eBURST: Inferring Patterns of Evolutionary Descent among Clusters of Related Bacterial Genotypes from Multilocus Sequence Typing Data

• DOI: 10.1128/jb.186.5.1518-1530.2004
• Publication Date: 2004-02-18T22:33:26Z
• Authors: Edward J. Feil; Bao C. Li; David M. Aanensen; William P. Hanage; Brian G. Spratt
• Keywords: Multilocus sequence typing; Microevolution

ABSTRACT The introduction of multilocus sequence typing (MLST) for the precise characterization isolates bacterial pathogens has had a marked impact on both routine epidemiological surveillance and microbial population biology. In fields, key prerequisite exploiting this resource is ability to discern relatedness patterns evolutionary descent among with similar genotypes. Traditional clustering techniques, such as dendrograms, provide very poor representation recent events, they attempt reconstruct relationships in absence realistic model way which clones emerge diversify form clonal complexes. An increasingly popular approach, called BURST, been used an alternative, but present implementations are unable cope large data sets offer crude graphical outputs. Here we new implementation algorithm, eBURST, divides MLST set any size into groups related complexes, predicts founding (ancestral) genotype each complex, computes bootstrap support assignment. most parsimonious all complex from predicted founder(s) then displayed. advantages eBURST exploring demonstrated number examples, including simple Spain 23F -1 Streptococcus pneumoniae , “population snapshots” entire S. Staphylococcus aureus databases, more complicated complexes observed Campylobacter jejuni Neisseria meningitidis .