Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture

Abstract

Hi-C experiments measure the probability of physical proximity between pairs of chromosomal loci on a genomic scale. We report on several systematic biases that substantially affect the Hi-C experimental procedure, including the distance between restriction sites, the GC content of trimmed ligation junctions and sequence uniqueness. To address these biases, we introduce an integrated probabilistic background model and develop algorithms to estimate its parameters and renormalize Hi-C data. Analysis of corrected human lymphoblast contact maps provides genome-wide evidence for interchromosomal aggregation of active chromatin marks, including DNase-hypersensitive sites and transcriptionally active foci. We observe extensive long-range (up to 400 kb) cis interactions at active promoters and derive asymmetric contact profiles next to transcription start sites and CTCF binding sites. Clusters of interacting chromosomal domains suggest physical separation of centromere-proximal and centromere-distal regions. These results provide a computational basis for the inference of chromosomal architectures from Hi-C experiments.

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Figure 1: Sources of Hi-C biases.
Figure 2: Model performance.
Figure 3: Chromosomal architecture around active chromatin.
Figure 4: Chromosomal architecture around CTCF binding sites.
Figure 5: Contact map clustering.

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Acknowledgements

We thank W. de Laat for discussions and members of the Tanay group for critical reading of the manuscript. Research at A.T.'s laboratory was supported by the Israeli Science Foundation and by the EPIGENESYS FP7 program of the European Commission.

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E.Y. and A.T. conceived and performed the analysis. E.Y and A.T wrote the article.

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Correspondence to Amos Tanay.

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The authors declare no competing financial interests.

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Supplementary Figures 1–8 and Supplementary Table 1 (PDF 2719 kb)

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Yaffe, E., Tanay, A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat Genet 43, 1059–1065 (2011). https://doi.org/10.1038/ng.947

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