ChAI-Net predicts chromatin co-accessibility maps from DNA sequence

[bioRxiv] A bioRxiv preprint by Lee et al. combines single-molecule long-read footprinting with deep learning to study how protein-DNA interactions are coordinated within different types of cis-regulatory elements in mouse embryonic stem cells. The authors first quantify how often pairs of positions are simultaneously accessible on individual molecules, then train ChAI-Net, a neural network that predicts these locus-specific chromatin co-accessibility maps from the underlying DNA sequence. Through model interpretation and in silico experiments, the authors report that ChAI-Net learns unique combinations of sequence motifs underlying co-accessibility within enhancers, promoters and CTCF-bound sites; many of these motifs are missed when modeling average accessibility, suggesting a specific role in coordinated binding. According to the authors, the model's regulatory grammar also highlights the importance of transcription factor motif orientation, spacing, positioning and co-occurrence in driving co-accessibility.