spread.gl:在高性能浏览器应用程序中实现病原体扩散的可视化

Yimin Li, N. Bollen, S. Hong, Marius Brusselmans, Fabiana G´ambaro, M. Suchard, A. Rambaut, P. Lemey, S. Dellicour, G. Baele
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引用次数: 0

摘要

系统地理学分析能够利用与取样分子序列相关的位置数据来重建病原体的时空扩散史。可视化软件通常用于帮助解释附带的估算结果,因为这些结果并不总是很容易解释。Spread.gl 是一款功能强大、开源且功能丰富的浏览器应用程序,可对离散和连续的系统地理学推断结果进行流畅、直观和用户友好的可视化处理,从而实现病原体随时间推移的地理扩散动画、世界地图)、包含从输入系统发育中提取的信息的多个图层以及代表环境数据的不同类型图层。因此,用户可以探索哪些环境数据可能影响了病原体的传播模式,随后可以通过更加原则性的统计分析对这些数据进行正式测试。我们在几个有代表性的病原体扩散实例中展示了 spread.gl 的可视化功能,包括大规模 SARS-CoV-2 分析产生的包含 17,000 多个基因组序列的系统发生的平滑动画。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
spread.gl: visualising pathogen dispersal in a high-performance browser application
Phylogeographic analyses are able to exploit the location data associated with sampled molecular sequences to reconstruct the spatio-temporal dispersal history of a pathogen. Visualisation software is commonly used to facilitate the interpretation of the accompanying estimation results, as these are not always easily interpretable. spread.gl is a powerful, open-source and feature-rich browser application that enables smooth, intuitive and user-friendly visualisation of both discrete and continuous phylogeographic inference results, enabling the animation of pathogen geographic dispersal through time. spread.gl can render and combine the visualisation of several data layers, including a geographic layer (e.g., a world map), multiple layers that contain information extracted from the input phylogeny, and different types of layers that represent environmental data. As such, users can explore which environmental data may have shaped pathogen dispersal patterns, that can subsequently be formally tested through more principled statistical analyses. We showcase the visualisation features of spread.gl on several representative pathogen dispersal examples, including the smooth animation of a phylogeny encompassing over 17,000 genomic sequences resulting from a large-scale SARS-CoV-2 analysis.
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