Inferring Diploid 3D Chromatin Structures from Hi-C Data

The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes. We propose a method to infer 3D diploid genomes from Hi-C data. We demonstrate the accuracy of the method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the active homolog exhibits a bipartite structure, whereas the active homolog does not

Inferring whole-genome 3D chromatin structures from diploid Hi-C data

Thesis (Ph.D.)--University of Washington, 2023The three-dimensional organization of the genome plays an important part in regulating numerous basic cellular functions, including gene regulation, differentiation, the cell cycle, DNA replication, and DNA repair. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Accordingly, many methods have been developed to infer 3D structures from Hi-C data. However, many challenges remain. Importantly, although many methods exist to infer the 3D structure of haploid genomes, accurately inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data does not typically distinguish between homologous chromosomes. Inference is also complicated in the setting of low-coverage or high-resolution data, which can lead to poor performance and high computational costs. This work describes two methods for inferring 3D diploid chromatin structures from Hi-C data. The first approach extends a previously published haploid method and enables diploid inference via the addition of two constraints. We demonstrate the accuracy of this method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the inactive homolog exhibits a bipartite structure, whereas the active homolog does not. Our second method addresses the difficulties presented by low-coverage or high-resolution data via multiscale optimization, an optimization strategy that solves a large optimization problem by building upon the solutions to smaller versions of the problem. Similar approaches have been successfully employed in the context of haploid structural inference methods. However, because many organisms of interest are diploid, we sought to develop a multiscale optimization approach that infers the structure of diploid genomes. We use simulations to show that integrating multiscale optimization into our first method significantly improves the accuracy of inferred structures