Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

ABSTRACT: Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

PROVIDER: PRJEB20128 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	ERR1897930.fastq.gz	Fastqsanger.gz
	ERR1897940.fastq.gz	Fastqsanger.gz
	ERR1897950.fastq.gz	Fastqsanger.gz
	ERR1897960.fastq.gz	Fastqsanger.gz
	ERR1897970.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 576

Similar Datasets

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

Project description:This study aims to assess the reliability of spike-in normalization for analyzing single-cell RNA sequencing data. This is done by performing mixture experiments where two different sets of spike-in RNA (ERCC and SIRV) are added separately to a single mouse cell (416B or trophoblast stem cells (TSCs)), followed by generation of sequencing libraries using a modified version of the Smart-seq2 protocol. The aim is to measure the variance of the log-ratio of the total counts between the two spike-in sets. This will quantify how precisely the spike-in RNA was added to each well. As a control, addition was also performed with a premixed solution of both spike-ins, to quantify the variability in the log-ratios due to the experimental protocol. The same data can also be used to measure the well-to-well variability in the differences in behaviour between the two spike-in sets. The data contain four batches of libraries (block), using different batches of cells that were processed and sequenced separately. Each batch contains libraries with all three types of spike-in addition (ERCC+SIRV, SIRV+ERCC or Premixed). The 416B cells also contain a CBFB-MYH11 oncogene, which is expressed in half of the cells (Induced) and silent in the other half (Control).

2017-05-31 | E-MTAB-5522 | biostudies-arrayexpress

"The Wild West of Spike-in Normalization": Benchmarking the ChIP-seq spike-in normalization method

Project description:Spike-in normalization is a powerful approach to assess global changes in data obtained from genomic mapping of DNA-associated proteins by methods such as ChIP-sequencing (ChIP-seq) or CUT&RUN. While multiple spike-in methods provide detailed documentation, the implementation of these approaches often omit critical quality control steps and veer from the established procedures. Here, we show that proper application of spike-in normalization can increase quantification accuracy across a spectrum of conditions. Next, we outline how misuse of spike-in approaches can create erroneous biological interpretations and provide guidelines to minimize pitfalls when applying this approach to normalize data from protein-DNA interaction results.

2024-08-05 | GSE273915 | GEO

"The Wild West of Spike-in Normalization": Benchmarking the ChIP-seq spike-in normalization method

Project description:"The Wild West of Spike-in Normalization": Benchmarking the ChIP-seq spike-in normalization method

| PRJNA1144087 | ENA

Novel small RNA spike-in oligonucleotides enable absolute normalization of small RNA-Seq data

Project description:Normalization of high-throughput small RNA sequencing (sRNA-Seq) data is required to compare sRNA levels across different samples. Commonly used relative normalization approaches can cause erroneous conclusions due to fluctuating small RNA populations between tissues. We developed a set of sRNA spike-in oligonucleotides (sRNA spike-ins) that enable absolute normalization of sRNA-Seq data across independent experiments, as well as the genome-wide estimation of sRNA:mRNA stoichiometries when used together with mRNA spike-in oligonucleotides.

2017-07-19 | GSE98553 | GEO

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.

Project description:By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell and to scale the observed expression values so that the coverage of spike-in transcripts is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behavior between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses, such as detection of highly variable genes and clustering. Our results suggest that scaling normalization using spike-in transcripts is reliable enough for routine use in single-cell RNA sequencing data analyses.

| S-EPMC5668938 | biostudies-literature

Assessing absolute microbial abundancies by spike-in based calibration

Project description:Adding a new perspective to microbiome profiling: Assessing absolute microbial abundancies by spike-in based calibration

| PRJEB11953 | ENA

ChIP-seq data processing and relative and quantitative signal normalization for Saccharomyces cerevisiae

Project description:Chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) is a widely used technique for genome-wide analyses of protein-DNA interactions. This protocol provides a guide to ChIP-seq data processing in Saccharomyces cerevisiae, with a focus on signal normalization to address data biases and enable meaningful comparisons within and between samples. Designed for researchers with minimal bioinformatics experience, it includes practical overviews and refers to scripting examples for key tasks, such as configuring computational environments, trimming and aligning reads, processing alignments, and visualizing signals. This protocol employs the sans-spike-in method for quantitative ChIP-seq (siQ-ChIP) and normalized coverage for absolute and relative comparisons of ChIP-seq data, respectively. While spike-in normalization, which is semiquantitative, is addressed for context, siQ-ChIP and normalized coverage are recommended as mathematically rigorous and reliable alternatives.

2025-02-02 | GSE288548 | GEO

Assessing placental maturity through histological and transcriptomic analyses in idiopathic spontaneous preterm birth

Project description:Assessing placental maturity through histological and transcriptomic analyses in idiopathic spontaneous preterm birth

2019-12-25 | GSE118442 | GEO

Use of synthetic circular RNA spike-ins (SynCRS) for normalization of circular RNA sequencing data.

Project description:Use of synthetic circular RNA spike-ins (SynCRS) for normalization of circular RNA sequencing data.

| PRJNA1089133 | ENA

Global ChIP-seq Normalization Reveals Epigenome Modulations

Project description:Epigenomic profiling by ChIP-seq is a prevailing methodology used to investigate chromatin-based regulation in biological systems, such as human disease, yet the lack of an empirical methodology to normalize amongst experiments has limited the usefulness of this technique. Here we describe a “spike-in” normalization method that allows the quantitative comparison of histone modification status across cell populations using defined quantities of a reference epigenome. We demonstrate the utility of this method in measuring epigenomic changes following chemical perturbations and show how control normalization of ChIP-seq experiments enables discovery of disease-relevant changes in histone modification occupancy.

2014-10-30 | GSE60104 | 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