4See: a flexible browser to explore 4C data
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE137417 | GEO | 2019/09/14
REPOSITORIES: GEO
Similar Datasets
Project description:4See: a flexible browser to explore 4C data
| PRJNA565407 | ENA
Project description:4C-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. 4C-Seq experiments from Igh and Cd83 bait in activated B cells and Tcrb (Eb) bait in double negative T cells and immature B cells. RNA-Seq and ATAC-Seq experiments in DN and Immature B cells.
2016-02-27 | E-GEOD-77645 | biostudies-arrayexpress
Project description:4C-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.
2016-02-27 | GSE77645 | GEO
Project description:We performed 4C analysis of the immunity-related FCGR2A gene promoter in the U937 monocytic cell line, using cells in resting conditions. The FCGR2A transcription start site was used as bait.
2022-06-22 | GSE180154 | GEO
Project description:Allele-specific circular chromosome conformation capture sequencing (4C-seq) using the single nucleotide polymorphism rs2836411 as a bait was performed in two cell types: human umibilical vein endothelial cells (HUVECs) and human aortic smooth muscle cells (HASMCs).
2019-10-11 | GSE137288 | GEO
Project description:High-resolution methods such as 4C and Capture-C enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/CapC analyses is the identification of robust peaks in the data for the identification of chromatin loops. Here we present an R package for the analysis of 4C/CapC data. We generated 4C data for 10 viewpoints in 2 tissues in triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates.
2018-05-14 | GSE105177 | GEO
Project description:It is established that transcription of many metazoan genes is regulated by distal regulatory sequences beyond the promoter. Enhancers have been identified at up to megabase distances from their regulated genes, and/or proximal to or within the introns of unregulated genes. The unambiguous identification of the target genes of newly identified regulatory elements can thus be challenging. Well-studied enhancers have been found to come into direct physical proximity with regulated genes, presumably by the formation of chromatin loops. Chromosome conformation capture (3C) derivatives that assess the frequency of proximity between different genetic elements is thus a popular method for exploring gene regulation by distal regulatory elements. For studies of chromatin loops and promoter-enhancer communication, 4C (circular chromosome conformation capture) is one of the methods of choice, optimizing cost (required sequencing depth), throughput, and resolution. For ease of visual inspection of 4C data we present 4See, a versatile and user-friendly browser. 4See allows 4C profiles from the same bait to be flexibly plotted together, allowing biological replicates to either be compared, or pooled for comparisons between different cell types or experimental conditions. 4C profiles can be integrated with gene tracks, linear epigenomic profiles, and annotated regions of interest, such as called significant interactions, allowing rapid data exploration with limited computational resources or bioinformatics expertise.
| S-EPMC6985583 | biostudies-literature
Project description:One of the strongest associated type 2 diabetes (T2D) loci reported to date resides within the TCF7L2 gene. Previous studies point to the T allele of rs7903146 in intron 3 as the causal variant at this locus. To aid in the identification of the actual gene(s) under the influence of this variant, we first generated a CRISPR/Cas9 mediated 1.4kb deletion of the genomic region harboring rs7903146 in the HCT116 cell line followed by global gene expression analysis (see experiment E-MTAB-4839). We then carried out high-throughput chromosome conformation capture assays in the HCT116 and NCM460 cell lines and in colon tissue in order to ascertain which of these perturbed genes promoters made consistent physical contact with the genomic region harboring the variant. To assess consistency and reproducibility we utilized two different techniques: Circularized Chromosome Conformation Capture (4C) and Capture C. In both types of assays, after preparing 3C libraries, our bait of interest was the region harboring rs7903146. Loci interacting with such bait are enriched for by inverse PCR in 4C and by oligonucleotide capture in capture C. 4C assays were carried out in the following samples: two on NCM460 cells (using different primer sets), one on HCT116 cells, one on HCT116 cells with a CRISPR/Cas9 mediated 1.4kb deletion of the genomic region harboring the SNP rs7903146, and one on colon tissue. Capture C assays were carried out in one sample each of the cell lines NCM 460 and HCT116. This experiment, coupled with the associated E-MTAB-4839, revealed just one gene, ACSL5, which resides in the same topologically associating domain as TCF7L2.
2016-08-15 | E-MTAB-4845 | biostudies-arrayexpress
Project description:To explore the circadian regulations of Bmal1, we examined the transcriptome changes in mouse livers upon Bmal1 knock out at two circadian time points, CT0 and CT12. To explore the circadian regulations of Nr1d1, we examined the transcriptome changes in mouse livers upon Nr1d1 knock out at two circadian time points, CT0 and CT12. To explore interactome of lnc-Crot, 4C-seq was performed with lnc-Crot as bait region at CT6 and CT18.
2017-04-12 | GSE87299 | GEO
Project description:Purpose: The aim of this study is to investigate enhancer promoter interactions in skeletal muscle Methods: 50 ng of purified DNA were used as a template for PCR with bait-specific primers (Table S5) containing Illumina adapter termini. PCR reactions were pooled and, after primer removal with 1.8x AMPure XP beads, DNA was sequenced with the HiSeq 4000 (Illumina), as single-end 50 bp reads. Sequences were trimmed to remove primer and bait fragment sequence with the sabre tool (https://github.com/najoshi/sabre), mapped to the genome with Bowtie and converted to restriction fragment space as in van de Werken et al., 2012 (van de Werken et al., 2012). Interactions were called on single 4C experiments with peakC (Geeven et al., 2018), using a sliding window of 21 fragments.
2020-10-01 | GSE142516 | GEO