Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA171325 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Identification of microarray probe signals constantly present in multiple sample types (part 2)
| PRJNA171326 | ENA
Project description:Probe-Seq is a method that allows transcriptional profiling of specific cell types from heterogeneous tissue by labeling RNA. Briefly, we developed Probe-Seq, which allows deep transcriptional profiling of specific cell types isolated based on RNA abundance of defined markers. We applied Probe-Seq to purify and profile specific cell types from mouse, human, and chick retinas as well as the Drosophila gut. We also showed that Probe-Seq is compatible with frozen nuclei and that multiplexed Probe-Seq enables iterative isolation of multiple cell types from the same tissue sample. Provided that unique markers are available, this simple, novel method could potentially enable bulk RNA sequencing of any cell type from any organism.
2019-12-09 | GSE135572 | GEO
Project description:Pooled RNA samples were prepared for each of the four mouse strainsDU6, DU6i, DUKs, and DBA by epididymal fat tissues of 42 days old 20 male per lines. Pooling was used to reduce individual variability, which is high in outbred populations. For the hybridization of the tissue specific RNAs to the GeneChips, samples were prepared according to the recommendations of the Affymetrix user guide. First strand synthesis was carried out by a T7-(dT)24 primer and SuperScript II Reverse Transcriptase (Gibco BRL, Life Technologies GmbH, Eggenstein, Germany) using 10 mg whole RNA sample. Second strand synthesis was done according to the SuperScript Choice System (Gibco BRL, Life Technologies GmbH) by E. coli DNA-Polymerase I, E. coli Ligase and RNaseH. Fragment end-polishing was performed using T4-Polymerase. An in vitro transcription reaction was used to incorporate Biotin-11-CTP and Biotin-16-UTP to the cRNA probe (BioArray HighYield RNA Transcript Labeling Kit, Enzo). The fragmented cDNA was hybridized overnight at 45°C to ensure the quality of the probe. The expression levels were calculated with the GeneChip Expression Analysis softwares Data Mining Tool (Version 1.2.) and Microarray Suite (version 4.0.1.) provided by Affymetrix. Initially, hybridization signals on every GeneChip were scaled using all probe sets to minimize differences in overall signal intensities between two arrays allowing more reliable detection of biologically relevant changes in the sample. The Affymetrix decision matrix was used for the assessment of present, marginally present, and absent transcript amounts of a gene in a tested RNA. Keywords = adiposity Keywords = selected mouse lines Keywords = diabetes Keywords = insulin Keywords = leptin Keywords: parallel sample
2004-11-09 | GSE1936 | GEO
Project description:A functional biodiversity microarray (EcoChip) prototype has been developed to facilitate the analysis of fungal communities in environmental samples with broad functional and phylogenetic coverage and to enable the incorporation of nucleic acid sequence data as they become available from large-scale (next generation) sequencing projects. A dual probe set (DPS) was designed to detect a) functional enzyme transcripts at conserved protein sites and b) phylogenetic barcoding transcripts at ITS regions present in precursor rRNA. Deviating from the concept of GeoChip-type microarrays, the presented EcoChip microarray phylogenetic information was obtained using a dedicated set of barcoding microarray probes, whereas functional gene expression was analyzed by conserved domain-specific probes. By unlinking these two target groups, the shortage of broad sequence information of functional enzyme-coding genes in environmental communities became less important. The novel EcoChip microarray could be successfully applied to identify specific degradation activities in environmental samples at considerably high phylogenetic resolution. Reproducible and unbiased microarray signals could be obtained with chemically labeled total RNA preparations, thus avoiding the use of enzymatic labeling steps. ITS precursor rRNA was detected for the first time in a microarray experiment, which confirms the applicability of the EcoChip concept to selectively quantify the transcriptionally active part of fungal communities at high phylogenetic resolution. In addition, the chosen microarray platform facilitates the conducting of experiments with high sample throughput in almost any molecular biology laboratory.
2011-11-14 | GSE28018 | GEO
Project description:we will acquire three types of data sets as negative controls from 1) probe-free samples (i.e., regular proteomics sample), 2) probe-labeled samples without isotope-coding, and 3) probe-free samples treated with isotope reagents (i.e., without probe labeling, peptides cannot be isotopically labeled).
2022-12-16 | PXD032392 | Pride
Project description:we will acquire three types of data sets as negative controls from 1) probe-free samples (i.e., regular proteomics sample), 2) probe-labeled samples without isotope-coding, and 3) probe-free samples treated with isotope reagents (i.e., without probe labeling, peptides cannot be isotopically labeled).
2022-12-16 | PXD032376 | Pride
Project description:The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. However, the identification of multiple modification types in individual RNA molecules remains challenging. We present CHEUI, a new method that detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual transcript molecules in a single condition as well as differential methylation between two conditions, using nanopore signals. CHEUI processes observed and expected signals with convolutional neural networks to achieve high single-molecule accuracy and outperform other methods in detecting m6A and m5C sites and quantifying their stoichiometry. Moreover, CHEUI’s unique capability to identify different modifications in the same signal data reveals a non-random co-occurrence of m6A and m5C in transcripts in human cell lines and during mouse embryonic brain development. CHEUI unlocks the capability of studying links between multiple RNA modifications and phenotypes, enabling the discovery of new epitranscriptome functions. Furthermore, CHEUI's training and testing protocols are adaptable to other modifications, making it a versatile RNA technology.
2022-10-31 | GSE211760 | GEO
Project description:The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. However, the identification of multiple modification types in individual RNA molecules remains challenging. We present CHEUI, a new method that detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual transcript molecules in a single condition as well as differential methylation between two conditions, using nanopore signals. CHEUI processes observed and expected signals with convolutional neural networks to achieve high single-molecule accuracy and outperform other methods in detecting m6A and m5C sites and quantifying their stoichiometry. Moreover, CHEUI’s unique capability to identify different modifications in the same signal data reveals a non-random co-occurrence of m6A and m5C in transcripts in human cell lines and during mouse embryonic brain development. CHEUI unlocks the capability of studying links between multiple RNA modifications and phenotypes, enabling the discovery of new epitranscriptome functions. Furthermore, CHEUI's training and testing protocols are adaptable to other modifications, making it a versatile RNA technology.
2024-01-16 | GSE253150 | GEO