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FAIR principles and the IEDB: short-term improvements and a long-term vision of OBO-foundry mediated machine-actionable interoperability

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      Abstract

      The Immune Epitope Database (IEDB), at www.iedb.org, has the mission to make published experimental data relating to the recognition of immune epitopes easily available to the scientific public. By presenting curated data in a searchable database, we have liberated it from the tables and figures of journal articles, making it more accessible and usable by immunologists. Recently, the principles of Findability, Accessibility, Interoperability and Reusability have been formulated as goals that data repositories should meet to enhance the usefulness of their data holdings. We here examine how the IEDB complies with these principles and identify broad areas of success, but also areas for improvement. We describe short-term improvements to the IEDB that are being implemented now, as well as a long-term vision of true ‘machine-actionable interoperability’, which we believe will require community agreement on standardization of knowledge representation that can be built on top of the shared use of ontologies.

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      Most cited references 22

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      The FAIR Guiding Principles for scientific data management and stewardship

      There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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        The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration.

        The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.
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          The Sequence Read Archive

          The combination of significantly lower cost and increased speed of sequencing has resulted in an explosive growth of data submitted into the primary next-generation sequence data archive, the Sequence Read Archive (SRA). The preservation of experimental data is an important part of the scientific record, and increasing numbers of journals and funding agencies require that next-generation sequence data are deposited into the SRA. The SRA was established as a public repository for the next-generation sequence data and is operated by the International Nucleotide Sequence Database Collaboration (INSDC). INSDC partners include the National Center for Biotechnology Information (NCBI), the European Bioinformatics Institute (EBI) and the DNA Data Bank of Japan (DDBJ). The SRA is accessible at http://www.ncbi.nlm.nih.gov/Traces/sra from NCBI, at http://www.ebi.ac.uk/ena from EBI and at http://trace.ddbj.nig.ac.jp from DDBJ. In this article, we present the content and structure of the SRA, detail our support for sequencing platforms and provide recommended data submission levels and formats. We also briefly outline our response to the challenge of data growth.
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            Author and article information

            Affiliations
            [1 ]La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery and Center for Emerging Diseases and Biodefense, 9420 Athena Circle, La Jolla, CA 92037, USA
            [2 ]Lawrence Berkeley National Laboratory, Division of Environmental Genomics and Systems Biology, 1 Cyclotron Rd Berkeley, CA 94720, USA
            Author notes
            Corresponding author: Tel: 858-752-6902; Fax: 858-752-6987; Email: bpeters@ 123456lji.org

            Citation details: Vita,R., Overton,J.A., Mungall,C.J. et al. FAIR principles and the IEDB: short-term improvements and a long-term vision of OBO-foundry mediated machine-actionable interoperability. Database (2017) Vol. 2017: article ID bax105; doi:10.1093/database/bax105

            Journal
            Database (Oxford)
            Database (Oxford)
            databa
            Database: The Journal of Biological Databases and Curation
            Oxford University Press
            1758-0463
            2018
            19 February 2018
            19 February 2018
            : 2018
            5819722 10.1093/database/bax105 bax105
            © The Author(s) 2018. Published by Oxford University Press.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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            Pages: 9
            Product
            Funding
            Funded by: National Institutes of Health 10.13039/100000002
            Award ID: HHSN272201200010C
            Award ID: 1R24HG010032–01
            Categories
            Original Article

            Bioinformatics & Computational biology

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