Statistical Bioinformatics and Genomics: Modern genomic technologies produce huge amounts of data that allow us to examine gene activity on a genome-wide scale. We can observe which genes are turned on and how active they are in any type of cell at any time. My research group develops advanced computational and statistical strategies to analyse and interpret these huge data sets. In collaboration with biomedical collaborators, we examine which genes play essential roles in normal cell development and which genes are disrupted or activated inappropriately in any particular disease. Our goal is to learn how diseases originate by examining how genetic disruption comes about and how it might be controlled. We analyse data from a number of genomic technologies, especially RNA sequencing (RNA-seq), but also DNA sequencing, gene expression microarrays, protein arrays, mass spectrometry and high-throughput PCR arrays. One of our key interests is the identification of genes, transcripts or molecular pathways that are differentially expressed between experimental conditions. We also analyse ChIP sequencing experiments to detect changes in the DNA epigenetic marks and DNA structure. We develop high performance algorithms to map short sequence reads to a reference genome. We use mathematical techniques such a linear modelling and empirical Bayes to borrow strength between genes and between experimental units, providing robust statistical conclusions even when the number of experimental units is relatively small.