CHARMER: detecting and harmonizing high-confidence chromatin interactions across tissues and Hi-C protocols

Area of Science:

• Genomics
• Epigenetics
• Computational Biology

Background:

• Chromatin conformation capture (CCC) experiments like Hi-C and Capture Hi-C (CHiC) reveal genome 3D organization and epigenetic regulation.
• These experiments generate large FASTQ datasets with significant technical noise, necessitating advanced computational pipelines.
• Existing data repositories often lack statistically significant, annotated interaction data crucial for downstream analyses.

Purpose of the Study:

• To develop an end-to-end computational pipeline for processing CCC data.
• To generate statistically significant, harmonized, and queryable chromatin interaction data.
• To provide a consistent output format across different CCC assays and cell/tissue types.

Main Methods:

• Integration of multiple CCC assay types (Hi-C, CHiC).
• Development of a sophisticated computational pipeline for data processing and analysis.
• Generation of harmonized chromatin interaction data in a BED-like format.

Main Results:

• CHARMER successfully processes CCC data to produce statistically significant chromatin interactions.
• The pipeline harmonizes interaction data across various cell/tissue types and CCC assays.
• Output data is queryable and in a consistent BED-like format.

Conclusions:

• CHARMER provides a valuable tool for analyzing CCC data, addressing limitations in current repositories.
• The pipeline enhances the utility of CCC data for downstream genetic and genomic research.
• Harmonized data will be accessible through the FILER database.