Multigene datasets for deep phylogeny

Though molecular phylogenetics has been very successful in reconstructing the evolutionary history of species, some phylogenies, particularly those involving ancient events, have proven difficult to resolve. One approach to improving the resolution of deep phylogenies is to increase the amount of data by including multiple genes assembled from public sequence databases. Using modern phylogenetic methods and abundant computing power, the vast amount of sequence data available in public databases can be brought to bear on difficult phylogenetic problems. In this thesis I outline the motivation for assembling large multigene datasets and lay out the obstacles associated with doing so. I discuss the various methods by which these obstacles can be overcome and describe a bioinformatics solution, TaxMan, that can be used to rapidly assemble very large datasets of aligned genes in a largely automated fashion. I also explain the design and features of TaxMan from a biological standpoint and present the results of benchmarking studies. I illustrate the use of TaxMan to assemble large multigene datasets for two groups of taxa – the subphylum Chelicerata and the superphylum Lophotrochozoa. Chelicerata is a diverse group of arthropods with an uncertain phylogeny. When a set of mitochondrial genes is used to analyse the relationships between the chelicerate orders, the conclusions are highly dependent upon the evolutionary model used and are affected by the presence of systematic compsitional bias in mitochondrial genomes. Lophotrochozoa is a recently-proposed group of protostome phyla. A number of distinct phylogenetic hypotheses concerning the relationships between lophotrochozoan phyla have been proposed. I compare the phylogenetic conclusions given by analysis of nuclear and mitochondrial protein-coding and rRNA genes to evaluate support for some of these hypotheses. The multigene approach to phylogenetics holds great promise for addressing previously intractable problems. While the availability of both computing power and sequence data looks bound to increase, the applicability of the multigene strategy will be limited by the sophistication of bioinformatics tools. Acknowledgements There are many people I'd like to thank for a huge variety of reasons. Firstly, my parents have given me unceasing support in everything I've done. None of my achievements would have happened without them. My colleagues at Edinburgh, both past and present, have been wonderful and a pleasure to work with. In no particular order: Ann, Alasdair, Jenna, Seanna, Ralf, Claire, Habib, Fran, and Katelyn all deserve my thanks. Special thanks go to James for proof-reading duties. Mark, my supervisor, have given me all his support and a great environment in which to work. My co-authors Victor and Benjamin have helped me publish the work in this thesis. Thanks also go to Chris Jiggins for assisting in software testing and to Graham Stone for co-supervision and support. Over the course of my PhD I've had the pleasure of living with a procession of great flatmates and friends. I want to thank Graham and Lea for their friendship over the last seven years, and for putting up with my messy ways. This work was supported by a BBSRC studentship. Thanks are also due to the many people working on open-source software, without which this work could not have been carried out. The biggest of hugs goes to Jane for making the last six months of my PhD happier than I ever thought they would be. She has had endless understanding and patience whenever it has been required, and I'm very lucky to have her. “A tree’s a tree. How many more do you need to look at? ” Ronald Reagan, 1965 (Attrib.) Brief Table of