Abstract
Knowledge of spatial chromosomal organizations is critical for the study of transcriptional regulation and other nuclear processes in the cell. Recently, chromosome conformation capture (3C) based technologies, such as Hi-C and TCC, have been developed to provide a genome-wide, three-dimensional (3D) view of chromatin organization. Appropriate methods for analyzing these data and fully characterizing the 3D chromosomal structure and its structural variations are still under development. Here we describe a novel Bayesian probabilistic approach, denoted as “Bayesian 3D constructor for Hi-C data” (BACH), to infer the consensus 3D chromosomal structure. In addition, we describe a variant algorithm BACH-MIX to study the structural variations of chromatin in a cell population. Applying BACH and BACH-MIX to a high resolution Hi-C dataset generated from mouse embryonic stem cells, we found that most local genomic regions exhibit homogeneous 3D chromosomal structures. We further constructed a model for the spatial arrangement of chromatin, which reveals structural properties associated with euchromatic and heterochromatic regions in the genome. We observed strong associations between structural properties and several genomic and epigenetic features of the chromosome. Using BACH-MIX, we further found that the structural variations of chromatin are correlated with these genomic and epigenetic features. Our results demonstrate that BACH and BACH-MIX have the potential to provide new insights into the chromosomal architecture of mammalian cells.
Highlights
The spatial organization of a genome plays an important role in gene regulation, DNA replication, epigenetic modification and maintenance of genome stability [1,2,3,4,5]
Our results demonstrate that Bayesian 3D constructor for Hi-C data’’ (BACH) and BACH-MIX have the potential to provide new insights into the chromosomal architecture of mammalian cells
Compared to other published methods, BACH has the following advantages: (1) It explicitly models and corrects known systematic biases associated with Hi-C data, such as restriction enzyme cutting frequencies, GC content and sequence uniqueness [17,20]; (2) It utilizes a Poisson model that better fits the count data generated from Hi-C experiments than the Gaussian model used in MCMC5C, and performs more robustly when applied to several experimental datasets; (3) It employs advanced Markov chain Monte Carlo (MCMC) techniques, such as Sequential Monte Carlo and Hybrid Monte Carlo, that significantly improve the efficiency in exploring the vast space of possible models [21]
Summary
The spatial organization of a genome plays an important role in gene regulation, DNA replication, epigenetic modification and maintenance of genome stability [1,2,3,4,5]. The 3D organization of chromosomes has been studied by microscopic and cytogenic methods such as florescent in situ hybridization (FISH). Several FISH studies have shown that the global 3D chromosomal structures are highly dynamic [6,7,8]. Due to the limitation of low throughput, low resolution FISH data, the 3D chromosomal structures at the fine scale are not fully understood. Higher throughput, higher resolution approaches based on chromosome conformation capture (3C) such as Hi-C [9] and TCC [10] allow genome-wide mapping of chromatin interactions. The chromatin interactions captured by Hi-C and TCC experiments, which are represented by the contact matrix in the original Hi-C study [9], provide an unprecedented opportunity for inferring 3D chromosomal structures at the fine resolution scale
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