Deciphering epigenomic code for cell differentiation using deep learning.

ABSTRACT:

Background

Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiation. To fill this gap, we employed two types of deep convolutional neural networks (CNNs) constructed for each of differentially related cell types and for each of histone marks measured in the cells, to learn the sequence determinants of various histone modification patterns in each cell type.

Results

We applied our models to four differentially related human CD₄⁺ T cell types and six histone marks measured in each cell type. The cell models can accurately predict the histone marks in each cell type, while the

SUBMITTER: Ni P 

PROVIDER: S-EPMC6739944 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature