Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy

Angsheng Li (), Xianchen Yin, Bingxiang Xu, Danyang Wang, Jimin Han, Yi Wei, Yun Deng, Ying Xiong and Zhihua Zhang ()
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Angsheng Li: Beihang University
Xianchen Yin: Chinese Academy of Sciences
Bingxiang Xu: Chinese Academy of Sciences
Danyang Wang: Chinese Academy of Sciences
Jimin Han: University of Chinese Academy of Sciences
Yi Wei: University of Chinese Academy of Sciences
Yun Deng: University of Chinese Academy of Sciences
Ying Xiong: University of Chinese Academy of Sciences
Zhihua Zhang: Chinese Academy of Sciences

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract Submegabase-size topologically associating domains (TAD) have been observed in high-throughput chromatin interaction data (Hi-C). However, accurate detection of TADs depends on ultra-deep sequencing and sophisticated normalization procedures. Here we propose a fast and normalization-free method to decode the domains of chromosomes (deDoc) that utilizes structural information theory. By treating Hi-C contact matrix as a representation of a graph, deDoc partitions the graph into segments with minimal structural entropy. We show that structural entropy can also be used to determine the proper bin size of the Hi-C data. By applying deDoc to pooled Hi-C data from 10 single cells, we detect megabase-size TAD-like domains. This result implies that the modular structure of the genome spatial organization may be fundamental to even a small cohort of single cells. Our algorithms may facilitate systematic investigations of chromosomal domains on a larger scale than hitherto have been possible.

Date: 2018
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DOI: 10.1038/s41467-018-05691-7

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