Abstract
Background Chromatin loops play a critical role in gene regulation by connecting regulatory loci and gene promoters. The identification of changes in chromatin looping between cell types or biological conditions is an important task for understanding gene regulation; however, the manipulation, statistical analysis, and visualization of data sets describing 3D chromatin structure is challenging due to the large and complex nature of the relevant data sets. Methods Here, we describe a workflow for identifying and visualizing differential chromatin loops from Hi-C data from two biological conditions using the ‘mariner’, ‘DESeq2’ and ‘plotgardener’ Bioconductor/R packages. The workflow assumes that Hi-C data has been processed into ‘.hic’ or ‘.cool’ files and that loops have been identified using an existing loop-calling algorithm. Results First, the ‘mariner’ package is used to merge redundant loop calls and extract interaction frequency counts. Next, ‘DESeq2’ is used to identify loops that exhibit differential contact frequencies between conditions. Finally, ‘plotgardener’ is used to visualize differential loops. Conclusion Chromatin interaction data is an important modality for understanding the mechanisms of transcriptional regulation. The workflow presented here outlines the use of ‘mariner’ as a tool to manipulate, extract, and aggregate chromatin interaction data, ‘DESeq2’ to perform differential analysis of these data across conditions, samples, and replicates, and ‘plotgardener’ to explore and visualize the results.
Published Version
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