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Preparing Data at the Source to Foster Interoperability across Rare Disease Resources

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Rare Diseases Epidemiology: Update and Overview

Abstract

The ability to combine heterogeneous data distributed across the globe is critically important to boost research on rare diseases, but it presents a number of methodological, representational and automation challenges. In this scenario, biomedical ontologies are of critical importance for enabling computers to aid in information retrieval and analysis across data collections.

This chapter presents an approach to preparing rare disease data for integration through the application of a global standard for computer-readable data and knowledge. This includes the use of common data elements, ontological codes and computer-readable data. This approach was developed under a number of domain-relevant requirements, such as controlled access to data, independence of the original sources, and the desire to combining the data sources with other computational workflows and data platforms.

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References

  1. Bizer C, Heath T, Berners-Lee T (2009) Linked data-the story so far. Int J Semant Web Inf Syst 5(3):1–22

    Article  Google Scholar 

  2. Brochhausen M, Zheng J, Birtwell D, Williams H, Masci AM et al (2016) OBIB-a novel ontology for biobanking. J Biomed Semant 7(May):23

    Article  Google Scholar 

  3. Ceusters W (2012) An information artifact ontology perspective on data collections and associated representational artifacts. Stud Health Technol Inform 180:68–72

    PubMed  Google Scholar 

  4. International Rare Disease Research Consortium (IRDiRC) Policies and Guidelines, Long version (2013). Available from: http://www.irdirc.org/wp-content/uploads/2013/06/IRDiRC_policies_24MayApr2013.pdf. Accessed Dec 2016

  5. Ison J, Kalas M, Jonassen I, Bolser D, Uludag M et al (2013) EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics 29(10):1325–1332

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Jupp S, Malone J, Bolleman J, Brandizi M, Davies M et al (2014) The EBI RDF platform: linked open data for the life sciences. Bioinformatics 30(9):1338–1339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. López E, Thompson R, Gainotti S, Wang M, Rubinstein Y et al (2016) Overview of existing initiatives to develop and improve access and data sharing in rare disease registries and biobanks worldwide. Expert Opin Orphan Drugs 4(7):729–739

    Article  Google Scholar 

  8. Lynch C, Parastatidis S, Jacobs N, Van de Sompel H, Lagoze C (2007) The OAI-ORE Effort: Progress, Challenges, Synergies. Proceedings of the 7th ACM/IEEE-CS joint conference on digital libraries 80-80

    Google Scholar 

  9. Malone J, Holloway E, Adamusiak T, Kapushesky M, Zheng J et al (2010) Modeling sample variables with an experimental factor ontology. Bioinformatics 26(8):1112–1118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. McMurry J, Blomberg N, Burdett T, Conte N, Dumontier M et al (2015) 10 Simple rules for design, provision, and reuse of identifiers for web-based life science data. Zenodo. Available from: https://doi.org/10.5281/zenodo.31765. Accessed Dec 2016

  11. McMurry J, Köhler S, Washington NL, Balhoff JP, Borromeo C et al (2016) Navigating the phenotype frontier: the monarch initiative. Genetics 203(4):1491–1495

    Article  PubMed  PubMed Central  Google Scholar 

  12. Miles A, Bechhofer S (2009) SKOS Simple Knowledge Organization System Reference. World Wide Web Consortium. Available from: http://www.w3.org/TR/skos-reference/. Accessed Dec 2016

  13. Orphanet Standard Operating Procedures, Version 02.1 (2016) Available from: http://www.orpha.net/orphacom/special/eproc_SOPs_V2.pdf. Accessed Dec 2016

  14. Philippakis AA, Azzariti DR, Beltran S, Brookes AJ, Brownstein CA et al (2015) The matchmaker exchange: a platform for rare disease gene discovery. Hum Mutat 36(10):915–921

    Article  PubMed  PubMed Central  Google Scholar 

  15. Rath A, Olry A, Dhombres F, Brandt MM, Urbero B et al (2012) Representation of rare diseases in health information systems: the orphanet approach to serve a wide range of end users. Hum Mutat 33(5):803–808

    Article  PubMed  Google Scholar 

  16. RD-Connect “Bring Your Own Data (BYOD)” Workshop to Link Rare Disease Registries (September 29–30, 2016) National centre for rare diseases, Istituto Superiore di Sanità, Rome. Available from: http://www.iss.it/binary/cnmr4/cont/RD_Connect_BYOD_2016_preliminary_programme_rev12.07.2016.pdf. Accessed Dec 2016

  17. Roos M, Wilkinson MD, Kaliyaperumal R, Thompson M, Carta C et al (2016) Registries of domain-relevant semantic reference models help bootstrap interoperability in domains with fragmented data resources. Proceedings of the 9th International Semantic Web Applications and Tools for the Life Sciences (SWAT4LS) Conference. Available from: http://www.swat4ls.org/wp-content/uploads/2016/10/paper-16.pdf. Accessed Dec 2016

  18. Samadian S, McManus B, Wilkinson MD (2012) Extending and encoding existing biological terminologies and datasets for use in the reasoned semantic web. J Biomed Semant 3(1):6

    Article  Google Scholar 

  19. Smedley D, Jacobsen JOB, Jäger M, Köhler S, Holtgrewe M et al (2015) Next-generation diagnostics and disease-gene discovery with the exomiser. Nat Protoc 10:2004–2015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Smith B, Ashburner M, Rosse C, Bard J, Bug W et al (2007) The OBO foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol 25(11):1251–1255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Thompson R, Johnston L, Taruscio D, Monaco L, Béroud C et al (2014) RD-Connect: an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research. J Gen Intern Med 29(S3):S780–S787

    Article  PubMed  Google Scholar 

  22. Weibel S, Kunze J, Lagoze C, Wolf M (1998) Dublin core metadata for resource discovery. Available from: http://www.rfc-editor.org/info/rfc2413. Accessed: Dec 2016

  23. Whetzel PL, Noy NF, Shah NH, Alexander PR, Nyulas C et al (2011) BioPortal: enhanced functionality via new web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res 39(Web Server issue):W541–W545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M et al (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3(March):1600018

    Google Scholar 

  25. Williams AJ, Harland L, Groth P, Pettifer S, Chichester C et al (2012) Open PHACTS: semantic interoperability for drug discovery. Drug Discov Today 17(21–22):1188–1198

    Article  PubMed  Google Scholar 

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Roos, M., López Martin, E., Wilkinson, M.D. (2017). Preparing Data at the Source to Foster Interoperability across Rare Disease Resources. In: Posada de la Paz, M., Taruscio, D., Groft, S. (eds) Rare Diseases Epidemiology: Update and Overview. Advances in Experimental Medicine and Biology, vol 1031. Springer, Cham. https://doi.org/10.1007/978-3-319-67144-4_9

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