SnapHiC2: A computationally efficient loop caller for single cell Hi-C data.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell Hi-C (scHi-C) allows studying chromatin organization in individual cells.
• Identifying chromatin loops in sparse scHi-C data is difficult.
• Existing tools like SnapHiC are computationally intensive.

Purpose of the Study:

• Introduce SnapHiC2, an optimized pipeline for chromatin loop detection from scHi-C data.
• Improve computational efficiency and memory usage compared to previous methods.
• Enhance the sensitivity and accuracy of loop identification and gene target suggestion.

Main Methods:

• Developed SnapHiC2, incorporating a sliding window approximation for contact imputation.
• Optimized imputation to reduce memory and computational time by 70%.
• Applied SnapHiC2 to scHi-C data for loop identification at 5 Kb resolution.

Main Results:

• SnapHiC2 significantly reduces computational time and memory usage.
• Achieved high sensitivity and accuracy in identifying 5 Kb resolution chromatin loops.
• Demonstrated utility in suggesting cell-type-specific target genes for GWAS variants.

Conclusions:

• SnapHiC2 offers a computationally efficient and accurate solution for analyzing scHi-C data.
• The pipeline facilitates the discovery of chromatin loops and their functional implications.
• SnapHiC2 aids in understanding gene regulation and disease associations in a cell-specific context.