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Nucleic Acids Res. 2015 Jun 23;43(11):5331-9. doi: 10.1093/nar/gkv424. Epub 2015 May 4.

Accurate identification of centromere locations in yeast genomes using Hi-C.

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Mines ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, 35 rue St Honoré 77300 Fontainebleau, France Institut Curie, Paris, F-75248, France U900, INSERM, Paris, F-75248, France.
Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, USA.
Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, USA Department of Computer Science and Engineering, University of Washington, 185 Stevens Way, Seattle, WA 98195, USA


Centromeres are essential for proper chromosome segregation. Despite extensive research, centromere locations in yeast genomes remain difficult to infer, and in most species they are still unknown. Recently, the chromatin conformation capture assay, Hi-C, has been re-purposed for diverse applications, including de novo genome assembly, deconvolution of metagenomic samples and inference of centromere locations. We describe a method, Centurion, that jointly infers the locations of all centromeres in a single genome from Hi-C data by exploiting the centromeres' tendency to cluster in three-dimensional space. We first demonstrate the accuracy of Centurion in identifying known centromere locations from high coverage Hi-C data of budding yeast and a human malaria parasite. We then use Centurion to infer centromere locations in 14 yeast species. Across all microbes that we consider, Centurion predicts 89% of centromeres within 5 kb of their known locations. We also demonstrate the robustness of the approach in datasets with low sequencing depth. Finally, we predict centromere coordinates for six yeast species that currently lack centromere annotations. These results show that Centurion can be used for centromere identification for diverse species of yeast and possibly other microorganisms.

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