Abstract

The new advances in various experimental techniques that provide complementary information about the spatial conformations of chromosomes have inspired researchers to develop computational methods to fully exploit the merits of individual data sources and combine them to improve the modeling of chromosome structure. Here we propose GEM-FISH, a method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model. Comprehensive tests on a set of chromosomes, for which both Hi-C and FISH data are available, demonstrate that GEM-FISH can outperform previous chromosome structure modeling methods and accurately capture the higher order spatial features of chromosome conformations. Moreover, our reconstructed 3D models of chromosomes revealed interesting patterns of spatial distributions of super-enhancers which can provide useful insights into understanding the functional roles of these super-enhancers in gene regulation.

Methodological advances have increased our understanding of chromatin structure through chromosome conformation capture techniques and high resolution imaging, but integration of these datasets is challenging. Here the authors propose GEM-FISH, a method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model.

Details

Title
Integrating Hi-C and FISH data for modeling of the 3D organization of chromosomes
Author
Abbas, Ahmed 1 ; He, Xuan 1 ; Niu Jing 2 ; Zhou, Bin 3 ; Zhu Guangxiang 1 ; Ma Tszshan 2 ; Song Jiangpeikun 3 ; Gao Juntao 4 ; Zhang, Michael Q 5 ; Zeng Jianyang 6 

 Tsinghua University, Institute for Interdisciplinary Information Sciences, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) 
 Tsinghua University, Department of Basic Medical Sciences, School of Medicine, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) 
 Tsinghua University, School of Life Science, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) 
 Tsinghua University, MOE Key Laboratory of Bioinformatics; Bioinformatics Division, Center for Synthetic and Systems Biology, BNRist; Department of Automation, Tsinghua University; Center for Synthetic and Systems Biology, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) 
 the University of Texas at Dallas, Department of Biological Sciences, Center for Systems Biology, Richardson, USA (GRID:grid.267323.1) (ISNI:0000 0001 2151 7939); Tsinghua University, Department of Basic Medical Sciences, School of Medicine, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178); Tsinghua University, MOE Key Laboratory of Bioinformatics; Bioinformatics Division, Center for Synthetic and Systems Biology, BNRist; Department of Automation, Tsinghua University; Center for Synthetic and Systems Biology, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) 
 Tsinghua University, Institute for Interdisciplinary Information Sciences, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178); Tsinghua University, MOE Key Laboratory of Bioinformatics; Bioinformatics Division, Center for Synthetic and Systems Biology, BNRist; Department of Automation, Tsinghua University; Center for Synthetic and Systems Biology, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) 
Publication year
2019
Publication date
2019
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2219590877
Copyright
© The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.