Alexandra Gesine Cauer, Gürkan Yardımcı, Jean-Philippe Vert, Nelle Varoquaux, William Stafford Noble. 2019. bioRxiv
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 accuarcy 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.
| Chromosome | Counts | Metadata | Counts heatmaps |
|---|---|---|---|
| X | Ambiguous Partially ambiguous Unambiguous | Metadata | Ambiguous Partially ambiguous Unambiguous |
| 3 | Ambiguous Partially ambiguous Unambiguous | Metadata | Ambiguous Partially ambiguous Unambiguous |
