spread.gl：在高性能浏览器应用程序中可视化病原体传播

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

作者信息

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

机构信息

Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven 3000, Belgium.

Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels 1050, Belgium.

出版信息

Bioinformatics. 2024 Nov 28;40(12). doi: 10.1093/bioinformatics/btae721.

DOI:10.1093/bioinformatics/btae721

PMID:39626311

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11652268/

Abstract

MOTIVATION

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.

RESULTS

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.

AVAILABILITY AND IMPLEMENTATION

Source code, installation instructions, example input data, and outputs of spread.gl are accessible at https://github.com/GuyBaele/SpreadGL.

摘要

动机

贝叶斯系统发育地理学分析在重建病原体的时空传播历史中起着关键作用。然而，解释系统发育地理学重建的复杂结果需要复杂的可视化工具。

结果

为应对这一挑战，我们开发了spread.gl，这是一个开源的、功能丰富的浏览器应用程序，为离散和连续的系统发育地理学推断提供了一个平滑且直观的可视化工具，包括病原体地理扩散随时间的动画展示。spread.gl可以渲染并组合多个图层的可视化内容，这些图层包含从输入系统发育树中提取的信息以及各种环境数据图层，使研究人员能够在进行正式测试之前探索哪些环境因素可能影响了病原体的传播模式。我们用代表性示例展示了spread.gl的可视化功能，包括基于超过17000个SARS-CoV-2基因组序列的系统发育地理学重建的平滑动画。