A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships

Abstract

Background Chromatin contacts are essential for gene-expression regulation; however, obtaining a high-resolution genome-wide chromatin contact map is still prohibitively expensive owing to large genome sizes and the quadratic scale of pairwise data. Chromosome conformation capture (3C)-based methods such as Hi-C have been extensively used to obtain chromatin contacts. However, since the sparsity of these maps increases with an increase in genomic distance between contacts, long-range or trans-chromatin contacts are especially challenging to sample. Results Here, we create a high-density reference genome-wide chromatin contact map using a meta-analytic approach. We integrate 3600 human, 6700 mouse, and 500 fly Hi-C experiments to create species-specific meta-Hi-C chromatin contact maps with 304 billion, 193 billion, and 19 billion contacts in respective species. We validate that meta-Hi-C contact maps are uniquely powered to capture functional chromatin contacts in both cis and trans. We find that while individual dataset Hi-C networks are largely unable to predict any long-range coexpression (median 0.54 AUC), meta-Hi-C networks perform comparably in both cis and trans (0.65 AUC vs 0.64 AUC). Similarly, for long-range expression quantitative trait loci (eQTL), meta-Hi-C contacts outperform all individual Hi-C experiments, providing an improvement over the conventionally used linear genomic distance-based association. Assessing between species, we find patterns of chromatin contact conservation in both cis and trans and strong associations with coexpression even in species for which Hi-C data is lacking. Conclusions We have generated an integrated chromatin interaction network which complements a large number of methodological and analytic approaches focused on improved specificity or interpretation. This high-depth “super-experiment” is surprisingly powerful in capturing long-range functional relationships of chromatin interactions, which are now able to predict coexpression, eQTLs, and cross-species relationships. The meta-Hi-C networks are available at https://labshare.cshl.edu/shares/gillislab/resource/HiC/.

Randomizing the human genome by engineering recombination between repeat elements

“…This suggests that other factors, such as 3D proximity, may influence the probability of observing a rearrangement. A model that uses 3D distance, assessed by counting Hi-C read pairs, fits the data better (Pearson’s R = 0.68 (Hi-C, ( 29 )) vs 0.55 (length), Figure 3F). A linear model with both linear distance and Hi-C pairs only modestly outperformed one with only Hi-C pairs (R = 0.68 vs 0.67) suggesting that 3D contact frequency between two recognition sites is a better explanation of Cre efficiency.…”

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets

“…However, this approach is limited to TRs with long residence times on chromatin, despite some TRs being known for lack of detectable footprints [68,69]. Second, including three-dimensional long-range interaction information from Hi-C data may also serve as a possible improvement direction [70][71][72], but the sparse nature and low resolution of Hi-C data might also cause misleading conclusions and therefore should be used with caution and further validation.…”

The impact of HTLV-1 expression on the 3D structure and expression of host chromatin

“…The impact of HTLV-1 expression on the 3D structure and expression of host chromatin chromatin loops in the human genome [23], and with the mechanism of loop extrusion by which these loops are formed [24]. It is known that contacts can also be made with more distant locations on the genome, including other chromosomes; however, such distant contacts are typically much less frequent, although certain trans-chromosomal contacts may be evolutionarily conserved [25].…”