About the Project
1) Collate available datasets (transcriptomic/phosphoproteome) correlating changes across the cell cycle and in different breast cancer sub-types, to predict kinase-substrate relationships using machine learning techniques.
2) Use derived network information to predict key enzymes regulating NCP, which will be targeted for cellular perturbation and pathway validation.
3) Correlate identified sites of NCP with hotspots of cancer mutation with a view to pursuing future avenues for therapeutic intervention
Experimental Approach & Application:
The project will be largely computational, and thus would suit individuals with a background in bioinformatics / computational biology, or from engineering, computing, statistics or related background with a strong interest in biology. Students from a biological background (e.g. BSc in Biochemistry) keen to learn informatics methods are also encouraged to apply, and you will be given training in the relevant methods.
You will develop bioinformatics approaches and machine learning strategies (including e.g. neural networks), to define the relationship between dynamic NCP events and transcript profiles, as well as mutational hotspots in cancer. You will evaluate and validate potential NCP regulatory pathways in cancer models using small molecule inhibitors, siRNA and/or site specific CRIPSR/cas9 knock-in, and phosphoproteomics, either performing the experiments yourself or working in partnership with other team members in lab.
You will join an established collaboration between the two supervisors, Prof. Claire Eyers (Director, Centre for Proteome Research, https://www.liverpool.ac.uk/cpr/; Twitter: @ClaireEEyers) and Prof. Andy Jones (Director, Computational Biology Facility, https://www.liverpool.ac.uk/computational-biology-facility/; Twitter: @andy___jones), both at the University of Liverpool. Consequently, they will benefit from extensive support from both ‘wet’ and ‘dry’ scientists generating data on non-canonical phosphorylation and have access to cutting edge mass spectrometry technology. This project will expand considerably our understanding of these new kinase: substrate relationships and allow us to validate experimentally the hypotheses that arise from computational network approaches.
Assistance will be given to those who are applying to international funding schemes.
The successful applicant will be expected to provide the funding for tuition fees and living expenses as well as research costs of up to £2500 per year.
Details of costs can be found on the University website:
Ren Z et al (2019) Improvements to the Rice Genome Annotation Through Large-Scale Analysis of RNA-Seq and Proteomics Data Sets Mol Cell Proteomics. 18(1): 86–98. doi: 10.1074/mcp.RA118.000832
Ferries SJ et al. (2017) Evaluation of Parameters for Confident Phosphorylation Site Localization Using an Orbitrap Fusion Tribrid Mass Spectrometer. J Proteome Research 16, 3448
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