Deep Learning on Chromatin Accessibility

Abstract

DNA accessibility has been a powerful tool in locating active regulatory elements in a cell type, but dissecting the combinatorial logic within these regulatory elements has been a continued challenge in the field. Deep learning models have been shown to be highly predictive models of regulatory DNA and have led to new biological insights on regulatory syntax and logic. Here, we provide a framework for deep learning in genomics that implements best practices and focuses on ease of use, versatility, and compatibility with existing tools for inference on DNA sequence.

Keywords: ATAC-seq; DNA accessibility; DNase-seq; Deep learning; Machine learning.