Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (CEL-Seq2)

ABSTRACT: Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (CEL-Seq2)

PROVIDER: PRJNA551749 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR9621730_1.fastq.gz	Fastqsanger.gz
	SRR9621730_2.fastq.gz	Fastqsanger.gz
	SRR9621740_1.fastq.gz	Fastqsanger.gz
	SRR9621740_2.fastq.gz	Fastqsanger.gz
	SRR9621731_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 30

Similar Datasets

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (QUARTZ-Seq2)

Project description:Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (QUARTZ-Seq2)

| PRJNA551754 | ENA

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (CEL-Seq2)

Project description:Single-cell RNA sequencing (scRNA-seq) is the leading technique for charting the molecular properties of individual cells. The latest methods are scalable to thousands of cells, enabling in- depth characterization of sample composition without prior knowledge. However, there are important differences between scRNA-seq techniques, and it remains unclear which are the most suitable protocols for drawing cell atlases of tissues, organs and organisms. We have generated benchmark datasets to systematically evaluate techniques in terms of their power to comprehensively describe cell types and states. We performed a multi-center study comparing 13 commonly used single-cell and single-nucleus RNA-seq protocols using a highly heterogeneous reference sample resource. Comparative and integrative analysis at cell type and state level revealed marked differences in protocol performance, highlighting a series of key features for cell atlas projects. These should be considered when defining guidelines and standards for international consortia, such as the Human Cell Atlas project.

2019-12-10 | GSE133539 | GEO

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects

Project description:Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects

| PRJNA551748 | ENA

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (SingleNuclei)

Project description:Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (SingleNuclei)

| PRJNA551757 | ENA

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (C1HT)

Project description:Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (C1HT)

| PRJNA551747 | ENA

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (ICELL8)

Project description:Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (ICELL8)

| PRJNA551751 | ENA

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (ddSEQ)

Project description:Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (ddSEQ)

| PRJNA551758 | ENA

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (inDrop)

Project description:Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (inDrop)

| PRJNA551759 | ENA

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects (QUARTZ-Seq2)

2019-12-10 | GSE133543 | GEO

CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

Project description:Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with three-fold higher sensitivity, lower costs, and less hands-on time. We also implemented CEL-Seq2 on Fluidigm’s C1 system, thereby providing its first single-cell, on-chip barcoding method, and detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use

2016-04-08 | GSE78779 | 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