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Significance High-throughput chromosome conformation capture (5C, Hi-C) experiments deliver fascinating insights into 3D genome organization. They are typically performed on a large population of cells and therefore yield only average numbers of genomic contacts. Nevertheless, population Hi-C data are often interpreted in terms of a single genomic structure, which ignores all cell-to-cell variability. We propose a probabilistic, statistically rigorous method to infer chromatin structure ensembles from population Hi-C and similar data that takes the ensemble nature of the data explicitly into account and allows us to infer the number of structures required to explain the data.

Bayesian inference of chromatin structure ensembles from population-averaged contact data