Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Study to evaluate the effect of technical repetition on gene expression measurements

ABSTRACT: Study to evaluate the effect of technical repetition on gene expression measurements

PROVIDER: PRJNA531726 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Study to evaluate the effect of technical repetition on gene expression measurements

Project description:To evaluate the effect of technical repetition on gene expression measurements, we repeated the laboratory protocol at 6 different steps. Information was used to select probesets with a high technical reproducibility for the development of gene signatures.

2019-05-30 | GSE129558 | GEO

Study to evaluate the effect of intratumoral heterogeneity on gene expression measurements

Project description:Study to evaluate the effect of intratumoral heterogeneity on gene expression measurements

| PRJNA531723 | ENA

Study to evaluate the effect of intratumoral heterogeneity on gene expression measurements

Project description:To evaluate the effect of intratumorally heterogeneity on gene expression measurements, we profiled and compared samples from different macroscopic tumor areas. Information was used to select probe sets with a high spatial reproducibility for the development of gene signatures.

2019-05-30 | GSE129557 | GEO

Study to evaluate the effect of the type of tissue on gene expression measurements

Project description:To evaluate the effect of tissue type (cytology vs. surgical tissue) on gene expression measurements. Information was used to select probesets with a high spatial reproducibility for the development of gene signatures.

2019-05-30 | GSE129559 | GEO

Study to evaluate the effect of the type of tissue on gene expression measurements

Project description:Study to evaluate the effect of the type of tissue on gene expression measurements

| PRJNA531730 | ENA

Study for evaluating the effect of different microarray platforms on gene expression measurements

Project description:Study for evaluating the effect of different microarray platforms on gene expression measurements

| PRJNA531720 | ENA

BEARscc: robustness of single-cell clusters determined using simulated technical replicates

Project description:Technical variance is a major confounding factor in single-cell RNA sequencing, not least because measurements on the same cell are not replicable. We developed BEARscc, a tool that simulates experiment-specific technical replicates based on a probabilistic model of technical variance trained on RNA spike-in measurements. We demonstrate that the tool improves the unsupervised classification of cells and aids the interpretation of single-cell RNA-seq experiments.

2018-04-03 | GSE95155 | GEO

Technical controls MiCA

Project description:Technical controls MiCA

| PRJEB30293 | ENA

The effect of sample splitting and technical replication on mRNA-seq and sRNA-seq quantification

Project description:RNA sequencing (RNA-seq) is a widely used method for quantifying RNA levels across the environmental, biological and medical sciences. The accuracy of the output from an RNA-seq experiment is known to vary due to sequencing biases or errors in quantification. These have the potential to lead to false calls of differential expression (DE), and hence, to affect the accuracy of the biological inference. A proposed solution to reduce the number of false positives and increase confidence in the quality of results from such experiments is to increase the number of biological replicates. In addition, more recent suggestions are to create additional technical replicates within biological replicates (i.e. to split samples across sequencing lanes). The optimal strategy for analysing and normalizing such data, and for maximising accuracy as a function of cost, biological and technical replication is important to understand, yet currently unclear. The aim of this study was to test the effect of technical replication and sample splitting on the overall outcome of gene expression profiling for RNA-seq data.

2018-12-15 | GSE85806 | GEO

Protocol Dependence of Sequencing-based Gene Expression Measurements

Project description:RNA samples from human tissues (brain and liver) and cell lines (K562 and HL60) were used to assess RNA fragmentation, RNA fractionation, cDNA synthesis, and single versus multiple tag counting. Technical replicates and different cDNA synthesis protocols were compared. Though protocols employing polyA RNA selection generate the highest number of non-ribosomal reads and the most precise measurements for coding transcripts, such protocols were found to detect only a fraction of the non-ribosomal RNA in human cells. PolyA RNA excludes thousands of annotated and even more unannotated transcripts, resulting in an incomplete view of the transcriptome. Ribosomal-depleted RNA provides a more cost-effective method for generating complete transcriptome coverage. Expression measurements using single tag counting provided advantages for assessing gene expression and for detecting short RNAs relative to multi-read protocols. Detection of short RNAs was also hampered by RNA fragmentation.

2011-03-24 | GSE28123 | 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