Interpreting 4C-Seq Data: how far can we go?

“…In 4C-Seq, an inverse PCR step allows for the identification of all possible genome wide interactions from a single viewpoint (the "bait") and an assessment of the frequencies at which these occur. The sequencing coverage obtained by 4C near the bait region is extremely high and therefore enables precise characterization and quantification of regulatory interactions [12,13]. By focusing on one locus at a time and thus only the interactions that this locus is engaged in, 4C can reproducibly identify long-range interactions on cis and trans chromosomes [14].…”

Section: Introductionmentioning

confidence: 99%

“…Browser view of a far-cis region on chromosome12 showing domains identified as High, Low and Non interacting states and the location of BACs chosen to label these regions as well as the distances separating them from each other and from Igh. These BACs, together with probes labeling the constant region of Igh were used for 3D-FISH on activated B cells.…”

mentioning

confidence: 99%

4C-ker: A method to reproducibly identify genome-wide interactions captured by 4C-Seq experiments

Raviram

Rocha

Müller

et al. 2015

Preprint

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Abstract4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes. PLOS Computational Biology |

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“…In recent years the influence of chromosomal interactions in gene regulation has become increasingly apparent (1)(2)(3)(4)(5)(6). Insights into the importance of these associations were initially obtained from studies of at the single cell level using DNA FISH and more recently by molecular analysis using chromosome conformation capture (3C) techniques (7)(8)(9)(10)(11). The original 3C assays examined interaction frequencies between two fixed points of interest, while a later iteration of this technique, 4C-seq enabled comparisons of interaction frequencies from a single viewpoint to all other sites across the genome.…”

Section: Introductionmentioning

confidence: 99%

“…Hi-C took this a step further providing a tool to analyze all possible pairwise interactions in the nucleus (11). These molecular approaches highlighted the importance of enhancer -promoter interactions in controlling lineage and stage-specific gene expression patterns (12).…”

Section: Introductionmentioning

confidence: 99%

The interdependent nature of multi-loci associations can be revealed by 4C-Seq

Jiang

Raviram

et al. 2015

Preprint

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word limit)Use of low resolution single cell DNA FISH and population based high resolution chromosome conformation capture techniques have highlighted the importance of pairwise chromatin interactions in gene regulation. However, it is unlikely that these associations act in isolation of other interacting partners within the genome. Indeed, the influence of multi-loci interactions in gene control remains something of an enigma as beyond low-resolution DNA FISH we do not have the appropriate tools to analyze these.Here we present a method that uses standard 4C-seq data to identify multi-loci interactions from the same cell. We demonstrate the feasibility of our method using 4Cseq data sets that identify known pairwise interactions involving the Tcrb and Igk antigen receptor enhancers, in addition to novel tri-loci associations. We further show that enhancer deletions not only interfere with tri-loci interactions in which they participate, but they also disrupt pairwise interactions between other partner enhancers and this disruption is linked to a reduction in their transcriptional output. These findings underscore the functional importance of hubs and provide new insight into chromatin organization as a whole. Our method opens the door for studying multi-loci interactions and their impact on gene regulation in other biological settings. ! 3!

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Interpreting 4C-Seq Data: how far can we go?

Cited by 15 publications

References 17 publications

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4C-ker: A method to reproducibly identify genome-wide interactions captured by 4C-Seq experiments

The interdependent nature of multi-loci associations can be revealed by 4C-Seq

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