Arabidopsis thaliana
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA385496 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR5508380.fastq.gz | Fastqsanger.gz | |||
| SRR5508390_1.fastq.gz | Fastqsanger.gz | |||
| SRR5508390_2.fastq.gz | Fastqsanger.gz | |||
| SRR5508381.fastq.gz | Fastqsanger.gz | |||
| SRR5508391_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 18 |
Similar Datasets
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
Project description:Hypertranscription facilitates biosynthetically demanding cellular state transitions through global upregulation of the nascent transcriptome. Despite its potential widespread relevance, documented examples of hypertranscription remain few and limited predominantly to early development. This limitation is in large part due to the fact that modern sequencing approaches, including single-cell RNA sequencing (scRNA-seq), generally assume similar levels of transcriptional output per cell. Here, we use molecule counting and spike-in normalization to develop absolute scaling of single-cell RNA sequencing data. Absolute scaling enables an estimation of total transcript abundances per cell, which we validate in embryonic stem cell (ESC) and germline data and apply to adult mouse organs at steady-state or during regeneration.
2022-12-21 | GSE221184 | GEO
Project description:Normalization of RNA sequencing (RNA-seq) data for gene expression comparison is essential to ensure accurate gene expression quantification. It has been argued that samples with large differences in global expression level cannot be properly normalized without spike-in control RNAs, however, spike-in controls are expensive and not yet widely used. Here, we presented a spike-in independent quantitative RNA sequencing (siqRNA-seq) method, which uses reads from genome DNA as an internal reference to quantify gene expression level. We showed that siqRNA-seq profiles gene expression as traditional RNA-seq, but allows to identify different expression genes between samples with distinct mRNA content. We also showed siqRNA-seq enable us to assess the copy number of mRNA per cell without counting cells and adding spike-ins. Thus, siqRNA-seq provides a convenient and versatile means to quantitatively profile the mRNA landscape in cells.
2024-04-08 | GSE223145 | GEO
Project description:Small RNA sequencing in Arabidopsis plants
| PRJNA157121 | ENA
Project description:Small RNA mobility in Arabidopsis thaliana
| PRJNA391370 | ENA
Project description:SNP-ChIP is a novel method leveraging small-scale intra-species genetic polymorphisms, mainly SNPs, to allow quantitative spike-in normalization of ChIP-seq results. SNP-ChIP uses a different strain of the same organism as the spike-in material and can be applied to any organism for which genome assemblies are available for two different strains or individuals with sufficient genetic diversity. This ensures antibody cross-reactivity and thus extends the applicability of the method beyond the small number of highly conserved proteins. It also ensures complete physiological coherence between the test and the spike-in cells. In this work we develop and validate the method using test cases from budding yeast meiosis. We use strains with ~0.7% genomic sequence divergence as test strain background and the spike-in strain, respectively. Sequencing reads are mapped to a hybrid genome, with naturally occurring sequence polymorphisms allowing assignment of most reads to one of the two genomes. By targeting the yeast chromosomal protein Red1, we show that SNP-ChIP reliably identifies previously reported changes in overall protein levels, irrespective of changes in binding distribution. We also show that SNP-ChIP is robust to wide changes in sequencing depth, as well as the amount of spike-in material. SNP-ChIP allowed discovery of novel regulators of global Red1 protein accumulation and is also shown to allow quantitative analysis of the DNA-damage associated histone modification gamma-H2AX. SNP-ChIP is a robust and versatile spike-in normalization method that can be used with any target against which a ChIP-grade antibody is available and for any organisms with sufficient intra-species diversity, including most model organisms as well as human cells. Grant ID: FY16-208 Grant title: Meiotic segregation of small chromosomes Funding source: March of Dimes Grantee name: Andreas Hochwagen, New York University, New York, NY, United States
2018-11-21 | GSE115092 | GEO
Project description:Arabidopsis thaliana raw small RNA sequence reads
| PRJNA299084 | ENA
Project description:The plasma levels of tissue-specific microRNAs can be used as prognostic and diagnostic biomarkers for chronic and acute diseases. Thereby, the combination of diverse miRNAs into biomarker signatures using multivariate statistics seems especially powerful in view to tissue and condition specific miRNA shedding into the plasma. Although Next-Generation Sequencing (NGS) technology enables to analyse circulating microRNAs on a genome-scale level, it suffers from potential biases (e.g. adapter ligation bias) and lacks absolute transcript quantitation. In order to develop a robust NGS discovery assay for genome-scale quantitation of circulating microRNAs we first evaluated the sensitivity, repeatability and ligation bias of four commercially available small RNA library preparation protocols. The protocol from RealSeq Biosciences was selected based on its performance and usability, and coupled with a novel panel of exogenous small RNA spike-in controls to enable absolute quantitation and ensure comparability of data across independent NGS experiments. The established MicroRNA Next-Generation-Sequencing Discovery Assay (miND) was validated for its relative accuracy, precision, analytical measurement range and sequencing bias and was considered fit-for-purpose for microRNA biomarker discovery. Summarized, all these criteria were met and thus our analytical platform is considered fit-for-purpose for microRNA biomarker discovery from plasma, serum, cerebrospinal fluid (CSF), synovial fluid (SF), or extracellular vesicles (EV) extracted from cell culture medium in the setting of any diagnostic, prognostic or patient stratification need.
2022-01-20 | GSE189930 | GEO