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

ABSTRACT: 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. Apply

SUBMITTER: Shkolikov A 

PROVIDER: S-EPMC12817080 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature