Bayesian Inference of Spatial Organizations of Chromosomes

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.

Author Summary

Understanding how chromosomes fold provides insights into the complex relationship among chromatin structure, gene activity and the functional state of the cell. Recently, chromosome conformation capture based technologies, such as Hi-C and TCC, have been developed to provide a genome-wide, high resolution and three-dimensional (3D) view of chromatin organization. However, statistical methods for analyzing these data are still under development. Here we propose two Bayesian methods, BACH to infer the consensus 3D chromosomal structure and BACH-MIX to reveal structural variations of chromatin in a cell population. Applying BACH and BACH-MIX to a high resolution Hi-C dataset, we found that most local genomic regions exhibit homogeneous 3D chromosomal structures. Furthermore, spatial properties of 3D chromosomal structures and structural variations of chromatin are associated with several genomic and epigenetic features. Noticeably, gene rich, accessible and early replicated genomic regions tend to be more elongated and exhibit higher structural variations than gene poor, inaccessible and late replicated genomic regions.