Abstract
MotivationClinicians, health officials and researchers are interested in the epidemic spread of pathogens in both space and time to support the optimization of intervention measures and public health policies. Large sequence databases of virus sequences provide an interesting opportunity to study this spread through phylogenetic analysis. To infer knowledge from large phylogenetic trees, potentially encompassing tens of thousands of virus strains, an efficient method for data exploration is required. The clades that are visited during this exploration should be annotated with strain characteristics (e.g. transmission risk group, tropism, drug resistance profile) and their geographic context.ResultsPhyloGeoTool implements a visual method to explore large phylogenetic trees and to depict characteristics of strains and clades, including their geographic context, in an interactive way. PhyloGeoTool also provides the possibility to position new virus strains relative to the existing phylogenetic tree, allowing users to gain insight in the placement of such new strains without the need to perform a de novo reconstruction of the phylogeny.Availability and implementation https://github.com/rega-cev/phylogeotool (Freely available: open source software project).Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Expanding and intensifying sequencing efforts for the management of infectious diseases along with the generation of large-scale databases of clinical and demographical information provide unprecedented opportunities for the surveillance of epidemics and outbreaks of viral pathogens
Fast and accurate placement of novel virus sequences onto an existing phylogenetic tree can provide valuable insights for outbreak detection, by relating evolutionary dynamics to epidemiological and clinical characteristics
We present PhyloGeoTool, an application to interactively navigate large phylogenies and to explore associated clinical and epidemiological data
Summary
Expanding and intensifying sequencing efforts for the management of infectious diseases along with the generation of large-scale databases of clinical and demographical information provide unprecedented opportunities for the surveillance of epidemics and outbreaks of viral pathogens. Mapping the origin and dynamics of epidemics in space and time is becoming feasible as geo-tagged and timestamped sequence data are part of routine clinical care. Computational and methodological advances allow to infer phylogenies of tens of thousands of sequences (Liu et al, 2011) and applications have been developed to visualize such large phylogenetic trees (de Vienne, 2016; Huson and Scornavacca, 2012). Fast and accurate placement of novel virus sequences onto an existing phylogenetic tree can provide valuable insights for outbreak detection, by relating evolutionary dynamics to epidemiological and clinical characteristics
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