DeLoop: a deep learning model for chromatin loop prediction from sparse ATAC-seq data

Abstract

Deciphering gene regulation and understanding the functional implications of disease-associated non-coding variants require the identification of cell-type-specific 3D chromatin interactions. Current chromosome conformation capture technologies fall short in resolution when handling limited cell inputs. To address this limitation, we introduce DeLoop, a deep learning model designed to predict CTCF-mediated chromatin loops from sparse ATAC-seq data by leveraging multitask learning techniques and attention mechanisms. Our model utilizes ATAC-seq data and DNA sequence features, showcasing superior performance compared to existing state-of-the-art models, particularly under low read depth conditions, enabling accurate chromatin loop inference when sufficient cells are infeasible. In addition, generalizing across cell types, DeLoop proves effective in de novo prediction tasks and its potential for predicting functional interactions.