Online Program

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Thursday, October 3
Thu, Oct 3, 2:45 PM - 3:47 PM
Speed Session 1

Inferring Diploid 3D Chromatin Structures from Hi-C Data (306566)

*Gesine Alexandra Cauer, University of Washington 
William Stafford Noble, University of Washington 
Nelle Varoquaux, University of California, Berkeley 
Jean-Philippe Vert, Google Brain 
Gurkan Galip Yardimci, University of Washington 

Keywords: chromatin, genomics, bioinformatics, biology, optimization

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. Inferring accurate 3D models of how chromosomes fold from such data can yield insights that are 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. While 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, as Hi-C data does not distinguish between homologous chromosomes. We propose here a novel method to infer 3D diploid genomes from Hi-C data.