The story of visualizing 3D genome data as a path toward composable visualization tools

In biomedicine, emerging data acquisition methods often produce datasets that challenge traditional visualization approaches, driving the research in data visualization to produce new methods and infrastructure. The adoption of these innovations often hinges not just on technical merit, but on how well they integrate into the workflows of domain experts. While standalone prototypes can demonstrate what's theoretically possible, meaningful adoption requires aligning with real-world practices. In computational biology, for instance, much day-to-day analysis happens in environments like Jupyter notebooks—yet many specialized visualization tools remain inaccessible in that context. In this talk, I’ll explore this challenge through the lens of our work on visualizing 3D models of genome folding within the cell nucleus. Existing tools for working with 3D genome data are often grassroots efforts developed by biologists, and lack the generality, expressiveness, and usability found in modern data visualization systems. I’ll present our recent efforts to address this gap by extending the grammar-based visualization toolkit Gosling, enabling new capabilities for 3D genomic data exploration. I’ll close by outlining a vision for more composable, interoperable visualization systems—shaped by this project as well as broader work from the HIDIVE lab.

 

David Kouřil is a postdoctoral research fellow in Nils Gehlenborg’s HIDIVE lab at Harvard Medical School. Originally from the Czech Republic, David received his doctoral degree from TU Wien in Vienna, Austria, in 2021. His doctoral research focused on visualizing mesoscopic biological models with the aim of science communication and outreach to non-expert audiences. David’s broader research interests lie in designing novel visualization and interaction methods that support exploring and understanding of biomedical data, often with a spatial component. At HIDIVE, David’s work revolves around genomic data, such as predictions of chromatin folding in cell nucleus.