Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts.

ABSTRACT:

Motivation

Genome-wide profiles of chromatin accessibility and gene expression in diverse cellular contexts are critical to decipher the dynamics of transcriptional regulation. Recently, convolutional neural networks have been used to learn predictive cis-regulatory DNA sequence models of context-specific chromatin accessibility landscapes. However, these context-specific regulatory sequence models cannot generalize predictions across cell types.

Results

We introduce multi-modal, residual neural network architectures that integrate cis-regulatory sequence and context-specific expression of trans-regulators to predict genome-wide chromatin accessibility profiles across cellular contexts. We show that the average accessibility of a genomic region across training contexts can b

SUBMITTER: Nair S 

PROVIDER: S-EPMC6612838 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature