Associate Professor Jessica Mar's research group focuses on the development of bioinformatics methods to understand how regulatory processes go awry in human diseases. Specifically, the groups is interested in modelling how variability of gene expression contributes to regulation of the transcriptome. This interest has led us very naturally into single cell biology where there is a great need to develop accurate statistical approaches for data arising from single cell sequencing. Elucidating heterogeneity and variability in gene expression in this context in important as this may uncover new cellular subtypes or identify stochasticity in the usage of key pathway or master regulators.

The explosive availability of big data sets, coupled with the speed at which sequencing technologies have advanced have created an exciting environment for the current state of computational biology research. The Mar group looks to modern tools in statistics, such as Bayesian methodologies and machine learning algorithms, to make sense of biology from big data.