Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Robust single-cell DNA methylome profiling with snmC-seq2

ABSTRACT: Robust single-cell DNA methylome profiling with snmC-seq2

PROVIDER: PRJNA447435 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR6911630_1.fastq.gz	Fastqsanger.gz
	SRR6911630_2.fastq.gz	Fastqsanger.gz
	SRR6911640_1.fastq.gz	Fastqsanger.gz
	SRR6911640_2.fastq.gz	Fastqsanger.gz
	SRR6911650_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 498

Similar Datasets

Robust single-cell DNA methylome profiling with snmC-seq2

Project description:Single-cell DNA methylome profiling has enabled the study of epigenomic heterogeneity in complex tissues and during cellular reprogramming. However, broader applications of the method have been impeded by the modest quality of sequencing libraries. Here we report snmC-seq2, which provides improved read mapping, reduced artificial reads, enhanced throughput, and increased library complexity compared to snmC-seq. snmC-seq2 is an efficient strategy suited for large scale single-cell epigenomic studies.

2018-09-20 | GSE112471 | GEO

Robust single-cell DNA methylome profiling with snmC-seq2.

Project description:Single-cell DNA methylome profiling has enabled the study of epigenomic heterogeneity in complex tissues and during cellular reprogramming. However, broader applications of the method have been impeded by the modest quality of sequencing libraries. Here we report snmC-seq2, which provides improved read mapping, reduced artifactual reads, enhanced throughput, as well as increased library complexity and coverage uniformity compared to snmC-seq. snmC-seq2 is an efficient strategy suited for large-scale single-cell epigenomic studies.

| S-EPMC6147798 | biostudies-other

Simultaneous profiling of transcriptome and DNA methylome from a single cell

Project description:We developed a new single cell sequencing method to simultaneously sequence methylome and transcriptome for mouse DRG neurons Integrative analysis of transcription and methylation at single cell level

2016-04-12 | E-GEOD-76483 | biostudies-arrayexpress

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

Simultaneous profiling of transcriptome and DNA methylome from a single cell

Project description:We developed a new single cell sequencing method to simultaneously sequence methylome and transcriptome for mouse DRG neurons

2016-04-12 | GSE76483 | GEO

Single cell profiling of mouse blood cells using CEL-seq2

Project description:Single cell profiling of mouse blood cells using CEL-seq2

| PRJNA432538 | ENA

Single cell profiling of mouse blood cells using CEL-seq2

Project description:Gene expression in mouse blood cells from sorted via FACS analysis into a 384-well plate were profiled using a modified CEL-seq2 protocol.

2018-02-28 | GSE109999 | GEO

Laser-capture microscopy coupled with Smart-seq2 (LCM-seq) for robust and efficient transcriptomic profiling of mouse and human tissues

Project description:Generally, when LCM is used in diverse transcriptomic analyses, several hundred, if not thousands, of cells are needed to obtain high quality of RNA-seq data. As some cellular populations are very small and tissue often in scarcity, we aimed to carefully document the lowest number of cells needed to retrieve sequencable libraries. We started with capturing 120 cells and subsequently scaled down to 50 cells, 30 cells, 10 cells, 5 cells, 2 cells and finally 1 cell. By optimizing multiple steps in the procedure, including direct lysis of cells without performing RNA isolation, we developed LCM-seq that couples LCM with Smart-seq2 for robust and efficient polyA-based RNA sequencing. We applied LCM-seq to mouse and human neuron samples, and demonstrated that LCM-seq can allow us to acquire high quality RNA-seq data from mouse and human tissues to conduct various transcriptomic studies. Developing new sequencing technology LCM-seq to efficiently sequence mouse and human tissues

2016-07-05 | E-GEOD-76514 | biostudies-arrayexpress

Simultaneous assessment of human genome and methylome data in a single experiment using limited deamination of methylated cytosine

Project description:Multi-omics requires concerted recording of independent information, ideally from a single experiment. In this study, we introduce RIMS-seq2, a high-throughput technique to simultaneously sequence genomes and overlay methylation information while requiring only a small modification of the experimental protocol for high throughput DNA sequencing to include a controlled deamination step. Importantly, the rate of deamination of 5mC is negligible and thus, do not interfere with standard DNA sequencing and data processing. Thus, RIMS-seq2 libraries from whole or targeted genome sequencing show the same germline variation calling accuracy and sensitivity as compared to standard DNA-seq. Additionally, regional methylation levels provide an accurate map of the human methylome.

2023-06-19 | GSE234235 | GEO

Single cell RNA-sequencing of Spike-ins and mESC using Smart-Seq2 on C1 System

Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we perform Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit

2017-03-29 | E-MTAB-5485 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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