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

Johannes Waschke, Mario Hlawitschka, Kerim Anlas, Vikas Trivedi, Ingo Roeder, Jan Huisken, Nico Scherf
Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103 Leipzig, Germany
BioRxiv, 2020.04.17.043323, 2020

@article{waschke2020linus,

   title={linus: Conveniently explore, share and present large-scale biological trajectory data from a web browser},

   author={Waschke, Johannes and Hlawitschka, Mario and Anlas, Kerim and Trivedi, Vikas and Roeder, Ingo and Huisken, Jan and Scherf, Nico},

   journal={BioRxiv},

   year={2020},

   publisher={Cold Spring Harbor Laboratory}

}

In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data is often a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise package 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 and enriches them with additional features, such as edge bundling or custom axes and generates an interactive web-based visualisation that can be shared offline and online. The goal of linus is to facilitate the collaborative discovery of patterns in complex trajectory data.
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