CD-Loop: a chromatin loop detection method based on the diffusion model - Shen, Jiquan; Wang, Yang; Luo, Junwei (Frontiers in Genetics, 2024)
Motivation: In recent years, there have been significant advances in various chromatin conformation capture techniques, and annotating the topological structure from Hi-C contact maps has become crucial for studying the three-dimensional structure of chromosomes. However, the structure and function of chromatin loops are highly dynamic and diverse, influenced by multiple factors. Therefore, obtaining the three-dimensional structure of the genome remains a challenging task. Among many chromatin loop prediction methods, it is difficult to fully extract features from the contact map and make accurate predictions at low sequencing depths. Results: In this study, we put forward a deep learning framework based on the diffusion model, called CD-Loop for predicting accurate chromatin loops. First, by pretraining the input data, we obtain prior probabilities for predicting the classification of Hi-C contact map. Then by combining the denoising process based on the diffusion model and the prior probability obtained by pre-training, predict candidate loops from the input Hi-C contact map. Finally, CD-Loop uses a density-based clustering algorithm to cluster the candidate chromatin loops and predict the final chromatin loops. We compared CD-Loop with the currently popular methods, such as Peakachu, Chromosight, and Mustache, and found that in different cell types, species, and sequencing depths, CD-Loop outperforms other methods in loop annotation. We conclude that CD-Loop can accurately predict chromatin loops and reveal cell type specificity. The code is available from https://github.com/wangyang199897/CD-Loop. chromatin loop, in close spatial proximity, regulating the target gene by binding to the promote(Dekker