Improved phylogenetic tools for gene content data

从分子数据构建进化树的传统方法是获得组成单个基因的核苷酸或氨基酸序列，并建立一棵树来解释这些序列在物种之间的差异。自90年代中期以来，大量原核生物(细菌和古生菌：无核的单细胞生物)的基因组被测序。这些数据表明，传统方法是不可靠的，原因有两个。首先，单个基因的序列可能非常短，因此统计可变性可能会限制从单个基因派生的树的准确性。其次，不同的基因可能真的有不同的进化史，因为基因可以在原核生物之间转移，即使它们没有密切的亲缘关系。还有一种替代方法存在。基因可以迅速地从基因组中获得和丢失，很明显，密切相关的生物往往具有相似的存在和不存在基因的模式。这表明，我们可以利用基因的存在和缺失的数据来构建一棵反映基因组整体历史的进化树。有许多现有的方法试图做到这一点。然而，大多数人要么缺乏可靠的理论理由，而且已知在某些情况下给出了错误的答案，要么没有考虑到随着时间的推移进化过程中的可变性。一种名为条件基因组重建的新方法有充分的理论依据，即使在基因增减率随时间变化的情况下也表现良好。我们将开发新的统计方法，扩大这种方法可以应用到的基因组范围。然而，与大多数其他基因含量方法一样，条件基因组重建对于寄生细菌来说并不是很好。这可能是构建基因存在/缺失数据库的方式的人工制品。我们将通过使用不同方法跨数据库进行比较来探索这一点。另一种解释是，这个问题是基因损失率增加的结果，而这些基因对生活在其他细胞内的寄生生物来说是不必要的。对于这些寄生虫来说，许多对外部环境中的生命至关重要的基因可能会突然变得不重要，可能会丢失。这种缺失可能独立地发生在无关的寄生虫谱系中，导致这些谱系具有相似的基因内容。我们将通过分别分析必要的和可有可无的基因来确定是否存在这种情况。最后，我们将通过模拟大量现实的基因组数据，并比较新方法和现有方法在这些数据上的性能，来确定我们的新方法是否真的更好。这项工作将增加我们对整个基因组进化的理解，并将为构建进化树提供新的工具。

The traditional approach to constructing an evolutionary tree from molecular data is to obtain the sequence of nucleotides or amino acids that makes up a single gene, and build a tree that explains the differences in these sequences among species. Since the mid-90s, a large number of prokaryotic organisms (bacteria and archaea: single-celled organisms without a nucleus) have had their genomes sequenced. These data have suggested that the traditional approach is unreliable for two reasons. First, the sequence of a single gene may be quite short, so that statistical variability may limit the accuracy of a tree derived from a single gene. Second, different genes may genuinely have different evolutionary histories, because genes can be transferred between prokaryotic organisms, even when they are not closely related. An alternative approach exists. Genes can be rapidly gained and lost from the genome, and it is apparent that closely-related organisms tend to have similar patterns of presence and absence of genes. This suggests that we could use data on the presence and absence of genes to construct an evolutionary tree that reflects the overall history of the genome. There are many existing methods that attempt to do this. However, most either lack a sound theoretical justification and are known to give the wrong answer in some cases, or do not account for variability in the evolutionary process over time. A new method known as conditioned genome reconstruction has a sound theoretical justification and performs well even when the rates of gene gain and loss vary over time. We will develop new statistical methods that widen the range of genomes to which this method can be applied. However, as with most other gene content methods, conditioned genome reconstruction does not perform well for parasitic bacteria. This may be an artefact of the way that databases of gene presence/absence are constructed. We will explore this by comparisons across databases using different methods. Another explanation is that the problem is a consequence of increased loss rates of genes that are unnecessary for parasitic organisms that live inside other cells. For these parasites, many genes that are essential for life in the external environment may suddenly become unimportant and may be lost. This loss could occur independently in unrelated lineages of parasites, resulting in these lineages having similar gene content. We will determine whether this is the case by separately analyzing essential and dispensable sets of genes. Finally, we will determine whether our new methods are really better by simulating large and realistic sets of genome data, and comparing the performance of new and existing methods on these data. This work will increase our understanding of the evolution of whole genomes, and will provide new tools for the construction of evolutionary trees.