Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genome wide mapping in regulatory T cells at steady state

ABSTRACT: Genome wide mapping in regulatory T cells at steady state

PROVIDER: PRJNA494808 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR7968140_1.fastq.gz	Fastqsanger.gz
	SRR7968140_2.fastq.gz	Fastqsanger.gz
	SRR7968150_1.fastq.gz	Fastqsanger.gz
	SRR7968150_2.fastq.gz	Fastqsanger.gz
	SRR7968290.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 84

Similar Datasets

Genome wide mapping of chromatin accessibility in Treg cells at steady state

Project description:Genome wide mapping of chromatin accessibility in Treg cells at steady state

| PRJNA494806 | ENA

Transcriptome analysis of Bcl11b-deficient regulatory T cells at steady state

Project description:Transcriptome analysis of Bcl11b-deficient regulatory T cells at steady state

| PRJNA494812 | ENA

Genome wide mapping of NFAT1 binding sites in dendritic cells and of chromatin state in steady and dectin-1 activated dendritic cell line D1

Project description:We generated genome-wide chromatin-state maps of mouse dendritic cells in steady and dectin-1 activated with or without NFAT inhibitor FK506. We found the differential H3K4me3 signatures in these cells. This study provides the epigenetic signatures of steady, dectin-1activated with or without NFAT inhibition. We also generated stable dendritic cell line D1 expressing a V5 tagging NFAT1. This cell line was used to map the global binding sites of NFAT1 upon dectin-1 activation with 10ug/ml curdlan for 30 minutes. examine NFAT1-v5 genome wide binding sites and differential H3K4me3 epigenetic modification in steady, dectinn-1 activated with or without NFAT inhibiton.

2015-01-05 | E-GEOD-59998 | biostudies-arrayexpress

Steady-state expression of Giardia lamblia transcripts

Project description:Steady-state expression of Giardia lamblia transcripts

| PRJNA664462 | ENA

Immune landscape of mouse heart in steady state

Project description:To reveal the cardiac immune landscape of mouse heart in steady state, we performed single-cell RNA-sequencing (scRNA-seq) with heart tissue from wide type mice and immune subsets were analyzed to reveal cardiac immune features.

2023-07-01 | GSE230192 | GEO

Genome wide mapping of NFAT1 binding sites in dendritic cells and of chromatin state in steady and dectin-1 activated dendritic cell line D1

2015-01-05 | GSE59998 | GEO

Inferring gene regulation from stochastic transcriptional variation across single cells at steady-state

Project description:Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady-state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful co-fluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady-state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

2022-08-04 | GSE202292 | GEO

Integrated genome-wide analysis of transcription factor occupancy, RNA polymerase II binding and steady-state RNA levels identify differentially regulated functional gene classes

Project description:Routine methods for assaying steady-state mRNA levels such as RNA-seq and micro-arrays are commonly used as readouts to study the role of transcription factors (TFs) in gene expression regulation. However, cellular RNA levels do not solely depend on activity of TFs and subsequent transcription by RNA polymerase II (Pol II), but are also affected by RNA turnover rate. Here, we demonstrate that integrated analysis of genome-wide TF occupancy, Pol II binding and steady-state RNA levels provide important insights in gene regulatory mechanisms. Pol II occupancy, as detected by Pol II ChIP-seq, was found to correlate better with TF occupancy compared to steady-state RNA levels and is thus a more precise readout for the primary transcriptional mechanisms that are triggered by signal transduction. Furthermore, analysis of differential Pol II occupancy and RNA-seq levels identified genes with high Pol II occupancy and relatively low RNA levels and vice versa. These categories are strongly enriched for genes from different functional classes. Our results demonstrate a complementary value in Pol II chip-seq and RNA-seq approaches for better understanding of gene expression regulation. 3 replicates of RNAPII ChIP-seq and RNA-seq under normal and Wnt suppressed conditions

2012-03-09 | E-GEOD-36349 | biostudies-arrayexpress

Chromatin based modeling of transcription rates identifies the contribution of different regulatory layers to steady-state mRNA levels.

Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA. Strand specific expression profiling by high throughput sequencing.

2012-09-13 | E-GEOD-34473 | biostudies-arrayexpress

Immune landscape of mouse heart in steady state

Project description:Immune landscape of mouse heart in steady state

| PRJNA957810 | ENA