Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SpKAS-seq reveals R-loop dynamics using low-input materials by detecting single-stranded DNA with strand specificity.

ABSTRACT: R-loops affect transcription and genome stability. Dysregulation of R-loops is related to human diseases. Genome-wide R-loop mapping typically uses the S9.6 antibody or inactive ribonuclease H, both requiring a large number of cells with varying results observed depending on the approach applied. Here, we present strand-specific kethoxal-assisted single-stranded DNA (ssDNA) sequencing (spKAS-seq) to map R-loops by taking advantage of the presence of a ssDNA in the triplex structure. We show that spKAS-seq detects R-loops and their dynamics at coding sequences, enhancers, and other intergenic regions with as few as 50,000 cells. A joint analysis of R-loops and chromatin-bound RNA binding proteins (RBPs) suggested that R-loops can be RBP binding hotspots on the chromatin.

SUBMITTER: Wu T

PROVIDER: S-EPMC9710868 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

spKAS-seq reveals R-loop dynamics using low-input materials by detecting single-stranded DNA with strand specificity.

Wu Tong T Lyu Ruitu R He Chuan C

Science advances 20221130 48

R-loops affect transcription and genome stability. Dysregulation of R-loops is related to human diseases. Genome-wide R-loop mapping typically uses the S9.6 antibody or inactive ribonuclease H, both requiring a large number of cells with varying results observed depending on the approach applied. Here, we present strand-specific kethoxal-assisted single-stranded DNA (ssDNA) sequencing (spKAS-seq) to map R-loops by taking advantage of the presence of a ssDNA in the triplex structure. We show that ...[more]

PMID: 36449625

Similar Datasets

spKAS-seq reveals R-loop dynamics using low-input materials by detecting single-stranded DNA with strand specificity

Project description:R-loops impact transcription and genome stability and are related to human diseases. Genome-wide R-loop mapping typically uses the S9.6 antibody or inactive RNase H, both requiring a large number of cells with varying results observed depending on the approach applied. Here, we present strand-specific KAS-seq (spKAS-seq) to map R-loops by taking advantage of the presence of a single-stranded DNA (ssDNA) in the triplex structure. We show that spKAS-seq detects R-loops and their dynamics at coding sequences, enhancers, and other intergenic regions with as few as 50,000 cells and eliminates potential bias towards open chromatin and RNase H-sensitive regions. A joint analysis of R-loops and chromatin-bound RNA-binding proteins (RBPs) suggested that R-loops can be RBP-binding hotspots on the chromatin.

2022-06-06 | GSE192822 | GEO

spKAS-seq reveals R-loop dynamics using low-input materials by detecting single-stranded DNA with strand specificity

Project description:spKAS-seq reveals R-loop dynamics using low-input materials by detecting single-stranded DNA with strand specificity

| PRJNA793268 | ENA

Comparative evaluation of RNA-Seq library preparation methods for strand-specificity and low input.

Project description:Library preparation is a key step in sequencing. For RNA sequencing there are advantages to both strand specificity and working with minute starting material, yet until recently there was no kit available enabling both. The Illumina TruSeq stranded mRNA Sample Preparation kit (TruSeq) requires abundant starting material while the Takara Bio SMART-Seq v4 Ultra Low Input RNA kit (V4) sacrifices strand specificity. The SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Pico) by Takara Bio claims to overcome these limitations. Comparative evaluation of these kits is important for selecting the appropriate protocol. We compared the three kits in a realistic differential expression analysis. We prepared and sequenced samples from two experimental conditions of biological interest with each of the three kits. We report differences between the kits at the level of differential gene expression; for example, the Pico kit results in 55% fewer differentially expressed genes than TruSeq. Nevertheless, the agreement of the observed enriched pathways suggests that comparable functional results can be obtained. In summary we conclude that the Pico kit sufficiently reproduces the results of the other kits at the level of pathway analysis while providing a combination of options that is not available in the other kits.

| S-EPMC6748930 | biostudies-literature

Refined RIP-seq protocol for epitranscriptome analysis with low input materials.

Project description:N6-Methyladenosine (m6A) accounts for approximately 0.2% to 0.6% of all adenosine in mammalian mRNA, representing the most abundant internal mRNA modifications. m6A RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq) is a powerful technique to map the m6A location transcriptome-wide. However, this method typically requires 300 μg of total RNA, which limits its application to patient tumors. In this study, we present a refined m6A MeRIP-seq protocol and analysis pipeline that can be applied to profile low-input RNA samples from patient tumors. We optimized the key parameters of m6A MeRIP-seq, including the starting amount of RNA, RNA fragmentation, antibody selection, MeRIP washing/elution conditions, methods for RNA library construction, and the bioinformatics analysis pipeline. With the optimized immunoprecipitation (IP) conditions and a postamplification rRNA depletion strategy, we were able to profile the m6A epitranscriptome using 500 ng of total RNA. We identified approximately 12,000 m6A peaks with a high signal-to-noise (S/N) ratio from 2 lung adenocarcinoma (ADC) patient tumors. Through integrative analysis of the transcriptome, m6A epitranscriptome, and proteome data in the same patient tumors, we identified dynamics at the m6A level that account for the discordance between mRNA and protein levels in these tumors. The refined m6A MeRIP-seq method is suitable for m6A epitranscriptome profiling in a limited amount of patient tumors, setting the ground for unraveling the dynamics of the m6A epitranscriptome and the underlying mechanisms in clinical settings.

| S-EPMC6136692 | biostudies-literature

Comparative evaluation of RNA-Seq library preparation methods for strand-specificity and low input

