Chromosome3D: reconstructing three-dimensional chromosomal structures from Hi-C interaction frequency data using...

Area of Science:

• Genomics
• Structural Biology
• Computational Biology

Background:

• Reconstructing three-dimensional (3D) chromosome structures aids in visualizing their cellular shapes and understanding their functions.
• Hi-C data provides contact counts that can be translated into spatial information.

Purpose of the Study:

• To develop a robust method for reconstructing 3D chromosome structures from Hi-C data.
• To validate the method's accuracy and robustness using simulated and real-world data.

Main Methods:

• Translating Hi-C contact counts into Euclidean distances.
• Employing a structure reconstruction method from protein structure determination.
• Validating conversion rules using Spearman's rank correlation coefficient.
• Applying the strategy to real Hi-C data for optimal transformation and structure selection.

Main Results:

• The reconstruction algorithm demonstrated robustness on noisy simulated data, outperforming state-of-the-art methods.
• Spearman's rank correlation coefficient effectively optimized the transformation of interaction frequencies to distance restraints.
• Reconstructed human chromosome structures validated the known two-compartment organization.
• The method proved robust across different chromosomal resolutions and Hi-C data granularities.

Conclusions:

• Chromosome3D offers a robust method for reconstructing 3D chromosome models using Hi-C derived distance restraints.
• The tool is accessible as a web application and open-source software, facilitating broader research use.