The biostatistics team, led by Chris Greenman, develop mathematical and statistical techniques to investigate variant detection, detect selection, perform linkage analyses and other statistical problems arising in TGAC projects. Current research interests include the development of mathematical and statistical techniques required to interpret genomic data and investigate selection, including the following areas:

• Graph theoretic techniques to analyse rearrangements
• Markov process techniques to examine copy number variation
• dN/dS techniques of examining selection
• Stochastic methods of modelling clonal growth
• Genomics of plants, microbes animals or cancer

• Within host and between host mutation dynamics of viral species
• Development of graph theoretic techniques to analyse evolution of rearrangements
• Linkage analyses and association studies of resequencing experiments
• Integrative analyses for epigentic data

• Chris Greenman - Group Leader
• Donatien Chedom Fotso - Post Doctoral Scientist
• Luca Penso Dolfin - PhD Student