Project description:Library preparation is a key step in gene expression quantification. There are considerable advantages to both strand specific sequencing and the ability to sequence samples with very small amounts of starting material. Until recently there was no kit available that allowed both simultaneously. The standard Illumina-TruSeq stranded mRNA Sample Preparation kit requires abundant starting quantity while the Takara Bio-SMART-Seq® v4 Ultra® Low Input RNA kit allows for ultra low starting quantities but sacrifices strand specificity. Recently a kit that can do both, SMARTer® Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian by Takara Bio, has become available. Evaluating the performance and effects of these sample preparation kits is a critical determinant for selecting the appropriate sequencing protocol, but a comprehensive comparison is currently missing. To address this we performed a detailed comparative analysis of sequencing libraries prepared with the three kits. We prepared a set of samples representing two experimental conditions with each kit, allowing for comparison of the kits in a standard realistic differential expression analysis. We find substantial differences in the levels of alignment and differential gene expression. Using differential expression analysis we show that using Pico results in identifying 55% less differentially expressed genes than TruSeq. Nevertheless, using gene pathway enrichment analysis we find similar results for all three kits, indicating that ultimately comparable functional results can be reached.

2019-09-17 | GSE124167 | GEO

Systematic comparative analysis of strand-specific RNA-seq library preparation methods for low input samples.

Project description:Despite the recent precipitous decline in the cost of genome sequencing, library preparation for RNA-seq is still laborious and expensive for applications such as high throughput screening. Limited availability of RNA generated by some experimental workflows poses an additional challenge and increases the cost of RNA library preparation. In a search for low cost, automation-compatible RNA library preparation kits that maintain strand specificity and are amenable to low input RNA quantities, we systematically tested two recent commercial technologies-Swift RNA and Swift Rapid RNA, presently offered by Integrated DNA Technologies (IDT) -alongside the Illumina TruSeq stranded mRNA, the de facto standard workflow for bulk transcriptomics. We used the Universal Human Reference RNA (UHRR) (composed of equal quantities of total RNA from 10 human cancer cell lines) to benchmark gene expression in these kits, at input quantities ranging between 10 to 500 ng. We found normalized read counts between all treatment groups to be in high agreement. Compared to the Illumina TruSeq stranded mRNA kit, both Swift RNA library kits offer shorter workflow times enabled by their patented Adaptase technology. We also found the Swift RNA kit to produce the fewest number of differentially expressed genes and pathways directly attributable to input mRNA amount.

| S-EPMC8810888 | biostudies-literature

Refined RIP-seq protocol for epitranscriptome analysis with low input materials

Project description:We optimized the key parameters of m6A MeRIP-seq, including the starting amount of RNA, RNA fragmentation, antibody selection, MeRIP washing/elution conditions, methods for RNA library construction and the bioinformatics analysis pipeline. With the optimized immunoprecipitation conditions and a post-amplification rRNA depletion strategy, we were able to profile the m6A epitranscriptome using 2 µg of total RNA. We identified ~12,000 m6A peaks with a high signal/noise ratio from two lung adenocarcinoma (ADC) patient tumors. Through integrative analysis of the transcriptome, m6A epitranscriptome and proteome data in the same patient tumors, we identified dynamics at the m6A level that accounts for the discordance between mRNA and protein levels in these tumors.

2018-09-06 | GSE116002 | GEO

Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols.

Project description:Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in per-gene linearity of response, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample.

| S-EPMC4380159 | biostudies-literature

Technical variations in low-input RNA-seq methodologies.

Project description:Recent advances in RNA-seq methodologies from limiting amounts of mRNA have facilitated the characterization of rare cell-types in various biological systems. So far, however, technical variations in these methods have not been adequately characterized, vis-à-vis sensitivity, starting with reduced levels of mRNA. Here, we generated sequencing libraries from limiting amounts of mRNA using three amplification-based methods, viz. Smart-seq, DP-seq and CEL-seq, and demonstrated significant technical variations in these libraries. Reduction in mRNA levels led to inefficient amplification of the majority of low to moderately expressed transcripts. Furthermore, noise in primer hybridization and/or enzyme incorporation was magnified during the amplification step resulting in significant distortions in fold changes of the transcripts. Consequently, the majority of the differentially expressed transcripts identified were either high-expressed and/or exhibited high fold changes. High technical variations ultimately masked subtle biological differences mandating the development of improved amplification-based strategies for quantitative transcriptomics from limiting amounts of mRNA.

| S-EPMC3890974 | biostudies-literature

A low-cost, low-input method establishment for m6A MeRIP-seq.

Project description:N6-methyladenosine (m6A) is a highly prevalent modification found in mammal mRNA molecules that plays a crucial role in the regulation of cellular function. m6A RNA immunoprecipitation sequencing (MeRIP-seq) has been frequently used in transcriptomics research to identify the location of m6A. MABE572 (Millipore) is the most widely utilized and efficient anti-m6A antibody for MeRIP-seq. However, due to the high dose and price of this antibody, which has also been taken off the market, we discovered that CST's anti-m6A antibody can be used instead of MABE572 to map the m6A transcriptome. In the present study, we performed different concentrations of the CST anti-m6A antibodies with the corresponding initiation RNA of HEK293T cells, 2.5 μg antibody with 1 μg total RNA, 1.25 μg antibody with 0.5 μg total RNA, and 1.25 μg antibody with 0.1 μg total RNA. By comparing the m6A peak calling, enriched motifs, alternative splicing events, and nuclear transcripts modified by m6A between the CST and Millipore libraries, it was found that the CST library presented similar data to Millipore, even at incredibly low doses. The volume and cost of antibodies are significantly reduced by this refined MeRIP-seq using CST antibody, making it convenient to map future large-scale sample m6A methylation.

| S-EPMC10776898 | 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