Understanding the relative relationships between sponges and comb jellies and early animal evolution using novel phylogenomic methods and the integrat

Project Background The relationships at the root of the animal tree is an area of great scientific debate [1-5]. The problem lies with the comb jellies (Figure A; phylum Ctenophora) that have been suggested to be the sister group of either: (1) all the animals (Ctenophora-sister hypothesis [e.g. 1,2]), (2) all animals but the sponges (Porifera-sister hypothesis [e.g. 3,4]; Figure B1), or (3) the Cnidaria - jellyfishes and corals (the Coelenterata hypothesis [5]; Figure A2). Resolving the relationships at the root of the animals is fundamental to clarify the evolution of key features, like the neurons and the through gut [1-5]. However, how to resolve such relationships is unclear, as the use of different models on different data types invariably finds support for alternative hypotheses [1-5]. This project proposes to use new methods and data to understand what underpins the phylogenetic instability of the ctenophores and test alternative hypotheses of animal relationships.

Project Aims and Methods
The project proposes to take a holistic approach to understand relationships at the root of the animal; investigating multiple data types using different methods of analyses to understand how and why alternative approaches (i.e. data plus methods) are differently tied to the recovery of alternative hypotheses. We propose to use a mixture of theoretical and applied approaches (including simulations and the analyses of new genomic-scale datasets) to identify potential problems with previous approaches and develop strategies to address them to disambiguate phylogenetic relationships. The ultimate aim of this project is to test alternative hypotheses of animal relationships that have previously been suggested for the comb jellies and the animals more broadly (i.e. the Ctenophora-sister, Porifera-sister and Coelenterata hypotheses).
There is a significant degree of flexibility built within the project as you will play a key role, in (1) deciding what type of data to focus on (e.g. amino acid sequences, rare genomic changes, or even morphology), (2) developing the best pipelines to identify potential biases that might be method or data specific, and (3) design analytical strategies to circumvent such biases.