Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks.

ABSTRACT: The spatial conformation of chromosomes and genomes of single cells is relevant to cellular function and useful for elucidating the mechanism underlying gene expression and genome methylation. The chromosomal contacts (i.e. chromosomal regions in spatial proximity) entailing the three-dimensional (3D) structure of the genome of a single cell can be obtained by single-cell chromosome conformation capture techniques, such as single-cell Hi-C (ScHi-C). However, due to the sparsity of chromosomal contacts in ScHi-C data, it is still challenging for traditional 3D conformation optimization methods to reconstruct the 3D chromosome structures from ScHi-C data. Here, we present a machine learning-based method based on a novel SO(3)-equivariant graph neural network (HiCEGNN) to reconstruct 3D struc

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11928942 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature