Hi-C contact map of the Jurkat T cell genome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA506997 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8244644_1.fastq.gz | Fastqsanger.gz | |||
| SRR8244644_2.fastq.gz | Fastqsanger.gz | |||
| SRR8244645_1.fastq.gz | Fastqsanger.gz | |||
| SRR8244645_2.fastq.gz | Fastqsanger.gz | |||
| SRR8244646_1.fastq.gz | Fastqsanger.gz |
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Project description:We performed Hi-C on untreated Jurkat T cells growing in culture. We obtained contact maps with a final resolution of 5 kb where Topologically Associated Domains (TADs) are clearly visible. We identified 3D sub-compartments, characterized by enriched Hi-C contacts within themselves compared to contacts with other compartments. Our results suggest that the A compartment, associated with ongoing transcription, consists of two distinct sub-compartments called A1 and A2; and that the B compartment, associated with lack of transcription, consists of three sub-compartments called B1, B2 and B3. Genes in the A1 and A2 compartments are typically expressed at the same level, but chromatin marks associated with transcription, such as H3K4me2 or H3K27ac, are typically more abundant in A1 than in A2. The B1, B2 and B3 sub-compartments correspond to Polycomb, HP1 and lamina-associated chromatin, which were previously identified types of silent chromatin. Overall, these results suggest that spatial neighborhoods of the Jurkat nucleus correspond to homogeneous chromatin types, and that transcriptionally active regions are in fact two distinct types.
2018-11-27 | GSE122958 | GEO
Project description:H3K27ac Hi-ChIP analysis was performed in TAL1-FKBP12 Jurkat cells to analyze active chromatin-chromatin interactions after DMSO or dTAG-13 treatment.
2023-11-20 | GSE225937 | GEO
Project description:The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of celltype- specific interactions in settings where 3C-based assays are infeasible.
2024-08-31 | GSE246859 | GEO
Project description:H3K27ac Hi-ChIP analysis was performed in Jurkat and RPMI-8402 stably expressing dCAR9-KRAB after treatment with DMSO (Control) or Doxycycline to induce sgRNA expression to block MYCN enhnacer region
2023-08-16 | GSE231957 | GEO
Project description:H3K27ac Hi-ChIP analysis in TAL1-FKBP12 Jurkat cells
| PRJNA938077 | ENA
Project description:The spatial arrangement of interphase chromosomes in the nucleus is important for gene expression and genome function in animals and in plants. The recently developed Hi-C technology is an efficacious method to investigate genome packing. Here we present a detailed Hi-C map of the three-dimensional genome organization of the plant Arabidopsis thaliana. We find that local chromatin packing differs from the patterns seen in animals, with kilobasepair-sized segments that have much higher intra-chromosome interaction rates than neighboring regions and which represent a dominant local structural feature of genome conformation in A. thaliana. These regions appear as positive strips on two-dimensional representations of chromatin interaction and they are enriched in epigenetic marks H3K27me3, H3.1 and H3.3. We also identify over 400 insulator-like regions. Furthermore, although topologically associating domains (TADs), which are prominent in animals, are not the dominant feature of A. thaliana genome packing, we found over 1,000 regions that have properties of TAD boundaries, and a similar number of regions similar to the interior of TADs. These insulator-like, TAD-boundary-like, and TAD-interior-like regions show strong enrichment for distinct epigenetic marks, and correlate with gene transcription levels. We conclude that epigenetic modifications, gene density, and transcriptional activity all contribute to shaping the local structure of the A. thaliana nuclear genome.
2014-09-24 | E-MTAB-4438 | biostudies-arrayexpress
Project description:Mammalian interphase chromosomes interact with the nuclear lamina (NL) through hundreds of large Lamina Associated Domains (LADs). We report a method to map NL contacts genome-wide in single human cells. Analysis of ~400 maps reveals a core architecture of gene-poor LADs that contact the NL with high cell-to-cell consistency, interspersed by LADs with more variable NL interactions. The variable contacts are more sensitive to a change in genome ploidy than the consistent contacts. Single-cell maps indicate that NL contacts involve multivalent interactions over hundreds of kilobases. Moreover, we observe extensive intra-chromosomal coordination of NL contacts, even over tens of megabases. Such coordinated loci exhibit preferential interactions as detected by Hi-C. Finally, single-cell gene expression and chromatin accessibility analysis shows that loci with consistent NL contacts are expressed at lower levels and are more consistently inaccessible than loci with lower contact frequencies. These results highlight fundamental principles of single cell chromatin organization. Hi-C Data
2015-09-24 | E-GEOD-69841 | biostudies-arrayexpress
Project description:We generated an interaction map using capture in situ Hi-C in human iPSC-derived cardiomyocytes Differentiation of cardiomyocytes from iPSC followed by capture in situ Hi-C
2016-02-22 | E-GEOD-77266 | biostudies-arrayexpress
Project description:ChromaFold predicts the 3D contact map from single-cell chromatin accessibility
| PRJNA1035004 | ENA
Project description:The organisation of the genome in nuclear space is an important frontier of biology. Chromosome conformation capture methods such as Hi-C and Micro-C produce genome-wide chromatin contact maps that provide rich data containing quantitative and qualitative information about genome architecture. Most conventional approaches to genome-wide chromosome conformation capture data are limited to the analysis of pre-defined features, and may therefore miss important biological information. One constraint is that biologically important features can be masked by high levels of technical noise in the data. Here we introduce Twins, a replicate-based method for deep learning from chromatin conformation contact maps. Using a Siamese network configuration, Twins learns to distinguish technical noise from biological variation and outperforms image similarity metrics across a range of biological systems. Features extracted by Twins from Hi-C maps after perturbation of cohesin and CTCF reflect the distinct biological functions of cohesin and CTCF in the formation of domains and boundaries, respectively. Twins distance metrics are biologically meaningful, as they mirror the density of cohesin and CTCF binding. Taken together, these properties make Twins an powerful tool for the exploration of chromosome conformation capture data, such as Hi-C capture Hi-C, and Micro-C.
2023-06-30 | GSE233377 | GEO