Computational methods for identifying enhancer-promoter interactions

doi:10.15302/J-QB-022-0322

Quant. Biol.

2023, Vol. 11

Issue (2) : 122-142 https://doi.org/10.15302/J-QB-022-0322

REVIEW

Computational methods for identifying enhancer-promoter interactions

Haiyan Gong¹, Zhengyuan Chen¹, Yuxin Tang¹, Minghong Li¹, Sichen Zhang¹, Xiaotong Zhang^1,³(

), Yang Chen²(

)

¹. School of Computer and Communication Engineering, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing100083, China
². State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
³. Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China

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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.

Keywords enhancer promoter enhancer-promoter interaction machine learning deep learning

Corresponding Author(s): Xiaotong Zhang,Yang Chen

About author:

^* These authors contributed equally to this work.

Just Accepted Date: 01 March 2023 Online First Date: 12 April 2023 Issue Date: 21 June 2023

Cite this article:

Haiyan Gong,Zhengyuan Chen,Yuxin Tang, et al. Computational methods for identifying enhancer-promoter interactions[J]. Quant. Biol., 2023, 11(2): 122-142.

URL:

https://academic.hep.com.cn/qb/EN/10.15302/J-QB-022-0322
https://academic.hep.com.cn/qb/EN/Y2023/V11/I2/122

Fig.1 Mechanisms of transcriptional activation over EPIs.

Fig.2 An overview of the EPI prediction methods using different data sources.

Category	Refs.	Time	Source data	Method	Software name	Citation number
Traditional　machine 　learning-　based	[44]	2011	DNA sequence	SVM (support vector machine) classifier	k-mer-svm	162
	[45]	2012	TF motifs	LASSO regression	CLARE	9
	[46]	2012	ChIP-seq histone methylation and acetylation maps	Genetic algorithm-optimized support vector machine	ChromaGenSVM	77
	[47]	2013	Histone modification ChIP-seq	Random-forest- based	RFECS	144
	[48]	2014	Gapped k-mer features	SVM	gkm-svm	239
	[49]	2014	Histone modifications (ChIP-Seq), TFBSs, chromatin accessibility (DNase-Seq), transcription (RNA-Seq), evolutionary conservation, sequence signatures	Linear SVM and multiple kernel learning	EnhancerFinder	162
	[50]	2015	ChIP-seq	AdaBoost-based	DELTA	31
	[51]	2015	Histone ChIP-seq and DNA sequence	SVM	DEEP	73
	[52]	2016	DNA sequence	Machine learning	iEnhancer-PsedeKNC	15
	[53]	2016	DNA sequence, pseudo k-tuple nucleotide composition	SVM	iEnhancer-2L	334
	[54]	2016	Chromatin state, DNA sequence	A two-step wrapper- based feature selection method	EnhancerPred	52
	[55]	2016	WGBS DNA methylation profiles	Weighted support vector machine learning framework	LMethyR-SVM	9
Traditional　machine 　learning-　based	[56]	2017	Short dinucleotide repeat motifs (DRMs), DNA sequence, enhancer-associated histone modification data	Machine learning	−	22
	[57]	2017	Chromatin state, DNA sequence	A two-step wrapper-based feature selection method	EnhancerPred2.0	29
	[58]	2017	Histone ChIP-seq and methylation, DNA sequence	Random forest	REPTILE	43
	[59]	2018	DNA sequence	SVM	iEnhancer-EL	106
	[60]	2018	FANTOM5 atlas of TrEns	Feature matrix generation, feature ranking using Gini-index, logistic regression	TELS	2
	[61]	2018	DNA sequence	k-mer and machine learning based method	enhancer_prediction	13
	[62]	2020	STARR-seq	Supervised machine-learning	MatchedFilter	21
	[20]	2021	DNA sequence	Feature extraction technique and SVM	piEnPred	6
	[63]	2021	Chromatin state and DNA sequence	Enhanced feature representation using random forest	iEnhancer-RF	8
	[64]	2021	Nucleotide Composition	Two-Layer Predictor, Kullback-Leibler divergence, LASSO, SVM	iEnhancer-KL	1
	[65]	2021	DNA sequence	7-mer and random forest	Computational CRISPR Strategy (CCS)	38
	[66]	2021	DNA sequence	Random forest, extremely randomized tree, multilayer perceptron, SVM and extreme gradient boosting	Enhancer-IF	12
Deep 　learning-　based	[67]	2010	Histone modification ChIP-seq	Time delay neural network (TDNN)	CSI-ANN	160
	[68]	2016	ChIP-Seq, DNase-Seq, RNA-Seq, DNA meth- ylation, and other features	Deep learning-based	PEDLA	91
	[69]	2017	DNA sequence	CNN (convolution neural network)	DeepEnhancer	76
	[70]	2017	DNA sequence	Deep-learning-based	BiRen	91
	[71]	2018	ATAC-Seq	Neural network-based model	PEAS	22
	[72]	2019	DNA sequence	Word embeddings and SVM	iEnhancer-5Step	96
	[73]	2020	DNA sequence	Word Embedding and CNN	iEnhancer-CNN	26
	[74]	2021	DNA sequence and DNase-seq	Deep-learning-based	DeepCAPE	8
	[75]	2021	STARR-seq	Deep-learning-based	DECODE	2
	[76]	2021	DNA sequence	Augmented data and Residual CNN	ES-ARCNN	4
	[77]	2021	Pseudo - K-tuple nucleotide composition and DNA sequence	DNN	iEnhancer-DHF	8
	[78]	2021	DNA sequence	Word embedding, generative adversarial net, CNN	iEnhancer-GAN	8
	[79]	2022	DNA sequence	Neural network	RicENN	1
	[80]	2022	DNA sequence	Enhanced feature extraction strategy, deep learning	−	0
	[81]	2022	DNA sequence	One-hot encoding, convolutional neural network	iEnhancer-Deep	2
	[82]	2022	DNA sequence	DBSCAN, random forest, word2vec and attention-based Bi-LSTM	−	0

