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


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.

SUBMITTER: Li A 

PROVIDER: S-EPMC6093941 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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

Li Angsheng A   Yin Xianchen X   Xu Bingxiang B   Wang Danyang D   Han Jimin J   Wei Yi Y   Deng Yun Y   Xiong Ying Y   Zhang Zhihua Z  

Nature communications 20180815 1


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 mini  ...[more]

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