Sheffield Institute for Nucleic Acids

Dr Ian Sudbery

Research Interests

A cell’s molecular and functional identity is determined by its complement of proteins, the levels of which are, in large part determined by the levels of gene expression. Dr Sudbery and his group are interested in how the genome and transcriptome integrate information to make decisions about gene expression, and how this goes wrong in disease. They approach these questions using a range of computational and genomic approaches, particularly analysis of data generated by next-generation sequencing techniques, including RNA-seq, iCLIP and Capture-C.

Ongoing interests include how changes in the three dimensional structure of chromatin affect the expression status of the cell in disease, and why mutations in housekeeping genes tend to lead to cell-type specific pathology.


  • Greig, J. a, Sudbery, I.M., Richardson, J.P., Naglik, J.R., Wang, Y., and Sudbery, P.E. (2015). Cell Cycle-Independent Phospho-Regulation of Fkh2 during Hyphal Growth Regulates Candida albicans Pathogenesis. PLOS Pathog. 11, e1004630.
  • Sims, D., Sudbery, I., Ilott, N. E., Heger, A., & Ponting, C. P. (2014). Sequencing depth and coverage: key considerations in genomic analyses. Nature Reviews Genetics, 15, 121–32.
  • Rajan, P*., Stockley, J*., Sudbery, I.M.*, Fleming, J.T., Hedley, A., Kalna, G., Sims, D., Ponting, C.P., Heger, A., Robson, C.N., et al. (2014). Identification of a candidate prognostic gene signature by transcriptome analysis of matched pre- and post-treatment prostatic biopsies from patients with advanced prostate cancer. BMC Cancer 14, 977.
  • Rajan, P*., Sudbery, I.M*., Villasevil, M.E.M., Mui, E., Fleming, J., Davis, M., Ahmad, I., Edwards, J., Sansom, O.J., Sims, D., et al. (2014a). Next-generation Sequencing of Advanced Prostate Cancer Treated with Androgen-deprivation Therapy. Eur. Urol. 66, 32–39.
  • Sims, D., Ilott, N.E., Sansom, S.N., Sudbery, I.M., Johnson, J.S., Fawcett, K. a., Berlanga-Taylor, A.J., Luna-Valero, S., Ponting, C.P., and Heger, A. (2014). CGAT: Computational genomics analysis toolkit. Bioinformatics 30, 1290–1291.
  • Farcas, A.M., Blackledge, N.P., Sudbery, I., Long, H.K., McGouran, J.F., Rose, N.R., Lee, S., Sims, D., Cerase, A., Sheahan, T.W., et al. (2012). KDM2B links the Polycomb Repressive Complex 1 (PRC1) to recognition of CpG islands. Elife 1, e00205.
  • Sudbery, I., Stalker, J., Simpson, J.T., Keane, T., Rust, A.G., Hurles, M.E., Walter, K., Lynch, D., Teboul, L., Brown, S.D., et al. (2009). Deep short-read sequencing of chromosome 17 from the mouse strains A/J and CAST/Ei identifies significant germline variation and candidate genes that regulate liver triglyceride levels. Genome Biol. 10, R112.
  • Sudbery, I., Enright, A.J., Fraser, A.G., and Dunham, I. (2010). Systematic analysis of off-target effects in an RNAi screen reveals microRNAs affecting sensitivity to TRAIL-induced apoptosis. BMC Genomics 11, 175.
Dr Ian Sudbery
Lecturer in Bioinformatics, Centre Webmaster
School of Biosciences
+44 (0)114 222 2738