The human body consists of many different cell types. Cell types can be defined by the genes expressed, and unique cell-type-specific transcriptional mechanisms control these expressions. Single... Show moreThe human body consists of many different cell types. Cell types can be defined by the genes expressed, and unique cell-type-specific transcriptional mechanisms control these expressions. Single nucleotide polymorphisms (SNPs) in the DNA can be associated with diseases, but approximately 95% fall in the non-coding region. Usually, it is unknown whether these variants are causal, and which gene and cell type they affect.Advances in single-cell RNA-sequencing improved our understanding of heterogeneous tissues and led to the discovery of many new cell types. This new technology also presents computational challenges including consistent cell-type annotations. It is essential to annotate cells using classification instead of currently practiced clustering methods. To facilitate this transition, we benchmarked cell-type classification methods and developed computational methods to automatically build reference atlases using multiple already labeled single-cell datasets.Next, we establish a relationship between mutations and their effect on gene or isoform expression. We study sequence-to-expression models that can predict an alteration in expression when a mutation is observed. Given that gene expression mechanisms are cell-type specific, we introduce sequence-to-expression models based on single-cell data to make cell-type-specific predictions. We use these models to show that certain mutations are indeed changing expression, increasing our understanding of transcriptional regulation. Show less
