Mice and humans share a similar set of genes and, as a result, their biology has played a critical role in medical advancements for conditions such as obesity, diabetes, immune defects, blood cancers such as leukaemia and many more. If we are to develop a deeper understanding of the genomic determinants of human health and disease, more efficient links between human and experimental datasets are required.
The Biomedical Data Asset links two of Australia’s most important biomedical datasets – ASPREE and Missense Mutation Library – in a highly curated, user-friendly and accessible environment for genomic medicine.
Use cases for the data asset include:
- pursuing the most promising variants that have supportive evidence from both datasets, as potential drivers of disease (pathogenic variants) or novel drug targets (protective variants)
- improve clinical cancer diagnosis in the young, and improve overall understanding of genetic drivers of cancer
- improve the ability to interpret potentially pathogenic variation in human autoimmune disease.
The project involves four elements.
Data implementation plan – A data implementation plan will be developed and socialised with identified beneficiaries and end user groups to ensure the plan meets the needs of the stakeholder community.
Data wrangling – An enhanced, highly curated subset of both the ASPREE and Missense datasets enabling clear linkages and interoperability across the data. Curation and processing/analytical workflows will be made available through open source code repositories such as GitHub ensuring the data asset meets the Interoperability and Reusability principles of FAIR.
Data harmonisation and creation of gene/variant mapping – Develop an enhanced data mapping function that allows end users to specify a gene variant and pull down the relevant data from both the ASPREE and Missense collections into one data resource ready for use in analytical workflows. Harmonisation workflows will be made available through open source code repositories such as GitHub ensuring the data asset meets the Interoperability and Reusability principles of FAIR.
Data asset interface – The final enhanced data asset will be available to the research community through an easy-to-use data portal ensuring the data asset meets the Findable and Accessible principles of FAIR.
Who Will Benefit
Researchers, research organisations, infrastructure providers, government agencies (state and commonwealth), data analysts and managers, clinicians and health practitioners will benefit from the project’s core features:
- transdisciplinary research – the new data asset will enable researchers from many disciplines to answer more research questions
- igniting ideas and discussions – this data will improve collaboration on modelling
- improving our ability to diagnose and treat disease – improving diagnosis/treatment in clinical human studies where state-of-the-art genomic technologies have been used to identify genetic variation related to health and disease; where DNA changes identified require integration with animal models, to enable further interrogation and functional understanding of DNA changes, to advance the delivery of precision genomic medicine in Australia
- informing the model for future questions – observations from a large database of mouse DNA variation and phenotypes requires tracing back to analogous human DNA variation, to further interrogate and interpret observations regarding translatability from model animal systems into humans, accelerating scientific connectivity.
Our partners are:
- Phenomics Australia
- Bioplatforms Australia
- School of Public Health and Preventative Medicine / ASPREE, Monash University
- John Curtin School of Medical Research, Australian National University.
This project will result in the linkage of two of Australia’s most important biomedical datasets, in a highly curated, user-friendly and accessible environment. The new data asset will improve researchers’ ability to discover and experimentally validate gene function to improve disease diagnosis and treatment, especially in clinical human studies where state-of -the-art genomic technologies have been used to identify genetic variation related to health and disease.
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