Abstract
There is a strong demand for methods that can efficiently reconstruct valid super-resolution intact genome 3D structures from sparse and noise single-cell Hi-C data. Here, we develop Single-Cell Chromosome Conformation Calculator (Si-C) within the Bayesian theory framework and apply this approach to reconstruct intact genome 3D structures from single-cell Hi-C data of eight G1-phase haploid mouse ES cells. The inferred 100-kb and 10-kb structures consistently reproduce the known conserved features of chromatin organization revealed by independent imaging experiments. The analysis of the 10-kb resolution 3D structures reveals cell-to-cell varying domain structures in individual cells and hyperfine structures in domains, such as loops. An average of 0.2 contact reads per divided bin is sufficient for Si-C to obtain reliable structures. The valid super-resolution structures constructed by Si-C demonstrate the potential for visualizing and investigating interactions between all chromatin loci at the genome scale in individual cells.
Highlights
There is a strong demand for methods that can efficiently reconstruct valid super-resolution intact genome 3D structures from sparse and noise single-cell Hi-C data
Structure reconstructions were performed by the NucDynamics and single-cell lattice (SCL) methods based on the same data
Before using the Hi-C data to model 3D structures either by Single-Cell Chromosome Conformation Calculator (Si-C), NucDynamics, or SCL, we removed isolated contacts that are not supported by other contacts between the same two regions of 2 Mb because these contact reads have a high risk of sequence mapping errors
Summary
There is a strong demand for methods that can efficiently reconstruct valid super-resolution intact genome 3D structures from sparse and noise single-cell Hi-C data. Comparing to ShRec3D, MBO allows for flexibility in the determination of 3D chromosome structure from single-cell Hi-C data Another class of popular methods for reconstructing 3D structure from single-cell Hi-C data is known as constraints optimization methods[9,14,16,19,20]. These methods describe chromatin fiber as a polymer consisting of beads of the same size and introduce a cost function taking into account input Hi-C data and polymer-physics properties as constraints. Stevens et al developed NucDynamics[9], which uses a force field with two terms: one representing a general repulsion between all sequentially nonadjacent beads in the chromosome and the other describing the distance restraint between the bead pairs that show a contact in Hi-C data or that are adjacent along the chromosome sequence. Such a practice seems plausible, the variability of the structure ensemble cannot reflect the valid structure error bar, since the ensemble lacks a statistical foundation
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