Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Comparison of RNA Isolation Methods in Yeast on RNA-Seq: Implications for Differential Expression and Meta-Analyses

ABSTRACT: Comparison of RNA Isolation Methods in Yeast on RNA-Seq: Implications for Differential Expression and Meta-Analyses

PROVIDER: PRJNA558910 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR9908460.fastq.gz	Fastqsanger.gz
	SRR9908470.fastq.gz	Fastqsanger.gz
	SRR9908480.fastq.gz	Fastqsanger.gz
	SRR9908461.fastq.gz	Fastqsanger.gz
	SRR9908471.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 24

Similar Datasets

Comparison of RNA Isolation Methods in Yeast on RNA-Seq: Implications for Differential Expression and Meta-Analyses

Project description:Based on the different chemistries of phenol extraction of RNA versus commercial kits, we hypothesized that certain species of mRNA may be preferentially extracted depending upon method, which could masquerade as differential expression in downstream RNA-seq experiments. We tested this using Saccharomyces cerevisiae samples that only differ in the RNA isolation method: a "standard" hot phenol extraction, versus two different commercial RNA isolation kits. While the kits had comparable relative mRNA abundances, samples isolated with hot phenol had higher relative abundance of mRNAs encoding membrane proteins. We hypothesize that hot phenol better solubilizes mRNAs associated with cellular membranes. We then compared the effects of each RNA isolation method on the ability to identify differentially expressed transcripts, using the yeast heat shock response a test case. The method of RNA isolation had little effect on the ability to identify differentially expressed transcripts. Thus, experiments within a single lab are unlikely to be affected by the choice of RNA isolation method as long as the same method is used throughout an experiment. For meta-analyses however, researchers should be cautious if trying to compare experiments where the RNA isolation methods differ.

2019-08-07 | GSE135430 | GEO

RNA Isolation Methods - Diverse Biofluids

Project description:RNA Isolation Methods - Diverse Biofluids

| PRJNA1013972 | ENA

RNA Isolation Methods - Diverse Biofluids

Project description:To evaluate performance of different extra cellular RNA isolation methods in biofluids

2023-09-30 | GSE242613 | GEO

Colorectal cancer cell supermeres isolation methods

Project description:Colorectal cancer cell supermeres isolation methods

| PRJNA1136963 | ENA

Comparison of EVs isolation methods for miRNAs sequencing

Project description:Comparison of EVs isolation methods for miRNAs sequencing

| PRJNA928492 | ENA

Comparison of EVs isolation methods for miRNAs sequencing

Project description:Background & aims: MicroRNAs (miRNAs) encapsulated in EVs are potential diagnostic and prognostic biomarkers. However, discrepancies on miRNA patterns and their validation are still frequent due to differences in sample origin, EVs isolation, miRNA extraction and sequencing methods. Selecting appropriate EVs isolation methods is therefore a critical step for miRNA-based biomarker discovery. The aim of the present study is to find the most suitable EVs isolation method for miRNAs sequencing adequate for clinical application. Material & Methods EVs were isolated by Size Exclusion Chromatography (SEC), iodixanol gradients (GRAD) and the combination of both (SEC+GRAD), using the same plasma sample, in triplicate isolation assays. Isolated EVs were characterized and RNA was extracted. Three different protocols for miRNA library preparation were compared (NEBNext, NEXTFlex and SMARTer smRNA-seq) and miRNAs encapsulated on EVs were sequenced using NextSeq 500 system (Illumina). Finally, the yield, abundance and diversity of miRNAs using the three different EVs isolation protocols were analyzed and compared between them. Results The majority of lipoproteins, total cholesterol and plasma proteins were removed from the EVs-containing fractions by using SEC, GRAD, and SEC+GRAD. SEC method recovered a larger amount of EVs followed by GRAD and SEC+GRAD, while GRAD and SEC+GRAD yielded the purest vesicles. NEBNext was the library preparation kit that showed the highest reproducibility among replicas, higher number of reads corresponding to miRNAs and more different miRNAs, followed by NEXTFlex and SMARTer smRNA-seq. GRAD method showed the highest reproducibility among replicas, a higher number of reads corresponding to miRNAs and more different miRNAs, followed by SEC and SEC+GRAD methods. Conclusions These results render the GRAD method to isolate EVs as one of the most appropriate to detect miRNAs from Evs.

2023-04-26 | GSE223838 | GEO

Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation

Project description:Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation

| PRJNA671225 | ENA

Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation

Project description:Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation

| PRJNA671227 | ENA

Comparison of RNA isolation methods on RNA-Seq: implications for differential expression and meta-analyses.

Project description:BACKGROUND:The increasing number of transcriptomic datasets has allowed for meta-analyses, which can be valuable due to their increased statistical power. However, meta-analyses can be confounded by so-called "batch effects," where technical variation across different batches of RNA-seq experiments can clearly produce spurious signals of differential expression and reduce our power to detect true differences. While batch effects can sometimes be accounted for, albeit with caveats, a better strategy is to understand their sources to better avoid them. In this study, we examined the effects of RNA isolation method as a possible source of batch effects in RNA-seq design. RESULTS:Based on the different chemistries of "classic" hot phenol extraction of RNA compared to common commercial RNA isolation kits, we hypothesized that specific mRNAs may be preferentially extracted depending upon method, which could masquerade as differential expression in downstream RNA-seq analyses. We tested this hypothesis using the Saccharomyces cerevisiae heat shock response as a well-validated environmental response. Comparing technical replicates that only differed in RNA isolation method, we found over one thousand transcripts that appeared "differentially" expressed when comparing hot phenol extraction with the two kits. Strikingly, transcripts with higher abundance in the phenol-extracted samples were enriched for membrane proteins, suggesting that indeed the chemistry of hot phenol extraction better solubilizes those species of mRNA. CONCLUSIONS:Within a self-contained experimental batch (e.g. control versus treatment), the method of RNA isolation had little effect on the ability to identify differentially expressed transcripts. However, we suggest that researchers performing meta-analyses across different experimental batches strongly consider the RNA isolation methods for each experiment.

| S-EPMC7082934 | biostudies-literature

Comparison of six methods for isolation of urinary microvesicles for miRNA profiling in proteinuria.

Project description:Urinary extracellular vesicles (uEVs) are a promising source for biomarker discovery, including miRNAs. The optimal isolation method of uEVs for miRNA profiling in patients with proteinuria is unknown. Six different methods were compared for the isolation of microvesicles and these were compared to raw urine (method A). 2 methods failed and further analysis was performed with the following methods: ExoRNeasy (method C), modified protocol for ExoRNeasy (method E) and ultracentrifugation (method F). Different miRNAs are enriched by method C and F. No differences were observed between method C and E.

2020-08-01 | GSE102627 | 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