An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning

doi:10.1109/TCBB.2019.2901789

	An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning
	Jing, Fang 3; Zhang, Shao-Wu 3; Cao, Zhen 1,2,4; Zhang, Shihua 1,2,4
刊名	IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
	2021
卷号	18 期号:1 页码:355-364
关键词	Bioinformatics machine learning transcription factors binding sites convolutional neural networks DNA accessibility histone modification
ISSN号	1545-5963
DOI	10.1109/TCBB.2019.2901789
英文摘要	Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary DNA sequence. In addition to DNA sequences, histone modifications and chromatin accessibility are also important factors influencing their activity. They have been explored to predict TFBSs recently. However, current methods rarely take into account histone modifications and chromatin accessibility using CNN in an integrative framework. To this end, we developed a general CNN model to integrate these data for predicting TFBSs. We systematically benchmarked a series of architecture variants by changing network structure in terms of width and depth, and explored the effects of sample length at flanking regions. We evaluated the performance of the three types of data and their combinations using 256 ChIP-seq experiments and also compared it with competing machine learning methods. We find that contributions from these three types of data are complementary to each other. Moreover, the integrative CNN framework is superior to traditional machine learning methods with significant improvements.
资助项目	National Natural Science Foundation of China[61873202] ; National Natural Science Foundation of China[61473232] ; National Natural Science Foundation of China[11661141019] ; National Natural Science Foundation of China[61621003] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040600] ; National Ten Thousand Talent Program for Young Top-notch Talents ; Key Research Program of the Chinese Academy of Sciences[KFZD-SW-219] ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSW-SYS008]
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematics
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000615042600034
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/58140]
专题	中国科学院数学与系统科学研究院
通讯作者	Zhang, Shao-Wu; Zhang, Shihua
作者单位	1.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, NCMIS,CEMS, Beijing 100190, Peoples R China 3.Northwestern Polytech Univ, Sch Automat, Key Lab Informat Fus Technol, Minist Educ, Xian 710072, Shaanxi, Peoples R China 4.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Jing, Fang,Zhang, Shao-Wu,Cao, Zhen,et al. An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2021,18(1):355-364.
APA	Jing, Fang,Zhang, Shao-Wu,Cao, Zhen,&Zhang, Shihua.(2021).An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,18(1),355-364.
MLA	Jing, Fang,et al."An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 18.1(2021):355-364.