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BURST was devised and developed by Ed Feil (University of Bath;, while he was in the laboratory of Brian Spratt (now at Imperial College London;, as a way of displaying the relationships between closely-related isolates of a bacterial species or population*. BURST, unlike cluster diagrams, trees or dendrograms, uses a simple but appropriate model of bacterial evolution in which an ancestral (or founding) genotype increases in frequency in the population, and while doing so, begins to diversify to produce a cluster of closely-related genotypes that are all descended from the founding genotype. This cluster of related genotypes is often referred to as a “clonal complex”.

The BURST algorithm first identifies mutually exclusive groups of related genotypes in the population (typically a multilocus sequence typing [MLST] database), and attempts to identify the founding genotype of each group (see here). The algorithm then predicts the descent from the predicted founding genotype to the other genotypes in the group, displaying the output as a radial diagram, centred on the predicted founding genotype. The procedure was developed for use with the data produced by MLST (sequence types or STs, and their allelic profiles), but using a suitable criterion for the definition of groups of related genotypes, it could be used with some other types of data, particularly data from multilocus enzyme electrophoresis (MLEE) or from molecular typing methods that produce strings of integers by using multiple repeat length polymorphisms.

The approach used in BURST greatly simplifies the problem of depicting the evolutionary relationships among closely related genotypes, which are poorly represented on a tree, as the focus for each genotype is its relationship to, and distance from, its predicted founding genotype, and the great majority of the pair-wise relationships between genotypes in the population are ignored. BURST does not make any inferences about the relationships between the more distantly related genotypes that belong to different groups. In many bacterial populations, the relationships between distantly-related genotypes may be difficult to discern, as the extent of homologous recombination may be sufficiently high that relationships may be poorly represented by a phylogenetic tree, and should be represented by a network.

eBURST does not tell you the truth – it simply produces an hypothesis about the way each clonal complex may have emerged and diversified - and any additional phenotypic, genotypic, or epidemiological data that are available should be used to explore the plausibility of the proposed ancestry and patterns of descent.

The original version of BURST was implemented by Ed Feil and Man-Suen Chan ( A greatly enhanced version of BURST (eBURST v1) was developed as a Java™ applet by Bao Li (, and was integrated into the MLST websites by David Aanensen (, within the laboratory of Brian Spratt, with help and advice from Ed Feil, Jon Evans (, Bill Hanage ( and Christophe Fraser ( The original version of BURST is still available at and, if necessary, may be used in conjunction with eBURST, as the two algorithms differ slightly in the way they display the relationships between STs.

eBURSTv3 has been further developed with funding from the Wellcome Trust by Derek Huntley and David Aanensen at Imperial College London and contains several new features improving on previous versions:

  • JAVA Web Start Implementation – allowing the most up-to-date version of eBURSTv3 to be installed locally and data streamed over the internet directly to the application.
  • Comparative eBURST - The ability to compare two datasets and differentially colour those STs unique to one or other of the datasets, or present in both datasets.

  • Other enhancements
  • Docking of eBURSTv2 floating windows and menu based access to functionality.
  • Search and highlight STs within a population.
  • Printing directly from eBURST.
  • Output formats – images can be saved in a number of bitmap formats and also as Scalable Vector Graphics (SVG) allowing further editing of eBURST diagrams in third party applications.

* Feil, E.J., Li, B., Aanensen, D.M., Hanage, W.P. and Spratt, B.G. 2004. eBURST: Inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data. J. Bact. 186: 1518-1530.

Spratt BG, Hanage WP, Li B, Aanensen DM and Feil EJ. (2004) Displaying the relatedness among isolates of bacterial species -- the eBURST approach. FEMS Microbiol Lett. Dec 15;241(2):129-34

eBURSTv3 has been developed and is hosted at Imperial College London