TY - JOUR T1 - Bayesian Inference of Spatial Organizations of Chromosomes A1 - Hu, Ming A1 - Deng, Ke A1 - Qin, Zhaohui A1 - Dixon, Jesse A1 - Selvaraj, Siddarth A1 - Fang, Jennifer A1 - Ren, Bing A1 - Liu, Jun S. Y1 - 2013/01/31 N2 - 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. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 9 IS - 1 UR - https://doi.org/10.1371/journal.pcbi.1002893 SP - e1002893 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002893 ER -