Computational methods for identifying enhancer-promoter interactions

Haiyan Gong, Zhengyuan Chen, Yuxin Tang, Minghong Li, Sichen Zhang, Xiaotong Zhang, Yang Chen

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Quant. Biol. ›› 2023, Vol. 11 ›› Issue (2) : 122-142. DOI: 10.15302/J-QB-022-0322
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Computational methods for identifying enhancer-promoter interactions

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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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Haiyan Gong, Zhengyuan Chen, Yuxin Tang, Minghong Li, Sichen Zhang, Xiaotong Zhang, Yang Chen. Computational methods for identifying enhancer-promoter interactions. Quant. Biol., 2023, 11(2): 122‒142 https://doi.org/10.15302/J-QB-022-0322

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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.

OPEN ACCESS

This article is licensed by the CC By under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2023 The Author(s). Published by Higher Education Press.
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