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Published September 17, 2018 | Accepted Version
Journal Article Open

Model organism data evolving in support of translational medicine

Abstract

Model organism databases (MODs) have been collecting and integrating biomedical research data for 30 years and were designed to meet specific needs of each model organism research community. The contributions of model organism research to understanding biological systems would be hard to overstate. Modern molecular biology methods and cost reductions in nucleotide sequencing have opened avenues for direct application of model organism research to elucidating mechanisms of human diseases. Thus, the mandate for model organism research and databases has now grown to include facilitating use of these data in translational applications. Challenges in meeting this opportunity include the distribution of research data across many databases and websites, a lack of data format standards for some data types, and sustainability of scale and cost for genomic database resources like MODs. The issues of widely distributed data and application of data standards are some of the challenges addressed by FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The Alliance of Genome Resources is now moving to address these challenges by bringing together expertly curated research data from fly, mouse, rat, worm, yeast, zebrafish, and the Gene Ontology consortium. Centralized multi-species data access, integration, and format standardization will lower the data utilization barrier in comparative genomics and translational applications and will provide a framework in which sustainable scale and cost can be addressed. This article presents a brief historical perspective on how the Alliance model organisms are complementary and how they have already contributed to understanding the etiology of human diseases. In addition, we discuss four challenges for using data from MODs in translational applications and how the Alliance is working to address them, in part by applying FAIR data principles. Ultimately, combined data from these animal models are more powerful than the sum of the parts.

Additional Information

© 2018 Springer Nature Limited. Received 14 March 2018; Accepted 13 August 2018; Published 17 September 2018. The authors would like to thank all the current and past members of FlyBase, WormBase, ZFIN, MGI, SGD, RGD, and the Gene Ontology Consortium for their dedication over the past 30 years. The resulting resources are truly amazing and have a real impact on human health. The following funding sources are acknowledged for each. ZFIN: National Human Genome Research Institute at the US National Institutes of Health (U41 HG002659). MGD: National Human Genome Research Institute at the US National Institutes of Health (U41 HG000330, R25 HG007053). WB: National Human Genome Research Institute at the US National Institutes of Health (U41 HG002223), the UK Medical Research Council and the UK Biotechnology and Biological Sciences Research Council. FB: National Human Genome Research Institute at the US National Institutes of Health (U41 HG000739); British Medical Research Council. SGD: National Human Genome Research Institute at the US National Institutes of Health (U41 HG001315). RGD: National Heart, Lung, and Blood Institute at the US National Institutes of Health (RO1 HL64541). GO: National Human Genome Research Institute at the US National Institutes of Health (U41 HG002273). Alliance of Genome Resources: National Human Genome Research Institute at the US National Institutes of Health (U41 H002223).

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August 22, 2023
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