HiCMamba: Enhancing Hi-C Resolution and Identifying 3D Genome Structures with State Space Modeling
This work addresses the challenge of high sequencing costs and technical limitations in Hi-C technology for researchers studying 3D genome structures, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of limited coverage in Hi-C data, which leads to imprecise chromatin interaction estimates, by introducing HiCMamba, a deep learning method that enhances resolution using a state space model, outperforming state-of-the-art methods and reducing computational resources.
Hi-C technology measures genome-wide interaction frequencies, providing a powerful tool for studying the 3D genomic structure within the nucleus. However, high sequencing costs and technical challenges often result in Hi-C data with limited coverage, leading to imprecise estimates of chromatin interaction frequencies. To address this issue, we present a novel deep learning-based method HiCMamba to enhance the resolution of Hi-C contact maps using a state space model. We adopt the UNet-based auto-encoder architecture to stack the proposed holistic scan block, enabling the perception of both global and local receptive fields at multiple scales. Experimental results demonstrate that HiCMamba outperforms state-of-the-art methods while significantly reducing computational resources. Furthermore, the 3D genome structures, including topologically associating domains (TADs) and loops, identified in the contact maps recovered by HiCMamba are validated through associated epigenomic features. Our work demonstrates the potential of a state space model as foundational frameworks in the field of Hi-C resolution enhancement.