Bayesian Inference of Spatial Organizations of Chromosomes

• Type of Material: Article
• Authors: Ming Hu, Harvard University; Ke Deng, Harvard University; Zhaohui Qin, Emory University; Jesse Dixon, Ludwig Institute for Cancer Research; Siddarth Selvaraj, Ludwig Institute for Cancer Research; Jennifer Fang, Ludwig Institute for Cancer Research; Bing Ren, Ludwig Institute for Cancer Research; Jun S. Liu, Harvard University
• Language: English
• Date: 2013-01-01
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2013 Hu et al.
• License: Creative Commons Attribution 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pcbi.1002893
• Title of Journal or Parent Work: PLoS Computational Biology
• ISSN: 1553-734X
• Volume: 9
• Issue: 1
• Start Page: e1002893
• End Page: e1002893
• Grant/Funding Information: This work was supported by the Ludwig Institute for Cancer Research (BR), US National Institutes of Health grants R01HG005119 (ZQ), R01HG003991 (BR) and 5R01GM080625 (JSL).
• Keywords: Science & Technology; MOUSE GENOME; MONTE-CARLO; MATHEMATICAL & COMPUTATIONAL BIOLOGY; EXPRESSION; Biochemical Research Methods; INTERPHASE CHROMOSOMES; YEAST GENOME; Biochemistry & Molecular Biology; Mathematical & Computational Biology; BIOCHEMICAL RESEARCH METHODS; GENOME ORGANIZATION; CHROMATIN-STRUCTURE; GENE-REGULATION; Life Sciences & Biomedicine; CELLS; ARCHITECTURE
• Research Categories: Biology, Cell; Biology, Bioinformatics

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.