TY - JOUR
T1 - Population-based 3D genome structure analysis reveals driving forces in spatial genome organization
AU - Tjong, Harianto
AU - Li, Wenyuan
AU - Kalhor, Reza
AU - Dai, Chao
AU - Hao, Shengli
AU - Gong, Ke
AU - Zhou, Yonggang
AU - Li, Haochen
AU - Zhou, Xianghong Jasmine
AU - Le Gros, Mark A.
AU - Larabell, Carolyn A.
AU - Chen, Lin
AU - Alber, Frank
N1 - Funding Information:
ACKNOWLEDGMENTS: We thank Dr. Quan Chen for inspiring discussions about the methods formulations and Nan Hua and Qingjiao Li for helpful discussions to improve the manuscript. The authors wish to acknowledge the anonymous reviewers for their helpful comments on the manuscript. The work was supported by the Arnold and Mabel Beckman Foundation (BYI Program) (F.A.), NIH Grants R01GM096089 (to F.A.), 5R01 AI113009 (to L.C.), and U54DK107981-01 (to F.A., L.C., and X.J.Z.), National Heart, Lung, and Blood Institute MAP-GEN Grant U01HL108634 (to X.J.Z.), and NSF CAREER Grant 1150287 (to F.A.). F.A. is a Pew Scholar in Biomedical Sciences, supported by the Pew Charitable Trusts. The National Center for X-ray Tomography is supported by the National Institute of General Medical Sciences of the National Institutes of Health Grant P41GM103445 and the US Department of Energy, Office of Biological and Environmental Research Grant DE-AC02-05CH11231.
PY - 2016/3/22
Y1 - 2016/3/22
N2 - Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.
AB - Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.
KW - 3D genome organization
KW - Centromere clustering
KW - Genome structure modeling
KW - Hi-C data analysis
KW - Human genome
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U2 - 10.1073/pnas.1512577113
DO - 10.1073/pnas.1512577113
M3 - Article
C2 - 26951677
AN - SCOPUS:84962226487
SN - 0027-8424
VL - 113
SP - E1663-E1672
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 12
ER -