Predicting enhancer-promoter interaction from genomic sequence with deep neural networks.

ABSTRACT:

Background

In the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions.

Methods

Here we report a new computational method (named "SPEID") using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given.

Results

Our results across six different cell types demonstrate that SPEID i

SUBMITTER: Singh S 

PROVIDER: S-EPMC8188889 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature