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AQUAPONY: visualization and interpretation of phylogeographic information on phylogenetic trees

Bastien Cazaux 1, 2 Guillaume Castel 3, 2, * Eric Rivals 2, 1, *
* Corresponding author
1 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Motivation The visualization and interpretation of evolutionary spatiotemporal scenarios is broadly and in- creasingly used in infectious disease research, ecology, or agronomy. Using probabilistic frameworks, well- known tools can infer from molecular data ancestral traits for internal nodes in a phylogeny, and numerous phylogenetic rendering tools can display such evolutionary trees. However, visualizing such ancestral informa- tion and its uncertainty on the tree remains tedious. For instance, ancestral nodes can be associated to several geographical annotations with close probabilities and thus, several migration or transmission scenarios exist. Results We expose a web-based tool, named AQUAPONY, that facilitates such operations. Given an evolu- tionary tree with ancestral (e.g., geographical) annotations, the user can easily control the display of ancestral information on the entire tree or a subtree, and can view alternative phylogeographic scenarios along a branch according to a chosen uncertainty threshold. AQUAPONY interactively visualizes the tree and eases the ob- jective interpretation of evolutionary scenarios. AQUAPONY’s implementation makes it highly responsive to user interaction, and instantaneously updates the tree visualizations even for large trees (which can be exported as image files). Availability and Implementation: AQUAPONY is coded in JavaScript/HTML, available under Cecill license, and can freely used at atgc-montpellier.fr/aquapony.
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Submitted on : Wednesday, July 10, 2019 - 7:08:53 PM
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Bastien Cazaux, Guillaume Castel, Eric Rivals. AQUAPONY: visualization and interpretation of phylogeographic information on phylogenetic trees. Bioinformatics, Oxford University Press (OUP), 2019, 35 (17), pp.3163-3165. ⟨10.1093/bioinformatics/btz011⟩. ⟨lirmm-01980994⟩



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