Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Smart-RRBS for single cell methylome and transcriptome analysis [scRNAseq]

ABSTRACT: Smart-RRBS for single cell methylome and transcriptome analysis [scRNAseq]

PROVIDER: PRJNA660166 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR12545800_1.fastq.gz	Fastqsanger.gz
	SRR12545800_2.fastq.gz	Fastqsanger.gz
	SRR12545801_1.fastq.gz	Fastqsanger.gz
	SRR12545801_2.fastq.gz	Fastqsanger.gz
	SRR12545802_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 192

Similar Datasets

Smart-RRBS for single cell methylome and transcriptome analysis [scRNAseq]

Project description:The integration of DNA methylation and transcriptional state within single cells is of broad interest. Several single-cell dual- and multi-omics approaches have been reported recently that will enable further investigation into cellular heterogeneity including the discovery and in depth study of rare cell populations. Such analyses will continue to provide important mechanistic insights into the regulatory consequences of epigenetic modifications. Last year we reported a new method for profiling the DNA methylome and transcriptome from the same single cell. Here we present details of the protocol and provide guidance on its utility. Our Smart-RRBS protocol entails physically separating mRNA and genomic DNA and combines Smart-Seq2 and Reduced Representation Bisulfite Sequencing (RRBS). It generates paired epigenetic promoter and RNA-expression measurements for a substantial fraction (approximately one-fourth) of protein-coding genes in a typical single cell. It also works for micro-dissected tissue samples comprising hundreds of cells. The protocol, excluding sequencing, takes ~3 days to process up to 192 samples manually

2021-04-15 | GSE157113 | GEO

Smart-RRBS for single cell methylome and transcriptome analysis [scRRBS]

2021-04-15 | GSE157114 | GEO

Smart-RRBS for single cell methylome and transcriptome analysis

Project description:Smart-RRBS for single cell methylome and transcriptome analysis

| PRJNA660164 | ENA

Smart-RRBS for single cell methylome and transcriptome analysis [scRRBS]

Project description:Smart-RRBS for single cell methylome and transcriptome analysis [scRRBS]

| PRJNA660167 | ENA

Smart-RRBS for single-cell methylome and transcriptome analysis.

Project description:The integration of DNA methylation and transcriptional state within single cells is of broad interest. Several single-cell dual- and multi-omics approaches have been reported that enable further investigation into cellular heterogeneity, including the discovery and in-depth study of rare cell populations. Such analyses will continue to provide important mechanistic insights into the regulatory consequences of epigenetic modifications. We recently reported a new method for profiling the DNA methylome and transcriptome from the same single cells in a cancer research study. Here, we present details of the protocol and provide guidance on its utility. Our Smart-RRBS (reduced representation bisulfite sequencing) protocol combines Smart-seq2 and RRBS and entails physically separating mRNA from the genomic DNA. It generates paired epigenetic promoter and RNA-expression measurements for ~24% of protein-coding genes in a typical single cell. It also works for micro-dissected tissue samples comprising hundreds of cells. The protocol, excluding flow sorting of cells and sequencing, takes ~3 d to process up to 192 samples manually. It requires basic molecular biology expertise and laboratory equipment, including a PCR workstation with UV sterilization, a DNA fluorometer and a microfluidic electrophoresis system.

| S-EPMC8672372 | biostudies-literature

Smart-RRBS for single cell methylome and transcriptome analysis

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-04-15 | GSE157115 | GEO

Oryza sativa ssp. indica Illumina libraries of the method plant-RRBS used for methylome profiling

Project description:The contained data consist of Illumina HiSeq 2500 reads generated from restriction endonuclease digested genomic DNA of Oryza sativa ssp. indica used as proof of concept for plant-RRBS methylome profiling by bioinformatics analyses. Five biological repeats for an inbred control line and a derived epiline of the 4th generation where analysed using two restriction endonuclease combination (MspI-DpnII or MspI-ApekI).

2017-12-14 | E-MTAB-4626 | biostudies-arrayexpress

Improved Smart-Seq for sensitive full-length transcriptome profiling in single cells

Project description:Improved Smart-Seq for sensitive full-length transcriptome profiling in single cells. Cells of four different origins were profiled using commercial SMARTer and compared to five variants of an improved protocol (Smart-Seq2).

2013-09-01 | E-GEOD-49321 | biostudies-arrayexpress

GBM CpG methylome

Project description:RRBS methylome data from Glioblastoma samples

| PRJEB38380 | ENA

Q-RRBS: a quantitative reduced representation bisulfite sequencing method for single-cell methylome analyses.

Project description:Reduced representation bisulfite sequencing (RRBS) is a powerful method of DNA methylome profiling that can be applied to single cells. However, no previous report has described how PCR-based duplication-induced artifacts affect the accuracy of this method when measuring DNA methylation levels. For quantifying the effects of duplication-induced artifacts on methylome profiling when using ultra-trace amounts of starting material, we developed a novel method, namely quantitative RRBS (Q-RRBS), in which PCR-induced duplication is excluded through the use of unique molecular identifiers (UMIs). By performing Q-RRBS on varying amounts of starting material, we determined that duplication-induced artifacts were more severe when small quantities of the starting material were used. However, through using the UMIs, we successfully eliminated these artifacts. In addition, Q-RRBS could accurately detect allele-specific methylation in absence of allele-specific genetic variants. Our results demonstrate that Q-RRBS is an optimal strategy for DNA methylation profiling of single cells or samples containing ultra-trace amounts of cells.

| S-EPMC4622980 | biostudies-literature

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