Computational methods for identifying enhancer-promoter interactions

Abstract

Background: As parts of the cis-regulatory mechanism of the human genome, interactions between distal enhancers and proximal promoters play a crucial role. Enhancers, promoters, and enhancer-promoter interactions (EPIs) can be detected using many sequencing technologies and computation models. However, a systematic review that summarizes these EPI identification methods and that can help researchers apply and optimize them is still needed.

Results: In this review, we first emphasize the role of EPIs in regulating gene expression and describe a generic framework for predicting enhancer-promoter interaction. Next, we review prediction methods for enhancers, promoters, loops, and enhancer-promoter interactions using different data features that have emerged since 2010, and we summarize the websites available for obtaining enhancers, promoters, and enhancer-promoter interaction datasets. Finally, we review the application of the methods for identifying EPIs in diseases such as cancer.

Conclusions: The advance of computer technology has allowed traditional machine learning, and deep learning methods to be used to predict enhancer, promoter, and EPIs from genetic, genomic, and epigenomic features. In the past decade, models based on deep learning, especially transfer learning, have been proposed for directly predicting enhancer-promoter interactions from DNA sequences, and these models can reduce the parameter training time required of bioinformatics researchers. We believe this review can provide detailed research frameworks for researchers who are beginning to study enhancers, promoters, and their interactions.

Author summary

In this review article, we discuss prediction methods for enhancers, promoters, and enhancer-promoter interactions that may help researchers choose methods for identifying cis-regulatory elements.

Graphical abstract

Keywords

enhancer / promoter / enhancer-promoter interaction / machine learning / deep learning

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ABBREVIATIONS

CREs	cis-acting regulatory elements
EPI(s)	Enhancer-promoter interaction(s)
TSS	Transcription start sites
ChIP-seq	Chromatin immunoprecipitation
CUT& RUN	Cleavage under targets and release using nuclease
Hi-C	High-throughput chromosome conformation capture
ChIA-PET	Chromatin interaction analysis with paired-end-tag sequencing
TFs	Transcription factors
TFBS	Transcription factor binding sites
CKSNAP	Composition of k-spaced nucleic acid pair
DCC	Dinucleotide-based cross covariance
PseDNC	Pseudo dinucleotide composition
PseKNC	Pseudo k-tuple nucleotide composition
SVM	Support vector machine
CNN	Convolution neural network
GBDT	Gradient boosting decision tree
LSTM	Long short-term memory
DE	Downstream element

ACKNOWLEDGEMENTS

This study was funded by grants from the Foshan Higher Education Foundation (No. BKBS202203), the National Key R&D Program of China (No. 2018YFA0801402), the National Natural Science Foundation of China (No. 61971031) and the CAMS Innovation Fund for Medical Sciences (Nos. 2021-RC310-007, 2021-I2M-1-020 and 2022-I2M-1-020). Funding for open access charge: Department of Computer Science and Technology, Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing. The authors thank AiMi Academic Services for English language editing and review services.

COMPLIANCE WITH ETHICS GUIDELINES

Haiyan Gong, Zhengyuan Chen, Yuxin Tang, Minghong Li, Sichen Zhang, Xiaotong Zhang, and Yang Chen declare that they have no conflict of interest.

This article is a review article and does not contain any studies with human or animal subjects performed by any of the authors.

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