Deciphering hierarchical organization of topologically associated domains through change-point testing

Haipeng Xing,Yong Chen,Yingru Wu,Michael Q Zhang

doi:10.1186/s12859-021-04113-8

Abstract

BackgroundThe nucleus of eukaryotic cells spatially packages chromosomes into a hierarchical and distinct segregation that plays critical roles in maintaining transcription regulation. High-throughput methods of chromosome conformation capture, such as Hi-C, have revealed topologically associating domains (TADs) that are defined by biased chromatin interactions within them.ResultsWe introduce a novel method, HiCKey, to decipher hierarchical TAD structures in Hi-C data and compare them across samples. We first derive a generalized likelihood-ratio (GLR) test for detecting change-points in an interaction matrix that follows a negative binomial distribution or general mixture distribution. We then employ several optimal search strategies to decipher hierarchical TADs with p values calculated by the GLR test. Large-scale validations of simulation data show that HiCKey has good precision in recalling known TADs and is robust against random collisions of chromatin interactions. By applying HiCKey to Hi-C data of seven human cell lines, we identified multiple layers of TAD organization among them, but the vast majority had no more than four layers. In particular, we found that TAD boundaries are significantly enriched in active chromosomal regions compared to repressed regions.ConclusionsHiCKey is optimized for processing large matrices constructed from high-resolution Hi-C experiments. The method and theoretical result of the GLR test provide a general framework for significance testing of similar experimental chromatin interaction data that may not fully follow negative binomial distributions but rather more general mixture distributions.

Highlights

The nucleus of eukaryotic cells spatially packages chromosomes into a hierarchical and distinct segregation that plays critical roles in maintaining transcription regulation
Performance of HiCKey in detecting Topologically associating domain (TAD) To understand the performance of HiCKey in detecting TADs, we first tested it on largescale simulated Hi-C matrices [46], which included two types of data, Sim1 without nested TAD structures and Sim2 with nested TAD structures
We found that HiCKey produced a very accurate average number of TADs at the 4% noise level ( K − K = − 0.2 )

Summary

Introduction

The nucleus of eukaryotic cells spatially packages chromosomes into a hierarchical and distinct segregation that plays critical roles in maintaining transcription regulation. High-throughput methods of chromosome conformation capture, such as Hi-C, have revealed topologically associating domains (TADs) that are defined by biased chromatin interactions within them. Estimating the 3D organization of chromosomes can provide important insight into the role of high-order chromatin compaction in gene regulation and the way disordered chromatin interactions lead to diseases. Chromosome conformation capture (3C) [8] and its high-throughput derivatives, such as ChIA-PET [9], HiChIP [10] and Hi-C [11, 12], have granted researchers comprehensive information on chromatin interactions and hierarchical chromosomal organizations, including active or repressive compartments (A/B compartments) [13], topologically associated domains (TADs) [11, 14, 15], CTCF protein-mediated loops [9] and enhancer-promoter interactions [4]. Compared with ChIA-PET and other capture-based methods, Hi-C provides high-resolution unbiased signals of chromatin interactions [12]

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