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ShareScore release 0.9.0
Dataset results
210 results for “RNA maturation”
Regulation of mature mRNA levels by RNA processing efficiency
<p>Data from the research paper "Regulation of mature mRNA levels by RNA processing efficiency" by Henfrey, C., Murphy, S., and Tellier, M.:</p> <p>-Highest expressed transcript annotation for protein-coding genes, gencode v38.</p> <p>-15 txt (BED) files for chromatin vs nucleoplasm enrichment gene sets: HeLa full gene sets, canonical protein only sets, chromatin RNA seq subsamples, mNET seq subsamples, Raji gene sets.</p> <p>-Proteomics data table</p> <p>-mRNA half-life table</p> <p>-Splicing efficiency for POINT-seq, ChrRNA-seq, NucRNA-seq table</p> <p>-Ser2-P mNET-seq readthrough index data table</p> <p>-Splicing efficiency for siLuc/siEX3 table (ChrRNA-seq, NucRNA-seq)</p> <p>-RMATs output tables for alternative splicing results (siEX3 vs siLuc)</p> <p>-Tables for TSS:TES quantifications (mNET-seq(CTD) vs log2FoldChange, chr/nuc/mnet siEX3 vs siLuc)</p>
Post-transcriptional regulation supports the homeostatic expression of mature RNA
<h3>Overview</h3> <p>This dataset consists of RNA sequencing data stored in HDF5 files. The data has been processed to quantify gene expression changes at the precursor RNA (preRNA) and mature RNA (matureRNA) levels across various biological conditions, including normal tissues and diseases. The dataset supports the study "Post-transcriptional regulation supports the homeostatic expression of mature RNA," providing insight into the general influence of post-transcriptional regulation (PTR) on gene expression homeostasis.</p> <h3>File Contents</h3> <p>Each HDF5 file contains the following datasets:</p> <ul> <li><strong>logFC_MatureRNA</strong>: Represents the log fold change of mature RNA expression levels between different conditions or tissues.</li> <li><strong>logCPM_MatureRNA</strong>: Represents the log counts per million for mature RNA.</li> <li><strong>PValue_MatureRNA</strong>: Represents the p-value for the statistical test performed on mature RNA expression levels.</li> <li><strong>FDR_MatureRNA</strong>: Represents the false discovery rate for the mature RNA expression levels.</li> <li><strong>logFC_preRNA</strong>: Represents the log fold change of precursor RNA expression levels between different conditions or tissues.</li> <li><strong>logCPM_preRNA</strong>: Represents the log counts per million for precursor RNA.</li> <li><strong>PValue_preRNA</strong>: Represents the p-value for the statistical test performed on precursor RNA expression levels.</li> <li><strong>FDR_preRNA</strong>: Represents the false discovery rate for the precursor RNA expression levels.</li> <li><strong>genes</strong>: A list of gene identifiers used in the study.</li> <li><strong>studies</strong>: A list of study or condition names included in the dataset.</li> </ul> <h3>File Naming Convention</h3> <p>The HDF5 files are named to reflect the filtering applied to exclude lowly expressed genes (logCPM > 5), and DESeq2 and edgeR refer to the tools used for differential expression analysis:</p> <ul> <li><code>DESeq2_filtered_exon_intron_abnormal_conditions_all_reads.h5</code></li> <li><code>edgeR_filtered_exon_intron_abnormal_conditions_all_reads.h5</code></li> <li><code>DESeq2_filtered_exon_intron_gtex_tissue_all_reads.h5</code></li> <li><code>edgeR_filtered_exon_intron_gtex_tissue_all_reads.h5</code></li> </ul> <h3>Example Usage</h3> <p>To query the HDF5 files, you can use the provided `<a href="https://github.com/suzheng/PTR_RNA_seq/blob/main/notebooks/parse_h5_file.ipynb">parse_h5_file.ipynb</a>` example notebook to parse the HDF5 files.</p>
IKKb-matured, RNA-loaded Dendritic Cells for Metastasised Uveal Melanoma
ClinicalTrials.gov study NCT04335890. IPD Sharing: NO. Countries: 1. Publications: 4.
Nanoparticle Cellular Internalization is Not Required for RNA Delivery to Mature Plant Leaves
<p>These datasets are used to produce the figures/graphs published in our article.</p>
Single-cell RNA sequencing reveals molecular features of postnatal maturation in the murine retinal pigment epithelium
GEO Series GSE203138. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Subcellular sequence tracking of RNA maturation during neuronal differentiation [Total]
GEO Series GSE159944. Mus musculus. 27 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq of Fasciola hepatica eggs during the maturation process elucidate the role of proteolytic enzymes
GEO Series GSE160622. Fasciola hepatica. 9 samples. Type: Expression profiling by high throughput sequencing.
E. coli MazF does not create specialized ribosomes that translate leaderless mRNAs, but instead blocks rRNA maturation and ribosome biogenesis [RNA-seq]
GEO Series GSE107327. Escherichia coli. 14 samples. Type: Expression profiling by high throughput sequencing.
RNA Sequencing of Fetal and Adult Mature Thymic Vγ1+ γδ T-Cells
GEO Series GSE166086. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Bidirectional changes in postmitotic H3K27me3 distributions underlie cerebellar granule neuron maturation dynamics [RNA-seq DIV3]
GEO Series GSE230712. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
The long non-coding RNA mimi scaffolds neuronal granules to maintain nervous system maturity
GEO Series GSE183813. Drosophila melanogaster. 4 samples. Type: Expression profiling by high throughput sequencing.
RNA-sequencing of cTECs, mTECs, and thymocyte maturation subsets SM, M1 and M2 from the CD4 and CD8 lineages from WT and Psmb11-deficient mice [DSP405]
GEO Series GSE107536. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Single-Nucleus RNA Sequencing of Developing and Mature Superior Colliculus Identifies Neuronal Diversity and Candidate Mediators of Circuit Assembly
GEO Series GSE224407. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
The long non-coding RNA mimi scaffolds neuronal granules to maintain nervous system maturity
GEO Series GSE183812. Drosophila melanogaster. 20 samples. Type: Expression profiling by high throughput sequencing.
ADAR2 reproducibly changes abundance and sequence of mature microRNAs in the mouse brain [RNA-Seq]
GEO Series GSE61066. Mus musculus. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Single cell RNA-Seq analysis provide deeper molecular insights into abnormal maturation of tubastatin A-treated mouse oocytes
GEO Series GSE115008. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
single-cell RNA sequencing of in vivo matured and maturation deficient human oocytes
GEO Series GSE129123. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Controlling nephron precursor differentiation to generate proximal-biased kidney organoids with emerging maturity [bulk RNA-seq]
GEO Series GSE264217. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.
Circular non-coding RNA ANRIL modulates ribosomal RNA maturation and atherosclerosis in humans.
GEO Series GSE65392. Homo sapiens. 6 samples. Type: Expression profiling by array.
Bulk RNA-seq data of large-scale bone calluses from skeletally immature and mature mice
GEO Series GSE250199. Mus musculus. 40 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.