Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment

Abstract

Gene expression during development and differentiation is regulated in a cell- and stage-specific manner by complex networks of intergenic and intragenic cis-regulatory elements whose numbers and representation in the genome far exceed those of structural genes. Using chromosome conformation capture, it is now possible to analyze in detail the interaction between enhancers, silencers, boundary elements and promoters at individual loci, but these techniques are not readily scalable. Here we present a high-throughput approach (Capture-C) to analyze cis interactions, interrogating hundreds of specific interactions at high resolution in a single experiment. We show how this approach will facilitate detailed, genome-wide analysis to elucidate the general principles by which cis-acting sequences control gene expression. In addition, we show how Capture-C will expedite identification of the target genes and functional effects of SNPs that are associated with complex diseases, which most frequently lie in intergenic cis-acting regulatory elements.

Evolutionary comparison reveals that diverging CTCF sites are signatures of ancestral topological associating domains borders

“…S1) preferentially contacts the six3a but not the six2a promoter (Fig. 1A), with only minor contacts beyond the border region and with other contacts in common with the six3a promoter, revealing many enhancer-enhancer interactions within the six3a regulatory domain, as found for other loci (13)(14)(15)(16). The six3a and six2a 3D chromatin architecture is already present at blastula stage, before transcription initiation (Fig.…”

VHL Deficiency Drives Enhancer Activation of Oncogenes in Clear Cell Renal Cell Carcinoma

“…In order to achieve short read mapping to the hg19 reference genome, the resulting preprocessed reads were then in silico digested with DpnII and aligned using Bowtie (using p1, m2, best, and strata settings). Aligned reads were processed using Capture-C analyzer (118) to (i) remove PCR duplicates; (ii) classify subfragments as "capture" if they were contained within the capture fragment, "proximity exclusion" if they were within 1 Kb on either side of the capture fragment, or "reporter" if they were outside of the "capture" and "proximity exclusion" regions; and (iii) normalize read counts per 100,000 interactions in bigwig format. We additionally used the r3Cseq package (119) on the capture and reporter fragments to identify significant interactions of the viewpoint against a scaled background (q-value < 0.05).…”

Iteratively improving Hi-C experiments one step at a time

“…Even when more focused resolution is desired, many techniques begin with an approach similar to Hi-C and then isolate a subpopulation of interactions of interest. This is the case with approaches such as Capture-C [18], Hi-ChIP [19], and others. Further, it has recently become clear that Hi-C is a very useful tool not only for measuring 3D genome folding, but also for de novo whole genome sequence assembly [20][21][22] and translocation detection [23].…”