Welcome to PHYLOViZ 1.0


New code repository for PHYLOViZ: http://code.phyloviz.net/. All the PHYLOViZ code is now available for the developer community.


Francisco, A.P., C. Vaz, P. T. Monteiro, J. Melo-Cristino, M. Ramirez, and J. A. CarriƧo. 2012.
PHYLOViZ: Phylogenetic inference and data visualization for sequence based typing methods.
BMC Bioinformatics, 2012 May 8;13(1):87.


With the decreasing DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. Multilocus Sequence Typing (MLST), Single Nucleotide Polymorphism (SNP) analysis and Multilocus Variable Number of Tandem Repeats Analysis (MLVA), provide the reproducibility and comparability of results needed for a global scale bacterial population analysis as well as to be used in local epidemiological studies.

All these methods rely heavily on online databases that collect the generated allelic profiles and associated epidemiological data. Since this wealth of data remains underused and poorly annotated because no suitable querying and visualization software exists to mine it, we developed the Phyloviz software.

Phyloviz Features


Phyloviz is platform independent JAVA software that allows the analysis of sequence-based typing methods that generate allelic profiles and their associated epidemiological data.

For representing the possible evolutionary relationships between strains identified by allelic profiles it uses the goeBURST algorithm, a refinement of eBURST algorithm proposed by Feil et al., and its expansion to generate a complete minimum spanning tree (MST).

The software uses Prefuse and Freehep libraries for data visualization. The software is being developed in a modular way to allow its expansion with novel data analysis algorithms and new visualization modules.


  • Open source software
  • Modularity allows the creation of plugins to analyse different types of data
  • Allows the visualization of data overlaid onto goeBURST and MST results
  • Confidence assessment of each link in the graph
  • Query the data and see the query results directly onto the graphs
  • Search your data set using regular expressions to select what to display
  • Export the results as images in various formats: eps, png, gif, pdf, etc