Building a biological database with Python
This talk contains brief mentions of cancer research.
Part of the DjangoCon AU specialist track
This talk is a story of how open-source frameworks make it possible for academics to take on otherwise infeasibly complex projects, which in my case was a new community-driven database for biologists and clinical geneticists.
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Almost ten years ago, a couple of professors got together to discuss the rapid growth of large datasets in their emerging field and concluded that a new database was needed to organize them. They approached me and, emboldened by some exciting talks at PyCon AU, I decided to take on this challenge using the Django web framework.
Despite being an academic team with minimal web development expertise, we created a database that has become an important resource for biological researchers and clinical geneticists all over the world, helping them understand how differences in our DNA can affect disease.
This talk will include an introduction to how and why biologists are generating these large datasets. Then I’ll describe the evolution of the project starting with the initial data engineering and database design, and the development of Django-based prototypes and production deployments. Next I’ll discuss how the project survived contact with external users and their data submissions, and why we transitioned the project away from Django to FastAPI.
Finally, I’ll share my experience maintaining (and trying to find funding for) a public-facing open-source project within academia, as well as make some suggestions for how members of the Python community can get involved with ongoing research projects.
Alan is a computational biologist at WEHI and the University of Melbourne. His work focuses on making large-scale genomic data accessible and interpretable by clinical geneticists and other researchers.