Nucleosome eviction and multiple co-factor binding predict estrogen-receptor-alpha-associated long-range interactions.
Many enhancers regulate their target genes via long-distance interactions. High-throughput experiments like ChIA-PET have been developed to map such largely cell-type-specific interactions between cis-regulatory elements genome-widely. In this study, we integrated multiple types of data in order to reveal the general hidden patterns embedded in the ChIA-PET data. We found characteristic distance features related to promoter-promoter, enhancer-enhancer and insulator-insulator interactions. Although a protein may have many binding sites along the genome, our hypothesis is that those sites that share certain open chromatin structure can accommodate relatively larger protein complex consisting of specific regulatory and 'bridging' factors, and may be more likely to form robust long-range deoxyribonucleic acid (DNA) loops. This hypothesis was validated in the estrogen receptor alpha (ERα) ChIA-PET data. An efficient classifier was built to predict ERα-associated long-range interactions solely from the related ChIP-seq data, hence linking distal ERα-dependent enhancers to their target genes. We further applied the classifier to generate additional novel interactions, which were undetected in the original ChIA-PET paper but were validated by other independent experiments. Our work provides a new insight into the long-range chromatin interactions through deeper and integrative ChIA-PET data analysis and demonstrates DNA looping predictability from ordinary ChIP-seq data.
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.
Chao
C
He
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China michael.zhang@utdallas.edu.
Xiaowo
X
Wang
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas, Dallas 800 West Campbell Road, RL11 Richardson, TX 75080-3021, USA michael.zhang@utdallas.edu.
Michael Q
MQ
Zhang
D000465:Algorithms; D001665:Binding Sites; D002843:Chromatin / Q000737:chemistry; D047369:Chromatin Immunoprecipitation; D004247:DNA / Q000737:chemistry; D004742:Enhancer Elements, Genetic; D047628:Estrogen Receptor alpha / Q000378:metabolism*; D015870:Gene Expression; D006657:Histones / Q000378:metabolism; D006801:Humans; D061986:MCF-7 Cells; D009707:Nucleosomes / Q000378:metabolism*; D011401:Promoter Regions, Genetic; D014157:Transcription Factors / Q000378:metabolism
D002843:Chromatin; D047628:Estrogen Receptor alpha; D006657:Histones; D009707:Nucleosomes; D014157:Transcription Factors; D004247:DNA