The cis-trans binding strength defined by motif frequencies facilitates statistical inference of transcriptional regulation

doi:10.1186/s12859-019-2732-6

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	The cis-trans binding strength defined by motif frequencies facilitates statistical inference of transcriptional regulation
	Feng, Yance 1,2; Zhang, Sheng 1,2; Li, Liang 1,2; Li, Lei M.1,2,3
	2019-05-01
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	20 页码:11
摘要	BackgroundA key problem in systems biology is the determination of the regulatory mechanism corresponding to a phenotype. An empirical approach in this regard is to compare the expression profiles of cells under two conditions or tissues from two phenotypes and to unravel the underlying transcriptional regulation. We have proposed the method BASE to statistically infer the effective regulatory factors that are responsible for the gene expression differentiation with the help from the binding data between factors and genes. Usually the protein-DNA binding data are obtained by ChIP-seq experiments, which could be costly and are condition-specific.ResultsHere we report a definition of binding strength based on a probability model. Using this condition-free definition, the BASE method needs only the frequencies of cis-motifs in regulatory regions, thereby the inferences can be carried out in silico. The directional regulation can be inferred by considering down- and up-regulation separately. We showed the effectiveness of the approach by one case study. In the study of the effects of polyunsaturated fatty acids (PUFA), namely, docosahexaenoic (DHA) and eicosapentaenoic (EPA) diets on mouse small intestine cells, the inferences of regulations are consistent with those reported in the literature, including PPAR and NFB, respectively corresponding to enhanced adipogenesis and reduced inflammation. Moreover, we discovered enhanced RORA regulation of circadian rhythm, and reduced ETS1 regulation of angiogenesis.ConclusionsWith the probabilistic definition of cis-trans binding affinity, the BASE method could obtain the significances of TF regulation changes corresponding to a gene expression differentiation profile between treatment and control samples. The landscape of the inferred cis-trans regulations is helpful for revealing the underlying molecular mechanisms. Particularly we reported a more comprehensive regulation induced by EPA&DHA diet.
关键词	BASE Statistical inference Transcriptional regulation PUFA DHA EPA Binding strength
DOI	10.1186/s12859-019-2732-6
语种	英语
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDB13040600] ; National Natural Science Foundation of China[11871462] ; National Natural Science Foundation of China[91530105] ; National Natural Science Foundation of China[91130008] ; National Center for Mathematics and Interdisciplinary Sciences of the CAS ; Key Laboratory of Systems and Control of the CAS ; National Key Research and Development Program of China[2017YFC0908400]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS记录号	WOS:000467203000004
出版者	BMC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/33268
专题	系统科学研究所
通讯作者	Li, Lei M.
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
推荐引用方式 GB/T 7714	Feng, Yance,Zhang, Sheng,Li, Liang,et al. The cis-trans binding strength defined by motif frequencies facilitates statistical inference of transcriptional regulation[J]. BMC BIOINFORMATICS,2019,20:11.
APA	Feng, Yance,Zhang, Sheng,Li, Liang,&Li, Lei M..(2019).The cis-trans binding strength defined by motif frequencies facilitates statistical inference of transcriptional regulation.BMC BIOINFORMATICS,20,11.
MLA	Feng, Yance,et al."The cis-trans binding strength defined by motif frequencies facilitates statistical inference of transcriptional regulation".BMC BIOINFORMATICS 20(2019):11.