Inferring diploid 3D chromatin structures from bulk and single-cell Hi-Cdata

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Project Summary/Abstract The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and the cell cycle. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion. Inferring from such data accurate 3D models of how chromosomes fold can yield insights that are hidden in the raw data. Many methods exist to infer the 3D structures of haploid genomes, but diploid genomes pose a much more challenging problem because Hi-C data does not inherently distinguish between the alleles. Additionally, while single-cell experiments have made clear that chromatin structure exhibits a great deal of heterogeneity within a population, the sparsity of single-cell Hi-C data poses additional difficulties for inference. We have recently published a method to infer 3D diploid genomes by building upon a probabilistic framework we previously developed for haploid data. We propose to apply this method to model diploid yeast genomes in order to further characterize mitotic homolog pairing in yeast. We also propose to extend this method to work with single-cell data, and to validate and integrate our method with microscopy of chromatin sites and nuclear proteins. We will thereby provide an integrated 3D model of high-resolution imaging and DNA sequence.