Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Hi-C contact maps reveal three-dimensional genome organization but require statistical methods to identify significant contacts.
• Previous methods like Fit-Hi-C used splines to model distance-dependent contact decay and correct for biases, primarily for mid-range intra-chromosomal interactions.
• There is a need for scalable and versatile tools to analyze high-resolution, genome-wide Hi-C data, including inter-chromosomal contacts and filtering of indirect interactions.

Purpose of the Study:

• To introduce and describe the application of FitHiC2, an enhanced computational tool for statistical confidence estimation of Hi-C contact maps.
• To demonstrate FitHiC2's capability in identifying significant chromatin contacts across different resolutions and genomic scales, including genome-wide and inter-chromosomal interactions.
• To showcase FitHiC2's utility in filtering indirect interactions and accurately detecting key genomic loops, such as those involving CTCF binding sites.

Main Methods:

• FitHiC2 employs a non-parametric spline fitting approach to model the relationship between genomic distance and contact probability.
• The protocol incorporates corrections for locus-specific biases to improve the accuracy of contact probability estimates.
• FitHiC2 includes a merging filter module designed to eliminate indirect or bystander interactions, thereby refining the set of significant contacts.

Main Results:

• FitHiC2 successfully applied to diverse Hi-C datasets, including high-resolution (5 kb) intra-chromosomal contacts, whole-genome (40 kb) data, and yeast data at single restriction site resolution.
• The tool demonstrates scalability for genome-wide analysis of high-resolution (1 kb) Hi-C data, with processing times dependent on hardware resources.
• The merging filter module effectively reduces the number of reported contacts while preserving the detection of functionally relevant loops, like convergent CTCF loops.

Conclusions:

• FitHiC2 provides a robust and scalable framework for statistically assessing Hi-C contact maps, enabling more accurate identification of significant chromatin interactions.
• The reimplementation offers enhanced capabilities for genome-wide and inter-chromosomal analyses, as well as improved filtering of indirect interactions.
• FitHiC2 is a valuable bioinformatics tool for researchers studying 3D genome organization, readily available through common package managers.