Turner Katherine M E, Hanage William P, Fraser Christophe, Connor Thomas R, Spratt Brian G
Department of Infectious Disease Epidemiology, Imperial College, St. Mary's Hospital Campus, Norfolk Place, London, UK.
BMC Microbiol. 2007 Apr 12;7:30. doi: 10.1186/1471-2180-7-30.
The program eBURST uses multilocus sequence typing data to divide bacterial populations into groups of closely related strains (clonal complexes), predicts the founding genotype of each group, and displays the patterns of recent evolutionary descent of all other strains in the group from the founder. The reliability of eBURST was evaluated using populations simulated with different levels of recombination in which the ancestry of all strains was known.
For strictly clonal simulations, where all allelic change is due to point mutation, the groups of related strains identified by eBURST were very similar to those expected from the true ancestry and most of the true ancestor-descendant relationships (90-98%) were identified by eBURST. Populations simulated with low or moderate levels of recombination showed similarly high performance but the reliability of eBURST declined with increasing recombination to mutation ratio. Populations simulated under a high recombination to mutation ratio were dominated by a single large straggly eBURST group, which resulted from the incorrect linking of unrelated groups of strains into the same eBURST group. The reliability of the ancestor-descendant links in eBURST diagrams was related to the proportion of strains in the largest eBURST group, which provides a useful guide to when eBURST is likely to be unreliable.
Examination of eBURST groups within populations of a range of bacterial species showed that most were within the range in which eBURST is reliable, and only a small number (e.g. Burkholderia pseudomallei and Enterococcus faecium) appeared to have such high rates of recombination that eBURST is likely to be unreliable. The study also demonstrates how three simple tests in eBURST v3 can be used to detect unreliable eBURST performance and recognise populations in which there appears to be a high rate of recombination relative to mutation.
程序eBURST利用多位点序列分型数据将细菌群体划分为密切相关菌株组（克隆复合体），预测每组的奠基基因型，并展示该组中所有其他菌株从奠基菌株开始的近期进化谱系模式。使用具有不同重组水平且所有菌株谱系已知的模拟群体对eBURST的可靠性进行了评估。
对于严格的克隆模拟，即所有等位基因变化均由点突变引起，eBURST识别出的相关菌株组与真实谱系预期的组非常相似，并且eBURST识别出了大多数真实的祖先 - 后代关系（90 - 98%）。低或中等重组水平的模拟群体表现出同样高的性能，但随着重组与突变比率的增加，eBURST的可靠性下降。在高重组与突变比率下模拟的群体由一个单一的大型松散eBURST组主导，这是由于不相关的菌株组被错误地链接到同一个eBURST组中导致的。eBURST图中祖先 - 后代链接的可靠性与最大eBURST组中的菌株比例相关，这为判断eBURST何时可能不可靠提供了有用的指导。
对一系列细菌物种群体中的eBURST组进行检查表明，大多数处于eBURST可靠的范围内，只有少数（如类鼻疽伯克霍尔德菌和粪肠球菌）似乎具有如此高的重组率，以至于eBURST可能不可靠。该研究还展示了如何使用eBURST v3中的三个简单测试来检测不可靠的eBURST性能，并识别相对于突变似乎具有高重组率的群体。
