New approaches to resolving challenging nodes in the tree of life

Diatoms

The origin of plastids in secondarily photosynthetic eukaryotes such as diatoms (pictured) remains a mystery, with different methods supporting different relationships between these organisms and the plastids of plants and algae.

The aim is to construct realistic models of evolution that allow us to simulate data sets that are very similar to those observed.

The studentship is part of the Great Western Four+ Doctoral Training Partnership, funded by NERC and starts September 2018.

Apply for this course

Read the eligibility criteria and application guidance below, then apply through the University of Bristol.

Application deadline: 7 January 2018

Research focus

The aim of this project is to use machine learning to infer phylogenetic trees from genome data to construct realistic models of evolution that allow us to simulate data sets that are very similar to those observed.

Training 

The student will receive hands-on training in bioinformatics and statistics from all three supervisors. They will also attend two international bioinformatics courses (Computational Molecular Evolution at the Sanger, and an EMBO phylogenetics workshop) taught annually by Dr Williams, ensuring the highest level of scientific training.

The student will be a member of the Bristol Doctoral College (BDC), a university-wide framework for student training in generic and transferable skills. They will attend professional development courses run by BDC and Biological Sciences throughout their PhD to develop a broader skills base in line with their training needs and aspirations, which will be reviewed quarterly. 

Methodology

We have recently begun to explore the use of machine learning to infer phylogenetic trees from genome data, and our initial results are very promising.

These approaches are based on the idea of simulating large numbers of possible trees and genomic data and then finding those trees that are most compatible with the observed data.

We will use established statistical methods, including neural networks, that allow us to do this in a principled way.

A key aspect is to calculate appropriate summary statistics from the genomic data that allow us to compare between simulated and observed data sets.

A challenge that needs to be addressed is to construct realistic models of evolution that allow us to simulate data sets that are very similar to those observed.

You will then apply these methods to three key unanswered questions in the history of life: 

  • The deepest splits in the animal and plant trees
  • The evolutionary origins of plastids in secondarily photosynthetic eukaryotes
  • The role of horizontal gene transfer in deep time

Background

Molecular phylogenetics and phylogenomics have revolutionized our view of the history and diversity of life. With the advent of modern sequencing methods, we now have enough genome data to infer an overarching tree of life. But this abundance of data creates major problems for existing phylogenetic methods, creating trade-offs between the complexity of the analysis method and the amount of data that can be analyzed.

These have led to debates about the most appropriate analyses, and controversy over key relationships across the tree of life, from the deep relationships among cellular domains to the origins of animals.  At the same time, new computational methods such as machine learning have been developed by computer scientists for the analysis of big data. These show enormous promise but have not yet been brought to bear in phylogenomics. 

Eligibility

Candidate

This project would suit a computer literate candidate with an interest in vertebrate evolution, especially of amphibians, and in the development of methods.

Studentships are open to UK and other EU students. Other nationalities (eg EEA countries) may be eligible - students should enquire with the project's respective postgraduate administration to see if they qualify for home fee rates. Up to nine studentships are available to EU students who do not ordinarily reside in the UK. Please note that this may be subject to change pending post-EU referendum discussions. All applicants need to comply with the registered university's English-language requirements.

Applicants should have obtained or be about to obtain a First or Upper Second Class UK Honours degree, or equivalent qualifications gained outside the UK. Applicants with a Lower Second Class degree will be considered if they also have a master's degree. Applicants with a minimum Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

How to apply

Applications for the PhD are processed through the University of Bristol.

The deadline for applications is 7 January 2018.

Any questions?

If you have any questions about the project please contact

Main supervisor: Dr Tom Williams

Supervisors

University of Bristol

Main supervisor: Dr Tom Williams 

Co-supervisor: Prof Mark Beaumont 

The Natural History Museum

Co-supervisor: Dr Peter Foster

References

Keeling PJ. (2010) The endosymbiotic origin, diversification and fate of plastids. Phil Trans R Soc B365: 1541. 

Philippe H, Brinkmann H, Lavrov DV, Littlewood DTJ, Manuel M, et al. (2011) Resolving Difficult Phylogenetic Questions: Why More Sequences Are Not Enough. PLOS Biology 9(3): e1000602.  

LeCun Y, Bengio Y and Hinton G (2015) Deep learning. Nature521 (7553): 436-444.

Great Western Four+ Doctoral Training Partnership

Joint PhD training partnerships between the Natural History Museum and the Great Western Four, Bath, Bristol, Cardiff and Exeter universities.

Funded by 

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