Professor Geoff Barton FRSB
The completion in June 2000 of the first draft of the 3 Billion bases of DNA in the Human Genome was the most public demonstration that molecular biology had become a data intensive science. In today's “post-genome era” the DNA sequence of Human and other organisms is only the tip of an iceberg of data that includes information on gene expression (transcriptomics), protein expression (proteomics) and protein structure (structural genomics). These experimental techniques produce prodigious amounts of data that can only be organised, compared, understood and exploited to further scientific understanding and to cure disease by the development and application of advanced computational methods.
Bioinformatics is the research field that seeks to find computational ways of understanding biological systems. The subject is very broad and ranges from research in statistics and computer science, through software engineering and database development, to applications in specific biological systems. The possible biological applications are equally broad, from the study of populations through molecular structure and interactions, to simulations of metabolic and signalling processes.
Our work draws on and contributes to computer science, software engineering and statistics on one side and many aspects of modern biological research on the other. We publish our work both in conventional journals and as software packages and on-line resources accessible from our website: www.compbio.dundee.ac.uk. Many of our techniques and databases are widely used by the biological research community, these include JPred, a service for protein secondary structure prediction that performs up to 100,000 predictions a month for scientists worldwide and Jalview, a protein sequence analysis workbench that is installed on at least 55,000 computers in over 100 countries and is started more than 250,000 times per year. Our core research interests have long centered on the analysis and prediction of protein structure and function, but in recent years we have turned our attention to the problems of interpreting large and diverse biological datasets as well as the analysis of small RNAs. We now collaborate extensively with “wet lab” scientists and clinicians across a broad range of biological domains from plants through model organisms (e.g. Dictyostelium, chicken, Drosophila and mouse) to individual humans and human disease. Our group focuses in particular on the design of experiments and the interpretation of large datasets from proteomics and deep RNA/DNA sequencing to address questions in basic science and their clinical applications.
Addressing the specific biological problems important to each biological research area suggest gaps in our understanding of how proteins or other biological molecules function and so prompts us to perform new general studies. In turn these lead to the development of new and improved predictors that we can apply to the specific systems of interest to our wet-lab colleagues.
A more comprehensive description of our work can be found on the group web site www.compbio.dundee.ac.uk together with links to our web-accessible software, databases and downloads. For a complete publication list see our page on Google Scholar.
1. Schurch, N. J., Schofield, P., Gierlinski, M., Cole, C., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G. G., Owen-Hughes, T., Blaxter, M. and Barton, G. J. (2016) How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? Rna
2. Madeira, F., Tinti, M., Murugesan, G., Berrett, E., Stafford, M., Toth, R., Cole, C., MacKintosh, C. and Barton, G. J. (2015) 14-3-3-Pred: Improved methods to predict 14-3-3-binding phosphopeptides. Bioinformatics
3. Gierlinski, M., Cole, C., Schofield, P., Schurch, N. J., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G., Owen-Hughes, T., Blaxter, M. and Barton, G. J. (2015) Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment. Bioinformatics
4. Drozdetskiy, A., Cole, C., Procter, J. and Barton, G. J. (2015) JPred4: a protein secondary structure prediction server. Nucleic acids research
5. Cole, C., Kroboth, K., Schurch, N. J., Sandilands, A., Sherstnev, A., O'Regan, G. M., Watson, R. M., Irwin McLean, W. H., Barton, G. J., Irvine, A. D. and Brown, S. J. (2014) Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic dermatitis. The Journal of allergy and clinical immunology. 134, 82-91
6. Duc, C., Sherstnev, A., Cole, C., Barton, G. J. and Simpson, G. G. (2013) Transcription termination and chimeric RNA formation controlled by Arabidopsis thaliana FPA. PLoS genetics. 9, e1003867
7. Sherstnev, A., Duc, C., Cole, C., Zacharaki, V., Hornyik, C., Ozsolak, F., Milos, P. M., Barton, G. J. and Simpson, G. G. (2012) Direct sequencing of Arabidopsis thaliana RNA reveals patterns of cleavage and polyadenylation. Nature structural & molecular biology. 19, 845-852
8. Scott, M. S., Boisvert, F. M., McDowall, M. D., Lamond, A. I. and Barton, G. J. (2010) Characterization and prediction of protein nucleolar localization sequences. Nucleic acids research. 38, 7388-7399
9. Waterhouse, A. M., Procter, J. B., Martin, D. M., Clamp, M. and Barton, G. J. (2009) Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics. 25, 1189-1191
10. Jefferson, E. R., Walsh, T. P. and Barton, G. J. (2006) Biological units and their effect upon the properties and prediction of protein-protein interactions. Journal of molecular biology. 364, 1118-1129