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) and the Bidirectional Gated Recurrent Unit (BiGRU) to capture important features related to chromatin loops by comprehensively analyzing the Hi-C contact matrix, enabling the prediction of candidate chromatin loops. And CGLoop employs a density based clustering method to filter the candidate chromatin loops predicted by the neural network model. Finally, we compared CGloop with other chromatin loops prediction methods on several cell line including GM12878, K562, IMR90, and mESC. The code is available from https://github.com/wllwuliliwll/CGLoop .

Conclusions: The experimental results show that, loops predicted by CGLoop show high APA scores and there is an enrichment of multiple transcription factors and binding proteins at the predicted loops anchors, which outperforms other methods in terms of accuracy and validity of chromatin loops prediction.