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

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-sour...

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Veröffentlicht in:	Bioinformatics (Oxford, England) England), 2024-11, Vol.40 (12)
Hauptverfasser:	Li, Yimin, Bollen, Nena, Hong, Samuel L, Brusselmans, Marius, Gambaro, Fabiana, Klaps, Joon, Suchard, Marc A, Rambaut, Andrew, Lemey, Philippe, Dellicour, Simon, Baele, Guy
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Schlagworte:	Bayes Theorem COVID-19 - virology Humans Original Paper Phylogeny Phylogeography SARS-CoV-2 - genetics Software Web Browser
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creator	Li, Yimin Bollen, Nena Hong, Samuel L Brusselmans, Marius Gambaro, Fabiana Klaps, Joon Suchard, Marc A Rambaut, Andrew Lemey, Philippe Dellicour, Simon Baele, Guy
description	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.
doi_str_mv	10.1093/bioinformatics/btae721
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subjects	Bayes Theorem COVID-19 - virology Humans Original Paper Phylogeny Phylogeography SARS-CoV-2 - genetics Software Web Browser
title	spread.gl: visualizing pathogen dispersal in a high-performance browser application
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