Exploiting sequence-based features for predicting enhancer–promoter interactions

Motivation: A large number of distal enhancers and proximal promoters form enhancer‐promoter interactions to regulate target genes in the human genome. Although recent high‐throughput genome‐wide mapping approaches have allowed us to more comprehensively recognize potential enhancer‐promoter interactions, it is still largely unknown whether sequence‐based features alone are sufficient to predict such interactions. Results: Here, we develop a new computational method (named PEP) 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. The two modules in PEP (PEP‐Motif and PEP‐Word) use different but complementary feature extraction strategies to exploit sequence‐based information. The results across six different cell types demonstrate that our method is effective in predicting enhancer‐promoter interactions as compared to the state‐of‐the‐art methods that use functional genomic signals. Our work demonstrates that sequence‐based features alone can reliably predict enhancer‐promoter interactions genome‐wide, which could potentially facilitate the discovery of important sequence determinants for long‐range gene regulation. Availability and Implementation: The source code of PEP is available at: https://github.com/ma‐compbio/PEP. Contact: jianma@cs.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.