Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Evaluation of ultra-low input RNA sequencing for the study of human T cell transcriptome

ABSTRACT: Evaluation of ultra-low input RNA sequencing for the study of human T cell transcriptome

PROVIDER: PRJNA541904 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR9029210_1.fastq.gz	Fastqsanger.gz
	SRR9029210_2.fastq.gz	Fastqsanger.gz
	SRR9029220_1.fastq.gz	Fastqsanger.gz
	SRR9029220_2.fastq.gz	Fastqsanger.gz
	SRR9029230_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 80

Similar Datasets

Evaluation of ultra-low input RNA sequencing for the study of human T cell transcriptome

Project description:Deeper understanding of T cell biology is crucial for the development of new therapeutics. Human naïve T cells have low RNA content and their numbers can be limiting; therefore we set out to determine the parameters for robust ultra-low input RNA sequencing. We performed transcriptome profiling at different cell inputs and compared three protocols: Switching Mechanism at 5’ End of RNA Template technology (SMART) with two different library preparation methods (Nextera (SMART_Nxt) and Clontech (SMART_CC)), and AmpliSeq technology. As the cell input decreased the number of detected coding genes decreased with SMART, while stayed constant with AmpliSeq. However, SMART enables detection of non-coding genes, which is not feasible for AmpliSeq. The detection is dependent on gene abundance, but not transcript length. The consistency between technical replicates and cell inputs was comparable across methods above 1K but highly variable at 100 cell input. Sensitivity of detection for differentially expressed genes decreased dramatically with decreased cell inputs in all protocols, support that additional approaches, such as pathway enrichment, are important for data interpretation at ultra-low input. Finally, T cell activation signature was detected at 1K cell input and above in all protocols, with AmpliSeq showing better detection at 100 cells.

2019-05-24 | GSE130882 | GEO

Haematopoetic RNA-seq from ultra low input

Project description:Haematopoetic RNA-seq from ultra low input

| PRJNA419186 | ENA

Cell-free DNA methylome profiling by MBD-seq with ultra-low input

Project description:Here, we report an enrichment-based ultra-low input cfDNA methylation profiling method using methyl-CpG binding proteins capture, termed cfMBD-seq. We optimized the conditions of cfMBD capture by adjusting the amount of MethylCap protein along with using methylated filler DNA. Our data showed that cfMBD-seq performs equally to the standard MBD-seq (>1000 ng input) even when using 1 ng DNA as the input. cfMBD-seq demonstrated equivalent sequencing data quality as well as similar methylation profile when compared to cfMeDIP-seq. We showed that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of CpG islands. This new bisulfite-free ultra-low input methylation profiling technology has a great potential in non-invasive and cost-effective cancer detection and classification.

2021-01-01 | GSE161331 | GEO

Ultra-low-input native ChIP-seq data of four germ-cell types during mouse spermatogenesis

Project description:We applied ultra-low-input native ChIP-seq and profiled genome-wide H3K9me3 distribution in four germ cell types during spermatogenesis.

2020-07-17 | GSE137742 | GEO

Combined ultra-low input mRNA and whole-genome sequencing from human embryonic stem cells

Project description:Next Generation Sequencing has proven to be an exceptionally powerful tool in the field of genomics and transcriptomics. With recent development it is nowadays possible to analyze ultra-low input sample material down to single cells. Nevertheless investigating such sample material still limits the analysis to either the genome or transcriptome, hence a combined analysis of both types of nucleic acids from the same sample material is still in demand.We developed a protocol which enables the combined analysis of the genome as well as the transcriptome by Next Generation Sequencing from ultra-low input samples. The protocol was evaluated by sequencing sub-colony structures from human embryonic stem cells containing 150 to 200 cells. The method can be adapted to any available sequencing system (e.g. Illumina, SOLiD, 454, etc.).To our knowledge, this is the first report where sub-colonies of human embryonic stem cells have been analyzed both at the genomic as well as transcriptome level. The method of this proof of concept study may find useful practical applications for cases where only a limited number of cells are available, e.g. for tissues samples from biopsies, circulating tumor cells and cells from early embryonic development. The results we present demonstrate that a combined analysis of genomic DNA and messenger RNA from ultra low input samples is feasible and can readily be applied to other cellular systems with limited material available.

2015-11-17 | GSE69471 | GEO

Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples

Project description:RNA-seq is the standard method for profiling gene expression in many biological systems. Due to the wide dynamic range and complex nature of the transcriptome, RNA-seq provides an incomplete characterisation, especially of lowly expressed genes and transcripts. Targeted RNA sequencing (RNA CaptureSeq) focuses sequencing on genes of interest, providing exquisite sensitivity for transcript detection and quantification. However, uses of CaptureSeq have focused on bulk samples and its performance on very small populations of cells is unknown. Here we show CaptureSeq greatly enhances transcriptomic profiling of target genes in ultra-low-input samples and provides equivalent performance to that on bulk samples. We validate the performance of CaptureSeq using multiple probe sets on samples of iPSC-derived cortical neurons. We demonstrate up to 275-fold enrichment for target genes, the detection of 10% additional genes and a greater than 5-fold increase in identified gene isoforms. Analysis of spike-in controls demonstrated CaptureSeq improved both detection sensitivity and expression quantification. Comparison to the CORTECON database of cerebral cortex development revealed CaptureSeq enhanced the identification of sample differentiation stage. CaptureSeq provides sensitive, reliable and quantitative expression measurements on hundreds-to-thousands of target genes from ultra-low-input samples and has the potential to greatly enhance transcriptomic profiling when samples are limiting.

2019-11-16 | GSE140442 | GEO

Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples

Project description:Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples

| PRJNA589751 | ENA

Cell-free DNA methylome profiling by MBD-seq with ultra-low input

Project description:Cell-free DNA methylome profiling by MBD-seq with ultra-low input

| PRJNA677900 | ENA

Development of miniBac-seq to profile bacterial transcriptome from ultra-low input RNA amount

Project description:Development of miniBac-seq to profile bacterial transcriptome from ultra-low input RNA amount

| PRJNA764223 | ENA

Comprehensive analysis of RNA-seq kits for standard, low and ultra-low quantity samples

Project description:High-throughput RNA-sequencing has now become the gold standard method for whole-transcriptome gene expression analysis. It is widely used in a number of applications studying various transcriptomes of cells and tissues. It is also being increasingly considered for a number of clinical applications, including expression profiling for diagnostics or alternative transcripts detection. However, RNA sequencing can be challenging in some situations, for instance due to low input quantities or degraded RNA samples. Several protocols have been proposed to overcome some of these challenges, and many are available as commercial kits. Here we perform a comprehensive testing of three recent commercial technologies for RNA-seq library preparation (Truseq, Smarter and Smarter Ultra-Low) on human reference tissue preparations, for standard (1ug), low (100 and 10 ng) and ultra-low (< 1 ng) input quantities, and for mRNA and total RNA, stranded or unstranded. We analyze the results using read quality and alignments metrics, gene detection and differential gene expression metrics. Overall, we show that the Truseq kit performs well at 100 ng input quantity, while the Smarter kit shows degraded performances for 100 and 10 ng input quantities, and that the Smarter Ultra-Low kit performs quite well for input quantities < 1 ng. All the results are discussed in details, and we provide guidelines for the selection of a RNA-seq library preparation kits by biologists.

2019-05-10 | GSE124198 | 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