AUTHOR=Zheng Leqiong , Liu Li , Zhu Wen , Ding Yijie , Wu Fangxiang 

TITLE=Predicting enhancer-promoter interaction based on epigenomic signals

JOURNAL=Frontiers in Genetics

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1133775

DOI=10.3389/fgene.2023.1133775

ISSN=1664-8021

ABSTRACT=<p><bold>Introduction:</bold> The physical interactions between enhancers and promoters are often involved in gene transcriptional regulation. High tissue-specific enhancer-promoter interactions (EPIs) are responsible for the differential expression of genes. Experimental methods are time-consuming and labor-intensive in measuring EPIs. An alternative approach, machine learning, has been widely used to predict EPIs. However, most existing machine learning methods require a large number of functional genomic and epigenomic features as input, which limits the application to different cell lines.</p><p><bold>Methods:</bold> In this paper, we developed a random forest model, HARD (H3K27ac, ATAC-seq, RAD21, and Distance), to predict EPI using only four types of features.</p><p><bold>Results:</bold> Independent tests on a benchmark dataset showed that HARD outperforms other models with the fewest features.</p><p><bold>Discussion:</bold> Our results revealed that chromatin accessibility and the binding of cohesin are important for cell-line-specific EPIs. Furthermore, we trained the HARD model in the GM12878 cell line and performed testing in the HeLa cell line. The cross-cell-lines prediction also performs well, suggesting it has the potential to be applied to other cell lines.</p>