Name
Abstract | ||
|---|---|---|
Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/ deephic) that allows researchers to enhance their own Hi-C data with just a few clicks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1371/journal.pcbi.1007287 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 16 | 2 |
ISSN | Citations | PageRank |
1553-734X | 0 | 0.34 |
References | Authors | |
0 | 15 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Hong | 1 | 1 | 2.05 |
| Shuai Jiang | 2 | 4 | 2.48 |
| Hao Li | 3 | 261 | 85.92 |
| Guifang Du | 4 | 0 | 0.68 |
| Yu Sun | 5 | 0 | 0.34 |
| Huan Tao | 6 | 0 | 1.35 |
| Cheng Quan | 7 | 0 | 0.34 |
| Chenghui Zhao | 8 | 1 | 1.04 |
| Ruijiang Li | 9 | 2 | 1.40 |
| Wanying Li | 10 | 2 | 1.74 |
| Xiaoyao Yin | 11 | 0 | 0.34 |
| Yangchen Huang | 12 | 0 | 0.34 |
| Cheng Li | 13 | 114 | 18.42 |
| Hebing Chen | 14 | 2 | 1.74 |
| Xiaochen Bo | 15 | 285 | 23.72 |