Spencer Matthew, Bryant David, Susko Edward
Department of Mathematics and Statistics, Dalhousie University, Hali, Nova Scotia, B3H 3J5, Canada.
Syst Biol. 2007 Feb;56(1):25-43. doi: 10.1080/10635150601156313.
Genome phylogenies can be inferred from data on the presence and absence of genes across taxa. Logdet distances may be a good method, because they allow expected genome size to vary across the tree. Recently, Lake and Rivera proposed conditioned genome reconstruction (calculation of logdet distances using only those genes present in a conditioning genome) to deal with unobservable genes that are absent from every taxon of interest. We prove that their method can consistently estimate the topology for almost any choice of conditioning genome. Nevertheless, the choice of conditioning genome is important for small samples. For real bacterial genome data, different choices of conditioning genome can result in strong bootstrap support for different tree topologies. To overcome this problem, we developed supertree methods that combine information from all choices of conditioning genome. One of these methods, based on the BIONJ algorithm, performs well on simulated data and may have applications to other supertree problems. However, an analysis of 40 bacterial genomes using this method supports an incorrect clade of parasites. This is a common feature of model-based gene content methods and is due to parallel gene loss.
基因组系统发育可以从不同分类群中基因存在与否的数据推断出来。对数行列式距离可能是一种很好的方法，因为它们允许预期基因组大小在整个树中变化。最近，莱克和里维拉提出了条件基因组重建（仅使用条件基因组中存在的那些基因来计算对数行列式距离），以处理在所有感兴趣的分类群中都不存在的不可观察基因。我们证明，对于几乎任何条件基因组的选择，他们的方法都能一致地估计拓扑结构。然而，对于小样本，条件基因组的选择很重要。对于真实的细菌基因组数据，不同的条件基因组选择可能会导致对不同树拓扑结构的强大自引导支持。为了克服这个问题，我们开发了超树方法，该方法结合了来自所有条件基因组选择的信息。其中一种基于BIONJ算法的方法在模拟数据上表现良好，并且可能适用于其他超树问题。然而，使用这种方法对40个细菌基因组进行的分析支持了一个不正确的寄生虫分支。这是基于模型的基因含量方法的一个共同特征，并且是由于平行基因丢失所致。
