Coolpup.py: versatile pile-up analysis of Hi-C data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• High-throughput chromosome conformation capture (Hi-C) is crucial for studying genome 3D organization.
• A key limitation of Hi-C is the high sequencing depth needed for robust loop detection.
• Genome-wide averaging (piling-up) is used to overcome low sequencing depth, but current tools lack efficiency and versatility.

Purpose of the Study:

• To introduce coolpup.py, a versatile tool for Hi-C data pile-up analysis.
• To provide a computationally efficient and user-friendly solution for analyzing 3D genome organization.
• To facilitate the study of genomic interactions in various biological contexts.

Main Methods:

• Development of coolpup.py, a Python-based software for pile-up analysis.
• Application of coolpup.py to Hi-C datasets for feature averaging.
• Implementation of a novel pile-up variation for statistical analysis of looping interactions.

Main Results:

• coolpup.py successfully replicates known findings on cohesin and CTCF roles in genome organization.
• The tool reveals novel insights into Polycomb-mediated interactions.
• A new pile-up approach is presented, aiding the statistical analysis of looping interactions.

Conclusions:

• coolpup.py offers an efficient and versatile solution for Hi-C data pile-up analysis.
• The software simplifies the investigation of 3D genome organization and genomic interactions.
• coolpup.py is expected to become a valuable tool for the Hi-C research community.