R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets

Methods for the Differential Analysis of Hi-C Data

The 3D organization of chromatin within the nucleus enables dynamic regulation and cell type-specific transcription of the genome. This is true at multiple levels of resolution: on a large scale, with chromosomes occupying distinct volumes (chromosome territories); at the level of individual chromatin fibers, which are organized into compartmentalized domains (e.g., Topologically Associating Domains-TADs), and at the level of short-range chromatin interactions between functional elements of the genome (e.g., enhancer-promoter loops).The widespread availability of Chromosome Conformation Capture (3C)-based high-throughput techniques has been instrumental in advancing our knowledge of chromatin nuclear organization. In particular, Hi-C has the potential to achieve the most comprehensive characterization of chromatin 3D interactions, as it is theoretically able to detect any pair of restriction fragments connected as a result of ligation by proximity.This chapter will illustrate how to compare the chromatin interactome in different experimental conditions, starting from pre-computed Hi-C contact matrices, how to visualize the results, and how to correlate the observed variations in chromatin interaction strength with changes in gene expression.

HiC1Dmetrics: framework to extract various one-dimensional features from chromosome structure data

Eukaryotic genomes are organized in a three-dimensional spatial structure. In this regard, the development of chromosome conformation capture methods has enabled studies of chromosome organization on a genomic scale. Hi-C, the high-throughput chromosome conformation capture method, can reveal a population-averaged, hierarchical chromatin structure. The typical Hi-C analysis uses a two-dimensional (2D) contact matrix that indicates contact frequencies between all possible genomic position pairs. Oftentimes, however, such a 2D matrix is not amenable to handling quantitative comparisons, visualizations and integrations across multiple datasets. Although several one-dimensional (1D) metrics have been proposed to depict structural information in Hi-C data, their effectiveness is still underappreciated. Here, we first review the currently available 1D metrics for individual Hi-C samples or two-sample comparisons and then discuss their validity and suitable analysis scenarios. We also propose several new 1D metrics to identify additional unique features of chromosome structures. We highlight that the 1D metrics are reproducible and robust for comparing and visualizing multiple Hi-C samples. Moreover, we show that 1D metrics can be easily combined with epigenome tracks to annotate chromatin states in greater details. We develop a new framework, called HiC1Dmetrics, to summarize all 1D metrics discussed in this study. HiC1Dmetrics is open-source (github.com/wangjk321/HiC1Dmetrics) and can be accessed from both command-line and web-based interfaces. Our tool constitutes a useful resource for the community of chromosome-organization researchers.