ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data

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

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.

Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness

BACKGROUND

Chromosome is one of the most fundamental part of cell biology where DNA holds the hierarchical information. DNA compacts its size by forming loops, and these regions house various protein particles, including CTCF, SMC3, H3 histone. Numerous sequencing methods, such as Hi-C, ChIP-seq, and Micro-C, have been developed to investigate these properties. Utilizing these data, scientists have developed a variety of loop prediction techniques that have greatly improved their methods for characterizing loop prediction and related aspects.

RESULTS

In this study, we categorized 22 loop calling methods and conducted a comprehensive study of 11 of them. Additionally, we have provided detailed insights into the methodologies underlying these algorithms for loop detection, categorizing them into five distinct groups based on their fundamental approaches. Furthermore, we have included critical information such as resolution, input and output formats, and parameters. For this analysis, we utilized the GM12878 Hi-C datasets at 5 KB, 10 KB, 100 KB and 250 KB resolutions. Our evaluation criteria encompassed various factors, including memory usages, running time, sequencing depth, and recovery of protein-specific sites such as CTCF, H3K27ac, and RNAPII.

CONCLUSION

This analysis offers insights into the loop detection processes of each method, along with the strengths and weaknesses of each, enabling readers to effectively choose suitable methods for their datasets. We evaluate the capabilities of these tools and introduce a novel Biological, Consistency, and Computational robustness score ( B C C score ) to measure their overall robustness ensuring a comprehensive evaluation of their performance.

The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data

The three-dimensional organization of chromatin plays a crucial role in gene regulation and cellular processes like deoxyribonucleic acid (DNA) transcription, replication and repair. Hi-C and related techniques provide detailed views of spatial proximities within the nucleus. However, data analysis is challenging partially due to a lack of well-defined, underpinning mathematical frameworks. Recently, recognizing and analyzing geometric patterns in Hi-C data has emerged as a powerful approach. This review provides a summary of algorithms for automatic recognition and analysis of geometric patterns in Hi-C data and their correspondence with chromatin structure. We classify existing algorithms on the basis of the data representation and pattern recognition paradigm they make use of. Finally, we outline some of the challenges ahead and promising future directions.