Tab.1 Prediction methods of enhancer

Category	Refs.	Time	Source data	Method	Software name	Citation number
Deep　learning-　based	[83]	2012	DNA sequence	DNA sequence features	−	63
	[84]	2016	DNA sequence	Deep feature selection, DFS		200
	[85]	2017	DNA sequence	CNN	CNNProm	169
	[86]	2018	DNA sequence	SVM	BacSVM+	9
	[87]	2018	DNA sequence	DNA sequence features	iPromoter-2L	256
	[88]	2019	DNA sequence	CNN and LSTM	DeePromoter	80
	[89]	2019	DNA sequence	Deep learning and combination of continuous FastText N-Grams	deepPromoter	46
	[90]	2019	DNA sequence	Deep learning	PromID	68
	[91]	2019	DNA sequence	Minimum redundancy maximum relevance (mRMR) algorithm and increment feature selection strategy, SVM	iProEP	99
	[92]	2019	DNA sequence	Combinee smoothing cutting window algorithm, k-mer, SVM	iPromoter-2L2.0	57
	[93]	2019	Bacterial σ70 promoter sequences	Feature subspace based ensemble classifier	iPromoter-FSEn	30
	[94]	2019	Bacterial σ70 promoter sequences	Multiple windowing and minimal features	iPro70-FMWin	20
	[95]	2019	The physicochemical properties of nucleotides and their nucleotide density into pseudo K-tuple nucleotide composition	A two-layer predictor	iPSW(2L)-PseKNC	55
	[96]	2019	DNA sequence	F-score feature selection method	MULTiPly	87
	[97]	2020	DNA sequence of Escherichia coli K-12	Statistical physics model	PhysMPrePro	1
	[98]	2020	DNA sequence of Escherichia coli K-12	CNN	iPromoter-BnCNN	23
	[99]	2020	DNA sequence of Escherichia coli K-12	CNN, pseudo-di-nucleotide composition	PseDNC-DL	32
	[100]	2020	DNA sequence of Escherichia coli K-12	One-hot encoding and CNN	pcPromoter-CNN	17
	[101]	2021	The k-mer nucleotide composition, binary encoding and dinucleotide property matrix-based distance	Extremely randomized trees	iPromoter-ET	5
	[102]	2021	Rice-specific DNA sequence	CNN	Cr-Prom	9
	[103]	2021	DNA sequence of Escherichia coli K-12	A two-layer predictor	iPro2L-PSTKNC	5
	[104]	2021	DNA sequence	CNN	iPTT(2 L)-CNN	2
	[105]	2021	DNA sequence	Cascaded deep capsule neural networks	Depicter	23
	[106]	2022	DNA sequence	k-mers and deep learning network	PPred-PCKSM	1
	[107]	2022	DNA sequence	k-mer word vector, multiple descriptors and feature selection using XGBoost	dPromoter-XGBoost	1
	[108]	2022	DNA sequence	k-mers and LSTM network	−	1
	[109]	2022	DNA sequence	Moran-based spatial auto-cross correlation method and deep convolution generative adversarial network	iPro-GAN	2
	[110]	2022	Promoter data sets from both plants and humans	Synthetic sampling, transfer learning and label smoothing regularization	HMPI	0
	[111]	2022	Promoter sequences from six nannochloropsis strains	Densely connected convolutional neural networks	DenseNet-PredictPromoter	0
Peak 　calling	[112]	2015	Capture Hi-C	−	−	861
Peak 　calling	[113]	2016	Promoter capture Hi-C	−	−	769

