Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy
Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy
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DOI：
10.1038/s41467-018-05691-7
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发表时间：
2018
影响因子：
16.6
通讯作者：
Zhihua Zhang
中科院分区：
文献类型：
--
作者：
Angsheng Li;Xianchen Yin;Bingxiang Xu;Danyang Wang;Jimin Han;Yi Wei;Yun Deng;Ying Xiong;Zhihua Zhang
Submegabase-size topologically associating domains (TAD) have been observed in highthroughput.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.
