RT Journal
A1 Szałaj, Przemysław
A1 Tang, Zhonghui
A1 Michalski, Paul
A1 Pietal, Michal J
A1 Luo, Oscar J
A1 Sadowski, Michał
A1 Li, Xingwang
A1 Radew, Kamen
A1 Ruan, Yijun
A1 Plewczynski, Dariusz
T1 An integrated 3-dimensional genome modeling engine for data-driven simulation of spatial genome organization
JF Genome Research 
JO Genome Research 
YR 2016 
FD October 27 
DO 10.1101/gr.205062.116 
SP gr.205062.116 
UL http://genome.cshlp.org/content/early/2016/10/27/gr.205062.116.abstract 
AB ChIA-PET and Hi-C are high throughput versions of 3C-based mapping technologies that reveal long-range chromatin interactions and provide insights into the basic principles of spatial genome organization and gene regulation. Recently, we showed that a single ChIA-PET experiment provides information at all genomic scales of interest, from the high resolution locations of binding sites and enriched chromatin interactions mediated by specific protein factors, to the low resolution non-enriched interactions that reflect topological neighborhoods of higher-order associations. This multilevel nature of ChIA-PET data offers us an opportunity to use multiscale 3D models to study structural-functional relationships at multiple length scales, but doing so requires a structural modeling platform, which takes advantage of the full range of ChIA-PET data.
Here we report 3D-NOME (3-Dimensional NucleOme Modeling Engine), a complete computational pipeline for processing and analyzing ChIA-PET data. 3D-NOME consists of three integrated tools: a graph-distance-based heatmap normalization tool, a 3D modeling platform, and an interactive 3D visualization tool. We use ChIA-PET and Hi-C data of human B-lymphocytes to demonstrate the effectiveness of 3D-NOME in building 3D genome models at multiple levels, including the entire nucleome, individual chromosomes, and specific segments at megabase (Mb) and kilobase (kb) resolutions. Our simulation protocol generates a single average structure or an ensemble of structures. We incorporate CTCF-motif orientation and high-resolution looping patterns in order to achieve more reliable, biologically plausible structures.