Tab.2 Prediction methods of promoter

Fig.3 An overview of the enhancer and promoter identification process.

Tab.3 Available public data repository for enhancer and promoter

Tab.4 The benchmarking enhancer-promoter interaction dataset used in EPIs identification methods

Tab.5 Methods of calling loops from 3C-based data

Fig.4 Dynamic EPI affects gene transcription.

Category	Refs.	Time	Source data	Method	Software name	Citation number
Traditional machine 　learning-based + 　call loops from　Hi-C data	[42]	2014	DNA, histone marks, TFBSs, RNA-seq, ChIA-PET	Random forest	IM-PET	242
	[143]	2016	ChIP-seq, Hi-C	Machine learning-based	TargetFinder	349
	[154]	2019	Hi-C, enhancer, and promoter DNA sequences, ChIP-seq	Data screen, balanced and unbalanced models	EPIP	22
	[188]	2020	ChIP-seq, RNA-seq, Hi-C	Machine-learning-based	3DPredictor	32
Traditional machine　learning-based	[203]	2017	ChIP-Seq	Bayesian classifier	EP_Bayes	8
	[187]	2017	DHS, distance, eRNA, histone marks, ChIA-PET/Hi-C/eQTL	Linear regression	JEME	166
	[183]	2017	5C, FAIRE-seq, ChIP-seq, Cap-analysis gene expression (CAGE), DNA methylation, nucleosome occupancy, eRNAs, chromatin state	Random forest classifier	−	11
	[144]	2017	DNA sequence	Gradient boosting	PEP	67
	[204]	2018	DNA structure properties and transcription factor binding motifs	Machine-learning-based	−	3
	[145]	2018	DNA sequences of arbitrary lengths	Natural language processing and unsupervised deep learning (extract sequence embedding feature), GBDT	EP2vec	56
	[147]	2019	ChIP-seq	Random forest	−	2
	[179]	2020	DNA sequence, ChIP-seq, annotation file	XGBoost-based	XGBoost	11
	[205]	2022	CT-FOCS	Linear mixed effect models	ct-focs	2
Deep-learning-based	[148]	2019	DNA sequence	CNN and a recurrent neural network	EPIsCNN	38
	[146]	2019	DNA sequence	CNN, LSTM	SPEID	94
	[149]	2020	DNA sequence	Dna2vec, deep-learning-based	EPIVAN	100
	[155]	2020	Hi-C, ChromHMM of Roadmap Epigenomics	CNN, transfer learning	SEPT	14
	[151]	2021	DNA sequence	CNN, bidirectional gated recurrent unit network and matching heuristic mechanism	EPI-DLMH	18
	[152]	2021	Hi-C, DNA sequence,	Hilbert curve encoding, transfer learning	EPIsHilbert	2
	[184]	2022	Hi-C, ChIA-PET	Transformer-based model	TransEPI	1
	[153]	2022	DNA sequence	Dna2vec, transfer learning	EPI-Mind	0
	[185]	2022	−	A web server for prediction EPI	EPIXplorer	0

Tab.6 Prediction methods of enhancer-promoter interaction (EPI)

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