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spread.gl: visualising pathogen dispersal in a high-performance browser application.
Li, Yimin; Bollen, Nena; Hong, Samuel L; Brusselmans, Marius; Gámbaro, Fabiana; Suchard, Marc A; Rambaut, Andrew; Lemey, Philippe; Dellicour, Simon; Baele, Guy.
Afiliação
  • Li Y; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
  • Bollen N; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
  • Hong SL; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
  • Brusselmans M; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
  • Gámbaro F; Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium.
  • Suchard MA; Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
  • Rambaut A; Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
  • Lemey P; Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
  • Dellicour S; Institute for Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, Edinburgh, EH9 3FL, UK.
  • Baele G; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
medRxiv ; 2024 Jun 05.
Article em En | MEDLINE | ID: mdl-38883783
ABSTRACT
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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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article