Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MDRIP-seq is a high-throughput method for quantitative R-loop landscape profiling

ABSTRACT: Current methods for R-loop profiling need to perform experiments for each sample individually, with consequent limitations in throughput. Here, based on the barcoding strategy, we develop mDRIP-seq, a high-throughput method showing equivalent performance as conventional methods, but with merits of 7-fold less cost and 6-fold less hand-on time per sample. We also show the simplicity and effectiveness of mDRIP-seq for relative and absolute quantitation of genomic R-loop fractions for multiple samples. Together, mDRIP-seq is a high-throughput and cost-efficient method for R-loop mapping and quantitative assessment that can be widely applied to large-scale dynamic profiles of these important structures for diverse organisms.

ORGANISM(S): Oryza sativa Mus musculus Escherichia coli Arabidopsis thaliana Saccharomyces cerevisiae Homo sapiens

PROVIDER: GSE219071 | GEO | 2024/06/11

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

ssDRIP-seq: a high-throughput method for R-loop mapping and quantitative assessment

Project description:Current methods for R-loop mapping need to perform DNA:RNA immunoprecipitation for each sample individually, with consequent limitations in throughput. Here, we develop and validate mDRIP-seq, a multi-sample barcoding and pooling method for R-loop mapping. We show mDRIP-seq performs equivalently as conventional methods, but with the merits of high throughput and cost-efficiency. We also show the simplicity of mDRIP-seq for relative and absolute quantitation of genomic R-loop fractions for multiple samples. Together, mDRIP-seq is a high-throughput and cost-efficient method for R-loop mapping and quantitative assessment and can be widely applied to large-scale dynamic profiles of these important structures for diverse organisms.

2024-06-12 | GSE219069 | GEO

mDRIP-seq: a high-throughput method for R-loop mapping and quantitative assessment.

2024-06-12 | GSE246423 | GEO

RNA-seq for HEK293T: a high-throughput method for R-loop mapping and quantitative assessment

2024-06-12 | GSE219070 | GEO

mDRIP-seq: a high-throughput method for R-loop mapping and quantitative assessment

2024-06-11 | GSE219068 | GEO

mDRIP-seq is a high-throughput method for quantitative R-loop landscape profiling

Project description:mDRIP-seq is a high-throughput method for quantitative R-loop landscape profiling

| PRJNA906979 | ENA

mDRIP-seq: a high-throughput method for R-loop mapping and quantitative assessment

Project description:mDRIP-seq: a high-throughput method for R-loop mapping and quantitative assessment

| PRJNA906980 | ENA

mDRIP-seq: a high-throughput method for R-loop mapping and quantitative assessment.

Project description:mDRIP-seq: a high-throughput method for R-loop mapping and quantitative assessment.

| PRJNA1032915 | ENA

Ultrasonic-based filter aided sample preparation as the general method to sample preparation in proteomics

Project description:In this work, we propose a new high-throughput ultrafast method for large scale proteomics approaches by speeding the classic filter aided sample preparation protocol, FASP. The new US-FASP method matches the analytical minimalism roles as time, cost, sample requirement, reagent consumption, energy requirements and production of waste products are reduced to a minimum while maintaining high sample throughput in a robust manner as all the advantages of the filter aided sample preparation protocol are maintained.

2020-06-08 | PXD018360 | Pride

TIME-Seq Enables Scalable and Inexpensive Epigenetic Age Predictions

Project description:We report a high-throughput and low-cost targeted bisulfite sequencing method called TIME-Seq for discovery and assay of DNA methylation clocks. Data is related to the initial publication of TIME-Seq clocks and shallow sequencing-based predictions using scAge. Demultiplexing information as well as more detailed metadata for each sample in the pools will be posted at: https://github.com/patricktgriffin/TIME-Seq

2023-10-12 | GSE232346 | GEO

GASP and FASP are complementary for LC-MS/MS proteomic analysis of drug-metabolising enzymes and transporters in pig liver

Project description:Sample preparation is a critical step in the proteomic workflow. Numerous different approaches are used, tailored to the type of sample, the aims of the experiment, analytical method, and to an extent, user preference. This has resulted in large variation in reported protein abundances. In this study we demonstrate the complementarity of two different sample preparation techniques for the study of absorption, distribution, metabolism and excretion (ADME) related proteins from pig liver tissue. Filter-aided sample preparation (FASP) is a well-established and widely used method, whilst gel-aided sample preparation (GASP) is a relatively new method optimised and simplified from previous gel-associated digestion techniques. To investigate each method the number of peptides and proteins characterised, reproducibility of results and their real-time application were examined. Whilst both methods have their merits and limitations, for example, FASP is the less technical of the two methods, whilst GASP is time efficient, ultimately the two methods show significant differences in the peptides identified and therefore the use of both methods should be considered when examining and quantifying ADME related proteins.

2018-11-12 | PXD011324 | Pride

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