Computational methods for analyzing genome-wide chromosome conformation capture data

Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants

Genome-wide association studies (GWAS) have identified a vast number of variants associated with various complex human diseases and traits. However, most of these GWAS variants reside in non-coding regions producing no proteins, making the interpretation of these variants a daunting challenge. Prior evidence indicates that a subset of non-coding variants detected within or near cis-regulatory elements (e.g., promoters, enhancers, silencers, and insulators) might play a key role in disease etiology by regulating gene expression. Advanced sequencing- and imaging-based technologies, together with powerful computational methods, enabling comprehensive characterization of regulatory DNA interactions, have substantially improved our understanding of the three-dimensional (3D) genome architecture. Recent literature witnesses plenty of examples where using chromosome conformation capture (3C)-based technologies successfully links non-coding variants to their target genes and prioritizes relevant tissues or cell types. These examples illustrate the critical capability of 3D genome organization in annotating non-coding GWAS variants. This review discusses how 3D genome organization information contributes to elucidating the potential roles of non-coding GWAS variants in disease etiology.

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.

In Situ HiC

In situ HiC uses the relative frequency of DNA-DNA ligation events to reconstruct the three-dimensional architecture of a genome. As such, restriction enzyme digested ends of genomic DNA within fixed nuclei are tagged with biotinylated dNTPs. DNA-DNA ligation events generated via proximity ligation are then captured, amplified and next generation sequenced to determine their linear genomic position, but also their three-dimensional relationship. Here, we describe these steps in detail.

Computational approaches for inferring 3D conformations of chromatin from chromosome conformation capture data

Chromosome conformation capture (3C) and its variants are powerful experimental techniques for probing intra- and inter-chromosomal interactions within cell nuclei at high resolution and in a high-throughput, quantitative manner. The contact maps derived from such experiments provide an avenue for inferring the 3D spatial organization of the genome. This review provides an overview of the various computational methods developed in the past decade for addressing the very important but challenging problem of deducing the detailed 3D structure or structure population of chromosomal domains, chromosomes, and even entire genomes from 3C contact maps.

Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions

The majority of single nucleotide variants (SNVs) identified in Genome Wide Association Studies (GWAS) fall within non-protein coding DNA and have the potential to alter gene expression. Non-protein coding DNA can control gene expression by acting as transcription factor (TF) binding sites or by regulating the organization of DNA into chromatin. SNVs in non-coding DNA sequences can disrupt TF binding and chromatin structure and this can result in pathology. Further, environmental health studies have shown that exposure to xenobiotics can disrupt the ability of TFs to regulate entire gene networks and result in pathology. However, there is a large amount of interindividual variability in exposure-linked health outcomes. One explanation for this heterogeneity is that genetic variation and exposure combine to disrupt gene regulation, and this eventually manifests in disease. Many resources exist that annotate common variants from GWAS and combine them with conservation, functional genomics, and TF binding data. These annotation tools provide clues regarding the biological implications of an SNV, as well as lead to the generation of hypotheses regarding potentially disrupted target genes, epigenetic markers, pathways, and cell types. Collectively this information can be used to predict how SNVs can alter an individual's response to exposure and disease risk. A basic understanding of the regulatory information contained within non-protein coding DNA is needed to predict the biological consequences of SNVs, and to determine how these SNVs impact exposure-related disease. We hope that this review will aid in the characterization of disease-associated genetic variation in the non-protein coding genome.

Genomics Methods for Xenopus Embryos and Tissues

High-throughput sequencing methods have created exciting opportunities to explore the regulatory landscape of the entire genome. Here we introduce methods to characterize the genomic locations of bound proteins, open chromatin, and sites of DNA-DNA contact in Xenopus embryos. These methods include chromatin immunoprecipitation followed by sequencing (ChIP-seq), a combination of DNase I digestion and sequencing (DNase-seq), the assay for transposase-accessible chromatin and sequencing (ATAC-seq), and the use of proximity-based DNA ligation followed by sequencing (Hi-C).

HiCeekR: A Novel Shiny App for Hi-C Data Analysis

The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.

Computational Processing and Quality Control of Hi-C, Capture Hi-C and Capture-C Data

Hi-C, capture Hi-C (CHC) and Capture-C have contributed greatly to our present understanding of the three-dimensional organization of genomes in the context of transcriptional regulation by characterizing the roles of topological associated domains, enhancer promoter loops and other three-dimensional genomic interactions. The analysis is based on counts of chimeric read pairs that map to interacting regions of the genome. However, the processing and quality control presents a number of unique challenges. We review here the experimental and computational foundations and explain how the characteristics of restriction digests, sonication fragments and read pairs can be exploited to distinguish technical artefacts from valid read pairs originating from true chromatin interactions.

Highly interacting regions of the human genome are enriched with enhancers and bound by DNA repair proteins

In specific cases, chromatin clearly forms long-range loops that place distant regulatory elements in close proximity to transcription start sites, but we have limited understanding of many loops identified by Chromosome Conformation Capture (such as Hi-C) analyses. In efforts to elucidate their characteristics and functions, we have identified highly interacting regions (HIRs) using intra-chromosomal Hi-C datasets with a new computational method based on looking at the eigenvector that corresponds to the smallest eigenvalue (here unity). Analysis of these regions using ENCODE data shows that they are in general enriched in bound factors involved in DNA damage repair and have actively transcribed genes. However, both highly transcribed regions as well as transcriptionally inactive regions can form HIRs. The results also indicate that enhancers and super-enhancers in particular form long-range interactions within the same chromosome. The accumulation of DNA repair factors in most identified HIRs suggests that protection from DNA damage in these regions is essential for avoidance of detrimental rearrangements.