DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.

ABSTRACT:

Background

Chromatin loops are critical for the three-dimensional organization of the genome and gene regulation. Accurate identification of chromatin loops is essential for understanding the regulatory mechanisms in disease. However, current mainstream detection methods rely primarily on single-source data, such as Hi-C, which limits these methods' ability to capture the diverse features of chromatin loop structures. In contrast, multi-source data integration and deep learning approaches, though not yet widely applied, hold significant potential.

Results

In this study, we developed a method called DconnLoop to integrate Hi-C, ChIP-seq, and ATAC-seq data to predict chromatin loops. This method achieves feature extraction and fusion of multi-source data by integrating residua

SUBMITTER: Wang J 

PROVIDER: S-EPMC11959853 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature