HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences
Abstract Prediction of chromatin interactions from DNA sequence has been a significant research challenge in the last couple of years. Several solutions have been proposed, most of which are based on encoder-decoder architecture, where 1D sequence is convoluted, encoded into the latent representatio...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-10-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-024-10885-z |
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| Summary: | Abstract Prediction of chromatin interactions from DNA sequence has been a significant research challenge in the last couple of years. Several solutions have been proposed, most of which are based on encoder-decoder architecture, where 1D sequence is convoluted, encoded into the latent representation, and then decoded using 2D convolutions into the Hi-C pairwise chromatin spatial proximity matrix. Those methods, while obtaining high correlation scores and improved metrics, produce Hi-C matrices that are artificial - they are blurred due to the deep learning model architecture. In our study, we propose the HiCDiffusion, sequence-only model that addresses this problem. We first train the encoder-decoder neural network and then use it as a component of the diffusion model - where we guide the diffusion using a latent representation of the sequence, as well as the final output from the encoder-decoder. That way, we obtain the high-resolution Hi-C matrices that not only better resemble the experimental results - improving the Fréchet inception distance by an average of 11 times, with the highest improvement of 56 times - but also obtain similar classic metrics to current state-of-the-art encoder-decoder architectures used for the task. |
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| ISSN: | 1471-2164 |