HiCARN: resolution enhancement of Hi-C data using cascading residual networks.

ABSTRACT:

Motivation

High throughput chromosome conformation capture (Hi-C) contact matrices are used to predict 3D chromatin structures in eukaryotic cells. High-resolution Hi-C data are less available than low-resolution Hi-C data due to sequencing costs but provide greater insight into the intricate details of 3D chromatin structures such as enhancer-promoter interactions and sub-domains. To provide a cost-effective solution to high-resolution Hi-C data collection, deep learning models are used to predict high-resolution Hi-C matrices from existing low-resolution matrices across multiple cell types.

Results

Here, we present two Cascading Residual Networks called HiCARN-1 and HiCARN-2, a convolutional neural network and a generative adversarial network, that use a novel framework of

SUBMITTER: Hicks P 

PROVIDER: S-EPMC9048669 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature