An integrated 3-dimensional genome modeling engine for data-driven simulation of spatial genome organization
- Przemysław Szałaj1,
- Zhonghui Tang2,
- Paul Michalski2,
- Michal J Pietal3,
- Oscar J Luo2,
- Michał Sadowski3,
- Xingwang Li2,
- Kamen Radew3,
- Yijun Ruan2 and
- Dariusz Plewczynski1,4
- 1 Warsaw University & Medical University of Bialystok;
- 2 The Jackson Laboratory for Genomic Medicine;
- 3 Warsaw University
- ↵* Corresponding author; email: d.plewczynski{at}cent.uw.edu.pl
Abstract
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.
- Received February 4, 2016.
- Accepted October 20, 2016.
- Published by Cold Spring Harbor Laboratory Press
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