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eBURST: Inferring Patterns of Evolutionary Descent among Clusters of Related Bacterial Genotypes from Multilocus Sequence Typing Data. Edward J. Feil, 2004.The introduction of multilocus sequence typing [MLST] for theprecise characterization of isolates of bacterial pathogenshas had a marked impact on both routine epidemiological surveillanceand microbial population biology . In both fields, a key prerequisitefor exploiting this resource is the ability to discern the relatednessand patterns of evolutionary descent among isolates with similargenotypes . Traditional clustering techniques, such as dendrograms,provide a very poor representation of recent evolutionary events,as they attempt to reconstruct relationships in the absenceof a realistic model of the way in which bacterial clones emergeand diversify to form clonal complexes . An increasingly popularapproach, called BURST, has been used as an alternative, butpresent implementations are unable to cope with very large datasets and offer crude graphical outputs . Here we present a newimplementation of this algorithm, eBURST, which divides an MLSTdata set of any size into groups of related isolates and clonalcomplexes, predicts the founding [ancestral] genotype of eachclonal complex, and computes the bootstrap support for the assignment.The most parsimonious patterns of descent of all isolates ineach clonal complex from the predicted founder[s] are then displayed.The advantages of eBURST for exploring patterns of evolutionarydescent are demonstrated with a number of examples, includingthe simple Spain23F-1 clonal complex of Streptococcus pneumoniae,"population snapshots" of the entire S . pneumoniae and Staphylococcusaureus MLST databases, and the more complicated clonal complexesobserved for Campylobacter jejuni and Neisseria meningitidis. Emergence of Streptococcus pneumoniae with Very-High-Level Resistance to Penicillin. Stephanie J. Schrag, 2004. Isolation and Characterization of Micromonospora Phage Xiaohua Li, 2004.
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