CGLoop: a neural network framework for chromatin loop prediction.

ABSTRACT:

Background

Chromosomes of species exhibit a variety of high-dimensional organizational features, and chromatin loops, which are fundamental structures in the three-dimensional (3D) structure of the genome. Chromatin loops are visible speckled patterns on Hi-C contact matrix generated by chromosome conformation capture methods. The chromatin loops play an important role in gene expression, and predicting the chromatin loops generated during whole genome interactions is crucial for a deeper understanding of the 3D genome structure and function.

Results

Here, we propose CGLoop, a deep learning based neural network framework that detects chromatin loops in Hi-C contact matrix. CGLoop combines the convolutional neural network (CNN) with Convolutional Block Attention Module (CBAM)

SUBMITTER: Wang J 

PROVIDER: S-EPMC11971808 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature