深度学习在基因组学中的研究进展

doi:10.16288/j.yczz.24-151

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Hereditas(Beijing) ›› 2024, Vol. 46 ›› Issue (9): 701-715.doi: 10.16288/j.yczz.24-151

• Review • Previous Articles Next Articles

Progress on deep learning in genomics

Yanchun Bao¹^,²(), Caixia Shi¹, Chuanqiang Zhang³^,⁴, Mingjuan Gu¹, Lin Zhu¹, Zaixia Liu¹^,², Le Zhou¹^,², Fengying Ma¹^,², Risu Na¹(), Wenguang Zhang⁵()

1. College of Animal Science and Technology, Inner Mongolia Agricultural University, Hohhot 010018, China
2. Inner Mongolia Engineering Research Center of Genomic Big Data for Agriculture, Hohhot 010018, China
3. Inner Mongolia Saikexing Institute of Breeding and Reproductive Biotechnology in Domestic Animal, Hohhot 011517, China
4. National Center of Technology Innovation for Dairy Industry, Hohhot 010080, China
5. College of Life Sciences, Inner Mongolia Agricultural University, Hohhot 010021, China

Received:2024-05-27 Revised:2024-08-09 Online:2024-09-20 Published:2024-08-16
Contact: Risu Na, Wenguang Zhang E-mail:byc107054@163.com;narisu@swu.edu.cn;atcgnmbi@aliyun.com
Supported by:
Natural Science Foundation of Inner Mongolia Autonomous Region(2021ZD05);Sub-theme of the National Dairy Technology Innovation Center Project(2023-JSGG-2);Basic Research Expenses of Universities Directly of Inner Mongolia Autonomous Region(BR221024);Basic Research Expenses of Universities Directly of Inner Mongolia Autonomous Region(BR221113);Funding for Double First-class Construction of Inner Mongolia Agricultural University(BZX202201);Funding for Double First-class Construction of Inner Mongolia Agricultural University(QF202206);Funding for Double First-class Construction of Inner Mongolia Agricultural University(NDYB2022-1)

Abstract

Abstract:

With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning—an advanced artificial intelligence technology—offers powerful capabilities for data analysis and pattern recognition, revitalizing genomic research. In this review, we focus on four major deep learning models: Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), and Generative Adversarial Network(GAN). We outline their core principles and provide a comprehensive review of their applications in DNA, RNA, and protein research over the past five years. Additionally, we also explore the use of deep learning in livestock genomics, highlighting its potential benefits and challenges in genetic trait analysis, disease prevention, and genetic enhancement. By delivering a thorough analysis, we aim to enhance precision and efficiency in genomic research through deep learning and offer a framework for developing and applying livestock genomic strategies, thereby advancing precision livestock farming and genetic breeding technologies.

Key words: deep learning, genome, CNN, RNN, LSTM, GAN

Yanchun Bao, Caixia Shi, Chuanqiang Zhang, Mingjuan Gu, Lin Zhu, Zaixia Liu, Le Zhou, Fengying Ma, Risu Na, Wenguang Zhang. Progress on deep learning in genomics[J]. Hereditas(Beijing), 2024, 46(9): 701-715.

Figures/Tables 4

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Table 1

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领域	应用	发表年份	名称	模型	参考文献
DNA	增强子	2019	Enhancer-CRNN	CNN+RNN	[46]
		2021	BERT-Enhancer	BERT+CNN	[33]
		2022	Enhancer-LSTMAtt	LSTM	[54]
	启动子	2019	DeePromoter	CNN+LSTM	[55]
		2021		CNN	[34]
		2022	iPro-GAN	GAN	[71]
	表观修饰	2019	DeepMod	LSTM+RNN	[56]
		2020	MR-GAN	GAN	[72]
		2022	EDRNN	EDRNN	[47]
		2023	RcWGBS	CNN	[35]
	相互作用	2019	EnContact	RNN	[48]
	相互作用	2022	EPIHC	CNN	[36]
RNA	选择性剪接	2020		RNN	[49]
	选择性剪接	2021		LSTM	[57]
	非编码RNA	2021	miTAR	CNN+RNN	[50]
		2022	RLF-LPI	LSTM	[61]
		2023	CRBSP	LSTM	[60]
		2023		LSTM	[58]
		2023	NCYPred	LSTM	[59]
		2023	LDAF_GAN	GAN	[73]
		2023	GAMCNMDF	GAN	[74]
		2023		GAN	[75]
	表达	2022	Multi-LSTM	LSTM	[62]
		2022	T-GEM	CNN	[37]
		2024	IP3G	GAN	[76]
蛋白质	转录因子	2021	DeepD2V	Bi-LSTM+CNN	[63]
	转录因子	2023		GAN	[77]
	结构	2019	DeepDom	LSTM	[65]
		2022	EMBER2	CNN	[38]
		2022	CRNN	RNN	[51]
	功能	2021	Deep_CNN_LSTM_GO	CNN	[64]
		2021	PFP-WGAN	CNN+LSTM	[78]
		2021	DeepGOPlus	GAN	[39]
	DNA结合蛋白	2019	DeepSite	CNN+LSTM	[66]
	DNA结合蛋白	2022	DeepA-RBPBS	CNN+BiGRU	[40]
	RNA结合蛋白	2023	WVDL	CNN+LSTM+ResNet	[67]
	序列	2024	ECPN-HFGF	ResNet	[68]

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