REnformer, a single-cell ATAC-seq predicting model to investigate open chromatin sites

Abstract

Genome regulatory elements are fundamental to cellular identity and cell type specific gene expression. Understanding how the underlying genetic code is differentially utilised by different cell types is central to understanding human health and disease. To better understand how DNA encodes genome regulatory elements such as promoters, enhancers, and boundary elements, we leverage the Enformer gene expression and epigenetic prediction model. We used transfer learning with high quality single cell ATAC datasets to develop REnformer, a model to predict chromatin accessibility. By introducing a bench-mark for comparing performances against Enformer model, REnformer significantly outperformed Enformer in terms of higher prediction outcomes and lower error rates in all extensive analyses shown; introducing these benchmarks allowed us, and possible future works, to fairly compare such models. We further tested REnformer by predicting the effects of a well characterised -thalassemia variant and found that the prediction aligned with the observed change in genome regulatory element, previously validated. We conclude that REnformer is and can be a state-of-the-art tool to predict cell type specific regulatory elements, and interrogate the effect of genome variation in health and disease.