The identity of a cell is fundamentally governed by the set of genes expressed from its genome. Thus gene expression regulation is a key process for brain development and cell type specification, but also for cognitive functions such as learning and memory. We are working on understanding how the human genome achieves the extraordinary regulatory complexity required for fine-tuning gene expression in the human brain. We focus on two key areas: the role of non-coding regulatory elements and non-coding RNAs such as circular RNAs.
The role of enhancer elements in the human brain
The biogenesis and function of circular RNAs in the human brain
Gene expression measurements, similarly to DNA methylation and proteomic measurements, are strongly influenced by cellular composition. We are interested in controlling the effect of cellular composition on our analyses of bulk gene expression, and have carried out an extensive benchmarking of deconvolution methods on human brain data (Sutton et al, 2021).
We are interested in methods that can capture non-linear relationships in gene expression data. Topological data analysis uses concepts from topology to extract information from multi-dimensional data. We have developed TDAview (Walsh et al. 2020), a user-friendly implementation and visualization of the Mapper algorithm. We are also curious about the information that persistent homology can extract from gene expression data (Shnier at el. 2019).