Julian Gough

Julian Gough

Cell Reprogramming and Personal Genomics
Group Leader page

Project 1: Systematic cell reprogramming –transdifferentiation– cell fate and evolution using computational approaches..

The field of regenerative medicine was stunned in 2007 by the possibility of creating stem cells from adult human cells (1). Since then it has been shown that it is also possible to trans-differentiate from one mature human cell type to another. The process however is still poorly understood, and as a result, only a handful of different human cell types have successfully been created in the decade following the 2007 discovery (by Yamanaka).

We apply computational approaches to the predominantly experimental field of cell differentiation that bring a systematic and data-driven guide for directing research in the field (2). We combine theory of transcriptional regulation and cell networks with big data from high-throughput experiments relating to gene expression and molecular interaction. As well as expanding the repertoire of possible human cell conversions and pushing the boundaries of cellular manipulation, the PhD project will aim to deepen the understanding of cell fate and evolution (3).

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.


References

Takahashi, K., Tanabe, K., Ohnuki, M., Narita, M., Ichisaka, T., Tomoda, K. and Yamanaka, S. (2007)
Induction of pluripotent stem cells from adult human fibroblasts by defined factors.
Cell, 5, 861-872.

Rackham, O.J.L., Firas, J., Fang, H.,Oates, M.E., Holmes, M.L., Knaupp, A.S., The FANTOM Consortium, Suzuki, H.,Nefzger, C.M., Daub, C.O., Shin, J.W., Petretto, E.P., Forrest, A.R.R., Hayashizaki, Y., Polo, J.M. and Gough, J. (2016)
A predictive computational framework for direct reprogramming between human cell types.
Nature Genetics, 331-335.

Sardar, A.J., Oates, M.E., Fang, H., Forrest, A.R.R., Kawaji, H., The FANTOM consortium, Gough, J. and Rackham, O. (2014)
The Evolution of Human Cells in Terms of Protein Innovation.
Mol. biol. evol., 31(6), 1364-74.


Project 2: Personal genomics - computational bioinformatics of protein structure, disorder, function and phenotype.

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.


References

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.

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.

Fang, H. and Gough, J. (2013)
dcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more.
Nucl. Acids Res. 41, D536-D544.

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.