Improved phylogenetic tools for gene content data

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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.