COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartments with Epigenomic Information.

Chromatin compartmentalization and epigenomic modification are crucial in cell differentiation and diseases development. However, precise mapping of chromatin compartmental patterns requires Hi-C or Micro-C data at high sequencing depth. Exploring the systematic relationship between epigenomic modifications and compartmental patterns remains challenging. To address these issues, we present COCOA, a deep neural network framework using convolution and attention mechanisms to infer fine-scale chromatin compartment patterns from six histone modification signals. COCOA extracts 1-D track features through bi-directional feature reconstruction after resolution-specific binning epigenomic signals. These track features are then cross-fused with contact features using an attention mechanism and transformed into chromatin compartment patterns through residual feature reduction. COCOA demonstrates accurate inference of chromatin compartmentalization at a fine-scale resolution and exhibits stable performance on test sets. Additionally, we explored the impact of histone modifications on chromatin compartmentalization prediction through in silico epigenomic perturbation experiments. Unlike obscure compartments observed with 1 kb resolution high-depth experimental data, COCOA generates clear and detailed compartmental patterns, highlighting its superior performance. Finally, we demonstrated that COCOA enables cell-type-specific prediction of unrevealed chromatin compartment patterns in various biological processes, making it an effective tool for gaining chromatin compartmentalization insights from epigenomics in diverse biological scenarios. The COCOA python code is publicly available at https://github.com/onlybugs/COCOA.