Integrating Hi-C and FISH data for modeling of the 3D organization of chromosomes

Ahmed Abbas, Xuan He, Jing Niu, Bin Zhou, Guangxiang Zhu, Tszshan Ma, Jiangpeikun Song, Juntao Gao, Michael Q. Zhang and Jianyang Zeng ()
Additional contact information
Ahmed Abbas: Tsinghua University
Xuan He: Tsinghua University
Jing Niu: Tsinghua University
Bin Zhou: Tsinghua University
Guangxiang Zhu: Tsinghua University
Tszshan Ma: Tsinghua University
Jiangpeikun Song: Tsinghua University
Juntao Gao: Tsinghua University
Michael Q. Zhang: Tsinghua University
Jianyang Zeng: Tsinghua University

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-019-10005-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10005-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-10005-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().