Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Robust Hi-C maps of enhancer-promoter interactions reveal the function of non-coding genome in neural development and diseases

ABSTRACT: Robust Hi-C maps of enhancer-promoter interactions reveal the function of non-coding genome in neural development and diseases

PROVIDER: PRJNA474896 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR7270960_1.fastq.gz	Fastqsanger.gz
	SRR7270960_2.fastq.gz	Fastqsanger.gz
	SRR7270970_1.fastq.gz	Fastqsanger.gz
	SRR7270970_2.fastq.gz	Fastqsanger.gz
	SRR7270980_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 126

Similar Datasets

Robust Hi-C maps of enhancer-promoter interactions reveal the function of non-coding genome in neural development and diseases

Project description:Genome-wide mapping of chromatin interactions at high resolution remains experimentally and computationally challenging. Here we used a low-input “easy Hi-C” protocol to map the 3D genome architecture in human neurogenesis and brain tissues and also demonstrated that a rigorous Hi-C bias-correction pipeline (HiCorr) can significantly improve the sensitivity and robustness of Hi-C loop identification at sub-TAD level, especially the enhancer-promoter (E-P) interactions. We used HiCorr to compare the high-resolution maps of chromatin interactions from 10 tissue or cell types with a focus on neurogenesis and brain tissues. We found that dynamic chromatin loops are better hallmarks for cellular differentiation than compartment switching. HiCorr allowed direct observation of cell-type- and differentiation-specific E-P aggregates spanning large neighborhoods, suggesting a mechanism that stabilizes enhancer contacts during development. Interestingly, we concluded that Hi-C loop outperforms eQTL in explaining neurological GWAS results, revealing a unique value of high-resolution 3D genome maps in elucidating the disease etiology.

2020-07-31 | GSE116825 | GEO

Robust Hi-C maps of enhancer-promoter interactions reveal the function of non-coding genome in neural development and diseases

Project description:Robust Hi-C maps of enhancer-promoter interactions reveal the function of non-coding genome in neural development and diseases

| PRJNA480299 | ENA

Robust Hi-C maps of enhancer-promoter interactions reveal the function of non-coding genome in neural development and diseases

Project description:Despite recent progress in mammalian 3D genome studies, it remains experimentally and computationally challenging to identify chromatin interactions genome-wide. Here we developed a highly efficient “easy Hi-C” (eHi-C) protocol that generates high-yield libraries with 0.1 million cells. After rigorous bias-correction with a significantly improved Hi-C analysis pipeline, we can directly recognize the dynamic long- and short-range chromatin loops from contact heatmaps. We compared 10 ultra-deep Hi-C or eHi-C datasets (billion- read scale) from human tissue- or cell types focusing on brain and neurogenesis. We found that H3K9me3 marks a 3D genome compartmental barrier for induced pluripotency. Dynamic chromatin loops, but not genome compartments, are hallmarks of neuronal differentiation and neuron-related diseases. Interestingly, we observed many neuron-specific enhancer-promoter looping clusters spanning Mb-scale neighborhoods, supporting a phase-separation model leading to enhancer aggregation during brain development. Taken together, our 3D genome analyses shed light on the regulation of brain development and complex neuronal diseases.

2020-06-26 | GSE115407 | GEO

Robust Hi-C Maps of Enhancer-Promoter Interactions Reveal the Function of Non-coding Genome in Neural Development and Diseases.

Project description:Genome-wide mapping of chromatin interactions at high resolution remains experimentally and computationally challenging. Here we used a low-input "easy Hi-C" protocol to map the 3D genome architecture in human neurogenesis and brain tissues and also demonstrated that a rigorous Hi-C bias-correction pipeline (HiCorr) can significantly improve the sensitivity and robustness of Hi-C loop identification at sub-TAD level, especially the enhancer-promoter (E-P) interactions. We used HiCorr to compare the high-resolution maps of chromatin interactions from 10 tissue or cell types with a focus on neurogenesis and brain tissues. We found that dynamic chromatin loops are better hallmarks for cellular differentiation than compartment switching. HiCorr allowed direct observation of cell-type- and differentiation-specific E-P aggregates spanning large neighborhoods, suggesting a mechanism that stabilizes enhancer contacts during development. Interestingly, we concluded that Hi-C loop outperforms eQTL in explaining neurological GWAS results, revealing a unique value of high-resolution 3D genome maps in elucidating the disease etiology.

| S-EPMC7415676 | biostudies-literature

CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C data

Project description:Capture Hi-C (CHi-C) is a state-of-the art method for profiling chromosomal interactions involving targeted regions of interest (such as gene promoters) globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model, and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments, in which many spatially dispersed regions are captured, such as in Promoter CHi-C. We implement these procedures in CHiCAGO (http://regulatorygenomicsgroup.org/chicago), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs. Three human CHi-C biological replicates were generated (comprising 1, 2and 3 technical replicates). Two mouse CHi-C biological replicates were generated (both comprising three technical replicates) and a mouse Hi-C dataset. The publicly available HiCUP pipeline (doi: 10.12688/f1000research.7334.1) was used to process the raw sequencing reads. This pipeline was used to map the read pairs against the mouse (mm9) and human (hg19) genomes, to filter experimental artefacts (such as circularized reads and re-ligations), and to remove duplicate reads. For the CHi-C data, the resulting BAM files were processed into CHiCAGO input files, retaining only those read pairs that mapped, at least on one end, to a captured bait. CHiCAGO then identified Hi-C restriction fragments interacting, with statistical significant, to captured baits.

2016-05-18 | E-GEOD-81503 | biostudies-arrayexpress

Genome-wide maps of nuclear lamina interactions in single human cells. (Hi-C, KBM7 haploid)

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

Hi-C and Capture Hi-C maps in WT and Zfp661 knockout mESCs

Project description:Hi-C and Capture Hi-C maps in WT and Zfp661 knockout mESCs

| PRJNA1079698 | ENA

Promoter capture and whole genome in situ Hi-C of human iPSC and iPSC-derived cardiomyocytes from an Yoruban individual (NA19101)

Project description:We generated Hi-C interaction maps of iPSC and iPSC-derived cardiomyocytes as a resource to help identifying regulatory elements and their target genes in these tissues.

2018-07-11 | E-MTAB-6014 | biostudies-arrayexpress

Hi-C and Capture Hi-C maps in WT and Zfp661 knockout mESCs

Project description:In this assay, we aimed to investigate the impacts of ZFP661 on the permeability of CTCF barriers to chromatin interactions. To achieve this, we generated high-resolution Hi-C maps using WT and Zfp661 knockout (KO) mESCs. Our findings demonstrate that ZFP661 suppresses the trapping of cohesin at the CTCF barriers and modulates their permeability.

2024-02-24 | GSE256450 | GEO

Hi-CoP, a simple and robust assay for detecting interactions of regulatory regions

Project description:First we developed a new, simple, and robust assay called CoP (Column Purified chromatin) for profiling of accessible regions by directly purifying fragmentized crosslinked chromatin with DNA purification column. The CoP chromatin regions by CoP-seq are consistent with the accessible regions detected by ATAC-seq and FARIE-seq. Then we integrated CoP-seq and Hi-C technique (Hi-CoP) for interactions of accessible chromatin regions which represent active cis-regulatory elements in cells. Our data showed that Hi-CoP assay can robustly detect interactions of regulatory regions.

2020-01-29 | GSE144412 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data