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Inferring Diploid 3D Chromatin Structures from Hi-C Data

Authors Alexandra Gesine Cauer, Gürkan Yardımcı, Jean-Philippe Vert, Nelle Varoquaux , William Stafford Noble

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Author Details

Alexandra Gesine Cauer
  • Department of Genome Sciences, University of Washington, Seattle, WA, USA
Gürkan Yardımcı
  • Department of Genome Sciences, University of Washington, Seattle, WA, USA
Jean-Philippe Vert
  • Google Brain, Paris, France
  • Centre for Computational Biology, MINES ParisTech, PSL University Paris, France
Nelle Varoquaux
  • Department of Statistics, UC Berkeley, CA, USA
William Stafford Noble
  • Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

Funding

WSN acknowledges support from the National Institutes of Health Common Fund 4D Nucleome Program (Grant U54 DK107979). NV was supported by a BIDS fellowship from the Gordon and Betty Moore Foundation (Grant GBMF3834) and by the Alfred P. Sloan Foundation (Grant 2013-10-27).

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Alexandra Gesine Cauer, Gürkan Yardımcı, Jean-Philippe Vert, Nelle Varoquaux, and William Stafford Noble. Inferring Diploid 3D Chromatin Structures from Hi-C Data. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 11:1-11:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.WABI.2019.11

Abstract

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.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
Keywords
  • Genome 3D architecture
  • chromatin structure
  • Hi-C
  • 3D modeling

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