We are conducting a large-scale study analyzing brain tissues from mice and humans with different APOE genotypes, using both single-nucleus sequencing and spatial transcriptomics to assess RNA expression differences caused by APOE genotype. We are working with an expert bioinformatics core, but would like a data science student to help perform the analyses and act as an in-lab lead for the bioinformatics analysis. Prior experience analyzing RNA-sequencing data is preferred, but not required.
This project builds on a novel cellular model of human aging (Sturm et al. Epigenomics 2019) where we can investigate trajectories of multiple molecular features of aging over long time periods. The underlying multi-omic dataset includes epigenomic (DNA methylation), proteomic (protein abundance), bioenergetics (mitochondrial respiration), telomere length, and various secreted factors. A major challenge for the DSI Fellow will be to integrate the multi-omic dataset to capture dynamic signatures of mitochondrial dysfunction and cellular aging, working collaboratively with other scientists. The existing project is expected to result in one or more publications. Possibility to continue work for pay over the summer.
The function for much of the 3 billion letters in the human genome remain to be understood. Advances in DNA sequencing technology have generated enormous amount of data, yet we don’t have the tool to extract rules of how the genome works. Deep learning holds great potential in decoding the genome, in particular due to the digital nature of DNA sequences and the ability to handle large data sets. However, like many other applications, the interpretability of deep learning models hampers its ability to help understand the genome. We are developing deep learning architectures embedded with the principles of gene regulation and we will be leveraging billions of existing measurements of gene activity to learn a mechanistic model of gene regulation in human cells.
