Resolving Deep Animal Phylogenies Through Genome-wide Maximum Likelihood Phylogenetic Inference

Theme: Evolution & Adaptation
Main Supervisor:

Christophe Dessimoz

Genetics, Evolution and the Environment & Computer Science, UCL

 max telford
Second Supervisor:

Max Telford

Genetics, Evolution and the Environment, UCL

Project Description:

Reconstructing accurate phylogenies is a major pursuit in biology, yet despite the progress made in our understanding of deep metazoan phylogeny, important questions remain. These typically involve interesting cases of rapid radiation or of rapid divergence—two drivers of biodiversity change and biological innovation.

State-of-the-art species trees are inferred from concatenated, “carefully” selected marker gene. This is wasteful at two levels. First, most genes are simply not considered. Second, even for those that are, by “averaging” their sequence through concatenation, we stand to overlook interesting instances of rapid evolution having occurred in individual genes and branches.

This interdisciplinary project involves experimental and computational biology. In the experimental part, we will collect fresh material from members of animal taxa having undergone apparent rapid radiation (e.g. Lophotrochozoan phyla) or divergence (e.g. Chaetognatha,Ctenophora,Xenacoelomorpha)  We will extract RNA and use NGS to produce transcriptomes. In the computational part, we will implement a novel tree inference method that relaxes the assumption of identical branch length across all genes and infers species trees based on branching order, not branch lengths.

Policy Impact of Research:

The sequencing revolution is generating a wealth of molecular data, yet progress at the methodological level lags behind. The new method proposed here will improve our ability to characterise and understand rapid radiation and rapid divergence.

Furthermore, the project could reveal the basis of aspects of evolutionary innovation important to the establishment of complex species.

Applications are CLOSED.

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