Comparison of computational methods for 3D genome analysis at single-cell Hi-C level

Area of Science:

• Genomics
• Molecular Biology
• Computational Biology

Background:

• Hi-C is a standard high-throughput chromosome conformation capture technology.
• High-resolution Hi-C data is often cost-prohibitive, especially for single-cell studies.
• The performance of computational methods on ultra-sparse Hi-C data remains unevaluated.

Purpose of the Study:

• To survey computational methods for Hi-C data analysis.
• To assess the performance of existing methods on ultra-sparse Hi-C data.
• To investigate the nature of genomic structures (compartments, TADs, loops) in single cells versus cell populations.

Main Methods:

• Review of primary computational methods for Hi-C data analysis.
• Performance evaluation of representative methods on normalized, compartment identification, TAD, and loop detection using ultra-low resolution data.
• Application of top-performing methods to real single-cell Hi-C data.

Main Results:

• Most state-of-the-art computational methods perform poorly on ultra-sparse Hi-C data.
• Analysis of real single-cell Hi-C data revealed distinct behaviors for different genomic structures.
• Chromosomal compartments appear to be statistical features of cell populations.

Conclusions:

• Existing computational tools require re-evaluation for ultra-sparse Hi-C data applications.
• Chromosomal compartments may not be reliably detectable at the single-cell level with current methods.
• Topologically Associating Domains (TADs) and chromatin loops exhibit dynamic characteristics within individual cells.