Zoë May Pendlington, Ana Rath, Heidi L Rehm, Lyubov Remennik, Erin R Riggs, Paola Roncaglia, Justyne E Ross, Marion F Shadbolt, Kent A Shefchek, Morgan N Similuk, Nicholas Sioutos, Damian Smedley, Rachel Sparks, Ray Stefancsik, Ralf Stephan, Andrea L Storm, Doron Stupp, Gregory S Stupp, Jagadish Chandrabose Sundaramurthi, Imke Tammen, Darin Tay, Courtney L Thaxton, Eloise Valasek, Jordi Valls-Margarit, Alex H Wagner, Danielle Welter, Patricia L Whetzel, Lori L Whiteman, Valerie Wood, Colleen H Xu, Andreas Zankl, Xingmin Aaron Zhang, Christopher G Chute, Peter N Robinson, Christopher J Mungall, Ada Hamosh, Melissa A Haendel, medRxiv 2022.04.13.22273750; doi: https://doi.org/10.1101/2022.04.13.22273750

14. REACTOME PATHWAY DIAGRAM

Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/)

15. REACTOME PATHWAY ANALYSE SERVICE

Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/)

16. dbSNP

Databases are provided separately under the terms of the corresponding license, and Customer, as defined on Subscription Agreement, shall be considered a direct licensee of those datasets and agrees to Company to comply with the mentioned license.

Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD): National Center for Biotechnology Information, National Library of Medicine. (dbSNP Build ID: {151}).

Available from: http://www.ncbi.nlm.nih.gov/SNP/

https://opendatacommons.org/licenses/odbl/1-0

17. Phewas Catalog

Creative Commons Attribution 4.0 International (CC BY 4.0) License:

https://creativecommons.org/licenses/by/4.0

18. Mouse Genome Database (MGD)

Mouse Genome Database (MGD) at the Mouse Genome Informatics website, The Jackson Laboratory, Bar Harbor, Maine. World Wide Web (URL: http://www.informatics.jax.org). [August, 2023].

MGD data and annotations are licensed under a Creative Commons Attribution 4.0 International License (CC-BY)

19. Rat Genome Database (RGD)

RGD [Rattus norvergicus, https://download.rgd.mcw.edu/pub/data_release/annotated_rgd_objects_by_ontology/with_terms/rattus_terms_rdo and https://download.rgd.mcw.edu/data_release/RGD_ORTHOLOGS.txt ] Download file, Rat Genome Database Web Site, Medical College of Wisconsin, Milwaukee, Wisconsin. World Wide Web (URL: http://rgd.mcw.edu/). [Downloaded at 06:34PM on November 16, 2023]. RGD data and annotations are licensed under a Creative Commons Attribution 4.0 International License (CC-BY)

20. FinnGen GWAS

The FinnGen study is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The project is a collaboration between research organisations and biobanks within Finland and international industry partners.

We incorporate data from FinnGen GWAS Release 12 (November, 2024).

Kurki M.I., et al. FinnGen provides genetic insights from a well-phenotyped isolated population FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 2023 Jan;613(7944):508-518. doi: 10.1038/s41586-022-05473-8. Epub 2023 Jan 18.

21. UK Biobank GWAS/PheWAS

The UK Biobank GWAS/PheWAS data set was obtained from https://pheweb.org/UKB-TOPMed/phenotypes.

The PheWeb tool is described in: Gagliano Taliun, Sarah A., et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nature genetics 52.6 (2020): 550-552.

The TOPMed-imputed UK Biobank is introduced in: Taliun, Daniel, et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590.7845 (2021): 290-299.

22. Hancestro Ontology

Joannella Morales, Danielle Welter, Emily H. Bowler, Maria Cerezo, Laura W. Harris, Aoife C. McMahon, Peggy Hall, Heather A. Junkins, Annalisa Milano, Emma Hastings, Cinzia Malangone, Annalisa Buniello, Tony Burdett, Paul Flicek, Helen Parkinson, Fiona Cunningham, Lucia A. Hindorff and Jacqueline A. L. MacArthur. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biology, 2018, 19:21

https://github.com/ebispot/hancestro/

https://ebispot.github.io/hancestro/

Last modification: November 2025