Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

FloChIP's histone mark data obtained in multiplexing mode

ABSTRACT: FloChIP's histone mark data obtained in multiplexing mode

PROVIDER: PRJEB28149 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	ERR2732000_1.fastq.gz	Fastqsanger.gz
	ERR2732000_2.fastq.gz	Fastqsanger.gz
	ERR2732001_1.fastq.gz	Fastqsanger.gz
	ERR2732001_2.fastq.gz	Fastqsanger.gz
	ERR2732002_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 10

Similar Datasets

FloChIP's histone mark data obtained in multiplexing mode

Project description:FloChIP's histone mark data obtained in multiplexing mode

| PRJEB35683 | ENA

FloChIP's decreasing cell number data obtained in multiplexing mode

Project description:FloChIP's decreasing cell number data obtained in multiplexing mode

| PRJEB28205 | ENA

FloChIP's histone mark data obtained in multiplexing mode

Project description:By using FloChIP’s “sample multiplex” mode, we ChIPed in parallel 5 histone marks (H3K27ac, H3K4me3, H3K4me1 and H3K9me3), going from chromatin to sequencing-ready libraries, in just one day.

2019-12-08 | E-MTAB-8551 | biostudies-arrayexpress

FloChIP's histone mark data obtained in multiplexing mode

Project description:By using FloChIP’s “sample multiplex” mode, we ChIPed in parallel 5 histone marks (H3K27ac, H3K4me3, H3K27me3, H3K4me1 and H3K9me3), going from chromatin to sequencing-ready libraries, in just one day.

2020-06-26 | E-MTAB-7122 | biostudies-arrayexpress

FloChIP's decreasing cell number data obtained in multiplexing mode

Project description:By using FloChIP’s “sample multiplex” mode, we ChIPed in parallel 4 different cell dilution (100k cells, 50k cells, 5k cells, and 500 cells), going from chromatin to sequencing-ready libraries, in just one day.

2019-11-26 | E-MTAB-7150 | biostudies-arrayexpress

FloChIP's MEF2-A data obtained with high-throughput chip

Project description:FloChIP's MEF2-A data obtained with high-throughput chip

| PRJEB28685 | ENA

FloChIP's sequential chip data

Project description:FloChIP's sequential chip data

| PRJEB28503 | ENA

Comparative analysis of single-cell RNA sequencing methods with and without sample multiplexing

Project description:Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10X Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10X data where samples are not multiplexed. Despite a relatively lower library and cell capture efficiencies, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.

2024-03-24 | GSE246624 | GEO

CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics

Project description:High-throughput single-cell assays increasingly require special consideration in experimental design, sample multiplexing, batch effect removal, and data interpretation. Here, we describe a lentiviral barcode-based multiplexing approach, CellTag Indexing, which uses predefined genetic barcodes that are also heritable, enabling cell populations to be tagged, pooled, and tracked over time in the same experimental replicate. We demonstrate the utility of CellTag Indexing by sequencing transcriptomes using a variety of cell types, including long-term tracking of cell engraftment and differentiation in vivo. Together, this presents CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell technologies.

2019-04-20 | GSE130065 | GEO

Multiplexing droplet-based single cell RNA-sequencing using genetic barcodes

Project description:Here, we introduce an in-silico algorithm demuxlet that harnesses naturally occurring genetic variation in a pool of cells from unrelated individuals to discover the sample identity of each cell and identify droplets containing cells from two different individuals (doublets). These two capabilities enable a simple multiplexing design that increases single cell library construction throughput by experimental design where cells from genetically diverse samples are multiplexed and captured at 2-10x over standard workflows. We further demonstrate the utility of sample multiplexing by characterizing the interindividual variability in cell type-specific responses of ~15k PBMCs to interferon-beta, a potent cytokine. Our computational tool enables sample multiplexing of droplet-based single cell RNA-seq for large-scale studies of population variation and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

2017-06-13 | GSE96583 | 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