Computational Biology Group Research Overview

Research Goals

The development of statistical methodology for analysis of high-throughput genomic technology such as microarrays, for the analysis of whole-genome scans for association mapping, and for systems biology.

Current Research

Our research focuses on statistical methods in molecular biology, human genetics, population genetics, molecular evolution and cancer biology. We have a strong interest in cancer computational biology involving the analysis of data from a variety of microarray technologies including expression, arrayCGH, DNA methylation, metabolomics, microRNA expression profiling, alternate splicing experiments, and uncovering the genetic basis of variation in gene expression. Simon Tavaré also studies stem cell evolution, part of a broader study of mechanisms of tumour progression. His research in Monte Carlo inference methods includes Markov chain Monte Carlo without likelihoods and approximate Bayesian computation, often applied in the setting of population genetics (for example, linkage disequilibrium mapping) or paleobiology.

Our research topics can be broadly classified into the following areas:

• Microarray expression analysis studies
• Recovery of single-channel data from two-colour microarrays
• Analysis of arrayCGH data
• Joint analysis of arrayCGH and expression data
• Analysis of Illumina BeadArray data
• Development and analysis of DNA methylation arrays
• Studies involving metabolomics data
• Analysis of microRNA expression data in cancer
• Alternative splicing studies using microarrays
• Cancer grid project
• R software development

Core support and consultations

The Computational Biology Group also provides support to local research groups in the form of teaching, small data analysis tasks, consultation on experimental design, and guidance on analysis approaches.