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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. 相似文献
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