A 3-D Chromosome Structure Reconstruction Method With High Resolution Hi-C Data Using Nonlinear Dimensionality Reduction and Divide-and-Conquer Strategy

Haiyan Gong; Fuqiang Ma; Xiaotong Zhang; Yi Yang; Minghong Li; Zhengyuan Chen; Sichen Zhang; Yang Chen

doi:10.1109/TNB.2023.3277440

A 3-D Chromosome Structure Reconstruction Method With High Resolution Hi-C Data Using Nonlinear Dimensionality Reduction and Divide-and-Conquer Strategy

IEEE Trans Nanobioscience. 2023 Oct;22(4):716-727. doi: 10.1109/TNB.2023.3277440. Epub 2023 Oct 3.

Authors

Haiyan Gong, Fuqiang Ma, Xiaotong Zhang, Yi Yang, Minghong Li, Zhengyuan Chen, Sichen Zhang, Yang Chen

PMID: 37200118
DOI: 10.1109/TNB.2023.3277440

Abstract

Chromosomes are fundamental components of genetic material, and their structural characteristics play an essential role in the regulation of gene expression. The advent of high-resolution Hi-C data has enabled scientists to explore the three-dimensional structure of chromosomes. However, most of the currently available methods for reconstructing chromosome structures are unable to achieve high resolutions, such as 5 Kilobase (KB). In this study, we present NeRV-3D, an innovative method that utilizes a nonlinear dimensionality reduction visualization algorithm to reconstruct 3D chromosome structures at low resolutions. Additionally, we introduce NeRV-3D-DC, which employs a divide-and-conquer technique to reconstruct and visualize 3D chromosome structures at high resolutions. Our results demonstrate that both NeRV-3D and NeRV-3D-DC outperform existing methods in terms of 3D visualization effects and evaluation metrics on simulated and actual Hi-C datasets. The implementation of NeRV-3D-DC can be found at https://github.com/ghaiyan/ NeRV-3D-DC.