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

Abstract

Background 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. Results We 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. Conclusions HiCKey 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.

Nucleome programming is required for the foundation of totipotency in mammalian germline development

“…The degree of TAD boundary conservation was evaluated using a permutation test, where the number of boundaries being shared across cell types was compared against a background derived from merging the list of boundaries and shuffling cell type labels. Additionally, 9 other TAD identification algorithms (Levy‐Leduc et al , 2014; Rao et al , 2014; Shin et al , 2016; preprint: Dali et al , 2018; An et al , 2019; Cresswell et al , 2020; Matthey‐Doret et al , 2020; Soler‐Vila et al , 2020; Xing et al , 2021) were used with default parameters to validate trends observed with insulation scores, all at 50 kb resolution.…”

A comparison of topologically associating domain callers over mammals at high resolution

“…Xing et al [ 84 ] developed a statistical model of TADs called HiCKey, which identifies TADs in two main steps: (1) initially, the method proposes a generalized likelihood-ratio test to detect transition points in given interaction matrix which can follow general mixture or negative binomial distribution, (2) the method runs a number of search strategies to infer hiearchical TADs with their associated p values calculated by the generalized likelihood-ratio test. HiCKey can use an Hi-C matrix as input data and has three key parameters: lower bound of TAD size, threshold to test whether a boundary (change-point) is significant, threshold for identifying nested TADs.…”

Single-cell multi-omics enabled discovery of alkaloid biosynthetic pathway genes in the medical plant Catharanthus roseus

“…HiCKey 21 was used to detect topologically associated domains (TADs) and HiCCUPS to detect chromosome loops 22 , revealing distinct chromosomal organizations associated with biosynthetic gene clusters. For example, STR and TDC are physically clustered on chromosome 3 (Fig.…”