The age of personal genomics will soon be upon us. With large scale projects such as Genomics England and the 1,000 genomes project, plus the rapidly falling price of genomic sequencing, a significant proportion of the population will have their own personal genome partially or fully sequenced in the coming decade. Despite an exploding demand, our ability to turn this genetic data into actionable information is however still very limited. The greatest prospect for advancing our predictive capabilities on personal genomes lies in combining big data on protein structure (1)(2), function (3) and regulation with genetic variation (4).
This project builds on well-established bioinformatics tools and resources developed by the research group, and turns them to the task of understanding how a genetic variant in a person translates into a difference in the structure/disorder or regulation of a protein, it’s function and ultimately the phenotype at the cellular, physiological or organism level. The project also provides the opportunity to collaborate with our industrial biotech partners, e.g. in one case using elite athletes as models for studying variation in the healthy human, and in another case using clinical cohorts to study variation resulting in medical phenotypes.
The project would suit highly motivated individuals coming from a range of scientific disciplines, but an aptitude for theoretical and technical thinking will be essential for developing computational skills required to carry out the research.
1) Gough, J., Hughey ,R., Karplus ,K., and Chothia ,C.(2001). Assignment of genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol. 313(4), 903-19.
2) Oates, M.E., Romero, P., Takashi, I., Ghalwash, M., Mizianty, M.J., Xue, B., Dosztanyi, Z., Uversky, V.N., Obradovic, Z., Kurgan, L., Dunker, A.K. and Gough, J. (2013) D2P2: Database of Disordered Protein Predictions. Nucl. Acids Res. 41, D508-D516.
3) Fang, H. and Gough, J. (2013) dcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more. Nucl. Acids Res. 41, D536-D544.
4) Shihab, H.A., Gough, J., Cooper, D.N., Barker, G.L.A., Edwards, K.J., Day, I.N.M., Gaunt, T. (2012) Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models. Human mutation 34(1), 57-65.