Abstract
Chromatin interactions are important for gene regulation and cellular specialization. There is emerging evidence that many-body (≥3) chromatin interactions play important roles such as condensing super-enhancer regions into a cohesive transcriptional apparatus. Chromosome conformation capture such as Hi-C techniques are limited to measuring pairwise and population-averaged interactions, therefore are not suitable for direct measurements of many-body interactions. We describe a computational method that reconstructs 3D ensemble (4.0×105 chromatin chains). Using just 14-35 predicted driver interactions for regions of ∼480 Kb -1.94 Mb, these ensembles exhibit excellent correlations with Hi-C measurements (r = 0.960, SEM pm 0.003). These ensembles allow identification of significant many-body interactions from Hi-C data. For a global set of highly active transcriptional loci, with topologically-associated domains bounding at least 2 super-enhancers, we reconstructed the landscape of many-body interactions. Furthermore, we show that a predictor based on machine learning identifies key biological factors that can predict anchoring sites of specific many-body interactions.
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