Abstract
Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promotor capture Hi-C data generated on 28 different cell lines as a case study.
Highlights
Chromosome conformation capture (3C) methodologies[1,2,3] have provided insight into the effect of 3D genomic architecture on gene regulation[4,5,6]
Raw sequencing data was processed using HiCUP v0.6.19 to obtain only valid interaction di-tags aligned to build 38 of the human genome.Summary statistics for each promotor capture Hi-C (PCHi-C) dataset are provided in Supplementary Data 1
Model considerations We initially considered PCHi-C libraries from the 18 non-tumour cell lines
Summary
Chromosome conformation capture (3C) methodologies[1,2,3] have provided insight into the effect of 3D genomic architecture on gene regulation[4,5,6]. They preserve chromatin interactions by cross-linking followed by fragmentation, ligation and sequencing of interacting genomic regions. To identify ‘true’ interactions, it is necessary to identify the contribution from the null hypothesis, largely attributed to constrained Brownian motion and noise[8]. While not completely eliminating background noise, the development of in situ Hi-C, which preserves the integrity of the nucleus during Hi-C library generation, has gone some way to reducing it[3]
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