Our group is part of the Molecular Transducers of Physical Activity Consortium (MoTraPAC) . This is a large study looking at the effects of exercies through multiple genomic assays.
Together with Nathan Clark's lab we develop methods for understanding the relationship between evolutionary forces and phenotypes. Using our Relative Evolutionary Rate (RERconverge) method we have demonstrated that eye-specific genes and non-coding sequences can be identified based on patterns of relaxation in lineages of subterranean mammals.
Latent variable models for functional genomic data
Functional genomic data (such as RNAseq or DNA methylation) are composed of many layers of overlapping signals that reflect the output of individual upstream pathways. We develop methods to automatically decouple, extract and name all biological latent variables present in a dataset. Our method PLIER can estimate proportions of all the cell-types that contribute to gene expression variation in a human blood dataset, without a priori specifying their number or identity.
Automatic representation learning
Genomic data is often noisy and complex making it difficult to identify signals relevant to the underlying molecular mechanisms. We develop methods that combine machine learning techniques and insights about the biological process to learn useful data representation.
Genomics of memory B cells
Memory B-cells are immune cells that long-lived immune cells that are important for "remembering" antigen exposure and are important for vaccine efficacy. The Shlomchik lab has identified functionally distinct subsets of the cells and we are working to characterize their molecular differences using a multi-omics approach.
We are working with several UPMC research teams to use single-cell assay technologies to understand the role of the tumor micro-environment in tumor progression and treatment response.