HiCMamba: Enhancing Hi-C Resolution and Identifying 3D Genome Structures with State Space Modeling
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909537098792960 |
|---|---|
| author | Yang, Minghao Huang, Zhi-An Zheng, Zhihang Liu, Yuqiao Zhang, Shichen Zhang, Pengfei Xiong, Hui Tang, Shaojun |
| author_facet | Yang, Minghao Huang, Zhi-An Zheng, Zhihang Liu, Yuqiao Zhang, Shichen Zhang, Pengfei Xiong, Hui Tang, Shaojun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HiCMamba: Enhancing Hi-C Resolution and Identifying 3D Genome Structures with State Space Modeling Yang, Minghao Huang, Zhi-An Zheng, Zhihang Liu, Yuqiao Zhang, Shichen Zhang, Pengfei Xiong, Hui Tang, Shaojun Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | HiCMamba: Enhancing Hi-C Resolution and Identifying 3D Genome Structures with State Space Modeling |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.10713 |