A Bioconductor/R Workflow for the Detection and Visualization of Differential Chromatin Loops

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Chromatin loops are crucial for gene regulation, connecting regulatory elements to gene promoters.
• Analyzing 3D chromatin structure data (like Hi-C) is challenging due to data size and complexity.
• Identifying differential looping between conditions is key to understanding gene regulation changes.

Purpose of the Study:

• To present a computational workflow for identifying and visualizing differential chromatin loops from Hi-C data.
• To streamline the analysis of 3D genome organization changes between biological conditions.
• To provide a reproducible method for researchers studying gene regulation.

Main Methods:

• Utilized the Bioconductor/R packages 'mariner', 'DESeq2', and 'plotgardener'.
• Processed Hi-C data (in .hic or .cool format) with pre-identified loops.
• 'mariner' merged loop calls and extracted interaction counts.
• 'DESeq2' performed differential contact frequency analysis.
• 'plotgardener' visualized the identified differential loops.

Main Results:

• Successfully identified differential chromatin loops between two biological conditions.
• Demonstrated the utility of 'mariner' for data manipulation and aggregation.
• Showcased 'DESeq2' for robust differential analysis of interaction frequencies.
• Provided clear visualizations of differential looping using 'plotgardener'.

Conclusions:

• The presented workflow effectively analyzes and visualizes differential chromatin loops from Hi-C data.
• The combination of 'mariner', 'DESeq2', and 'plotgardener' offers a powerful tool for 3D genome analysis.
• This approach facilitates the understanding of transcriptional regulation mechanisms through chromatin interaction data.