spread.gl: visualizing pathogen dispersal in a high-performance browser application

Yimin Li*, Nena Bollen, Samuel L Hong, Marius Brusselmans, Fabiana Gambaro, Joon Klaps, Marc A Suchard, Andrew Rambaut, Philippe Lemey, Simon Dellicour, Guy Baele

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Bayesian phylogeographic analyses are pivotal in reconstructing the spatio-temporal dispersal histories of pathogens. However, interpreting the complex outcomes of phylogeographic reconstructions requires sophisticated visualization tools.To meet this challenge, we developed spread.gl, an open-source, feature-rich browser application offering a smooth and intuitive visualization tool for both discrete and continuous phylogeographic inferences, including the animation of pathogen geographic dispersal through time. Spread.gl can render and combine the visualization of multiple layers that contain information extracted from the input phylogeny and diverse environmental data layers, enabling researchers to explore which environmental factors may have impacted pathogen dispersal patterns before conducting formal testing. We showcase the visualization features of spread.gl with representative examples, including the smooth animation of a phylogeographic reconstruction based on >17 000 SARS-CoV-2 genomic sequences.Source code, installation instructions, example input data, and outputs of spread.gl are accessible at https://github.com/GuyBaele/SpreadGL.
Original languageEnglish
Article numberbtae721
Number of pages7
JournalBioinformatics
Volume40
Issue number12
Early online date29 Nov 2024
DOIs
Publication statusPublished - Dec 2024

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