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

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.

Automated summary

In this study, an innovative method called NeRV-3D and NeRV-3D-DC were introduced for reconstructing 3D chromosome structures at low and high resolutions respectively. The results show that both methods outperform existing methods in terms of 3D visualization effects and evaluation metrics.