ArticleF1000Research2024
A Bioconductor/R Workflow for the Detection and Visualization of Differential Chromatin Loops.
Article in F1000Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- UnionLoops: a workflow for calling chromatin loops across related Hi-C datasets with improved specificity, precision, and sensitivity.Genome biology · 2026Article
- Highly efficient chromatin conformation capture with post-enrichment in single cells by HiChew.Genome biology · 2026Article
- UnionLoops: a workflow for calling chromatin loops across related Hi-C datasets with improved specificity, precision, and sensitivity.bioRxiv : the preprint server for biology · 2026Article
- Stress-induced loss of CTCF reveals an alternative, promoter-based mode of cohesin looping.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.