linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser

Author summary Many of the processes that we study in biology are dynamic or interconnected. We can represent most of them as trajectories, being it connections between neurons in a brain or species in an ecosystem or motion traces of animals, cells or molecules. Modern experiments allow researchers to generate such trajectory data at unprecedented scales: think the parallel tracking of thousands of cells in a developing embryo over hours or days. However, visualising large-scale trajectory data is a challenge: the typical static visualisations result in excessive overplotting and often resemble the infamous hairballs. Simplification and interactivity are crucial strategies to deal with this problem. We present the lightweight tool linus that enables researchers to explore and share their trajectory data in an engaging way in web browsers from almost any device. In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data.

Automated summary

Trajectory data in biology is dynamic and interconnected, making visualization a challenge that requires simplification and interactivity; the tool linus provides researchers with a solution to explore and share trajectory data, facilitating collaborative discovery of data patterns.