Deciphering the 3D genome organization across species from Hi-C data

3D genome organization is essential for gene regulation, yet in various species it is driven by different biological mechanisms. Species-specific factors and DNA sequences influence chromatin folding, complicating cross-species comparisons. Leveraging Hi-C data and machine learning, we introduce Chimaera-a convolutional neural network that predicts Hi-C maps from DNA sequences, enabling exploration of genome folding in evolution. Chimaera's latent representations revealed an unsupervised atlas of key chromatin features (such as insulation, loops, fountains/jets) and supported the detection and quantification of structural signatures in processes such as the cell cycle and embryogenesis. Targeted search in the latent space linked DNA sequence elements to specific chromatin structures. Applying Chimaera across multiple species confirmed the insulator roles of CTCF in vertebrates and BEAF-32 in Drosophila melanogaster and identified a previously unreported insulator motif in D. melanogaster. In amoeba Dictyostelium discoideum, gene orientation on the DNA strand was shown to influence loop formation. Models for other organisms also showed chromatin folding patterns associated with gene location. Finally, using cross-species predictions we tested the transferability of chromatin folding patterns and revealed evolutionary relationships, culminating in a chromatin structure-based cluster tree spanning plants to mammals.