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332 results for “RNA processing”
Conserved regulation of RNA processing in somatic cell reprogramming
<p><strong>Data set 1. Transcript expression across human RNA-Seq samples: estimated read counts. </strong>The file contains estimated read counts, generated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), for human transcripts and RNA-Seq samples used in this study (see Additional file 2 of the accompanying publication). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. Ensembl transcript identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 2. Transcript expression across murine RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for mouse transcripts.</p> <p><strong>Data set 3. Transcript expression across simian RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for chimpanzee transcripts.</p> <p><strong>Data set 4. Transcript expression across across human RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 1, but instead of read counts, transcript abundances in transcripts per million (TPM), as estimated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), are listed. Format, column and row names as in Data set 1.</p> <p><strong>Data set 5. Transcript expression across murine RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for mouse transcripts.</p> <p><strong>Data set 6. Transcript expression across simian RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for chimpanzee transcripts.</p> <p><strong>Data set 7. Differential expression analyses across human RNA-Seq sample groups: log fold changes. </strong>The file contains log fold changes, inferred by edgeR (<a href="http://bioconductor.org/packages/release/bioc/html/edgeR.html">http://bioconductor.org/packages/release/bioc/html/edgeR.html</a>), for human genes and the RNA-Seq sample group contrasts listed in Additional file 3 of the accompanying publication in a compressed (GZIP) TSV gene-by-comparison matrix. Ensembl gene identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 8. Differential expression analyses across murine RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for mouse genes.</p> <p><strong>Data set 9. Differential expression analyses across simian RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for chimpanzee genes.</p> <p><strong>Data set 10. Differential expression analyses across human RNA-Seq sample groups: false discovery rates. </strong>The file contains false discovery rates (FDR) for the differential expression analyses summarized in Data set 7. Format, column and row names as in Data set 7.</p> <p><strong>Data set 11. Differential expression analyses across murine RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for mouse genes.</p> <p><strong>Data set 12. Differential expression analyses across simian RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for chimpanzee genes.</p> <p><strong>Data set 13. Quantification of alternative splicing events across human RNA-Seq samples. </strong>The file contains ‘percent spliced in’ (PSI) values computed by SUPPA (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>) for annotated alternative splicing events (inferred from the transcript annotation of the human genome, Ensembl release 84; <a href="http://www.ensembl.org/">http://www.ensembl.org/</a>). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. SUPPA-provided event identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 14. Quantification of alternative splicing events across murine RNA-Seq samples. </strong>As in Data set 13, but for mouse alternative splicing events.</p> <p><strong>Data set 15. Differential splicing analyses across human RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>The file contains ΔPSI values for human alternative splicing events (as in Data set 13). The RNA-Seq sample group contrasts are listed in Additional file 3 of the accompanying publication. Values were inferred by SUPPA’s diffSplice functionality (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>). The format is a compressed (GZIP) tab-separated gene-by-comparison matrix. SUPPA event identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 16. Differential splicing analyses across murine RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>As in Data set 15, but for mouse alternative splicing events.</p> <p><strong>Data set 17. Differential splicing analyses across human RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of human alternative splicing events summarized in Data set 15. Format, column and row names as in Data set 15.</p> <p><strong>Data set 18. Differential splicing analyses across murine RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of mouse alternative splicing events summarized in Data set 16. Format, column and row names as in Data set 15.</p> <p><strong>Data set 19. Transcript expression across murine RNA-Seq time course data: estimated read counts. </strong>As in Data set 2, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 20. Transcript expression across murine RNA-Seq time course data: estimated transcript abundances. </strong>As in Data set 5, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 21. Quantification of alternative splicing events across murine RNA-Seq time course data. </strong>As in Data set 14, but for the time course data generated for the accompanying publication.</p>
CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data: Codes and processed data
<p>Codes and processed data to reproduce the analysis discussed in: </p> <p>Wegmann <em>et Al.</em>,<strong> CellSIUS provides sensitive and specific detection of rare cell<br> populations from complex single cell RNA-seq data</strong>, Genome Biology 2019 (Accepted)<br> </p>
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>
Heatmaps of orthology and protein domain preservation in RNA Processing complexes throughout the fungal kingdom
<p>An analysis of the presence/absence of orthologues for Fungal RNA Processing protein complexes, and the presence/absence of the known PFAM protein domains within each protein within these complexes in the organism's proteome. </p> <p>Each image represents one RNA Processing protein complex.</p> <p>Orthology (far left panel in each image) is relative to Yeast, and taken from a query against the EnsEMBL orthology database (black = no orthologue; red = orthologue). <br> <br> Each orthologue was then queried for its PFAM domains, and the non-redundant set of PFAM domains representing each set of orthologous proteins, spanning all species, was then scanned against the complete proteome of each species. The resulting heatmap indicates the presence or absence of that PFAM domain anywhere in the proteome of that species. (black = absent; red = 1 copy; grey->blue = more than one copy)</p>
Processed data for "Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses"
<p>tar.gz of processed data in the form of compressed R files (rds) of SingleCellExperiment (<a href="https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html">https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html</a>) objects and a metadata csv for the data in the publication <em>Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses </em>(O'Flanagan et al. 2019).</p>
Processed RNA expression count data from Groen et al.: The strength and pattern of natural selection on rice gene expression
<p>We assessed transcriptome variation in populations of 216 accessions of rice, <em>Oryza sativa</em>, which represented all major varietal groups including indica and japonica. During the 2016 Philippines dry season the accessions were planted in triplicate (with two accessions planted in triplicate three times as replicated checks) in identical alpha-lattice layouts of 660 plots in two fields: a continuously wet paddy, and a field where plants were exposed to intermittent drought in the vegetative and reproductive stages. We measured transcript levels in leaf blades of 50-day-old plants at 33 days after seedling transplant, and 17 days after withholding water in the dry field, using a liquid automation-based 3’ mRNA-seq quantification approach. Samples were multiplexed in batches of 96 per library. Raw sequencing data are available at the SRA in BioProject accession number PRJNA588478. A key to the raw sequencing data in this BioProject can be found in the metadata of the processed RNA expression count data here.</p>
Processed datasets and codes for differential expression analysis on polulation-level RNA-seq data
<p>This version includes codes and data necessary to reproduce all results in our response to the correspondences ("Response to 'Neglecting normalization impact in semi‑synthetic RNA‑seq data simulation generates artificial false positives' and 'Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples'") (<a href="https://doi.org/10.1186/s13059-024-03232-8">https://doi.org/10.1186/s13059-024-03232-8</a>).</p> <p>It also includes a README file to guide the reproduction of the results in our original publication and resources for the goodness of fit test in the original publication, "Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples" (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4</a>).</p>
Processing single-cell RNA-seq datasets using SingCellaR
<ul> <li>cellranger_output.zip : Zipped files for cellranger output</li> <li>Code.zip: This contains the code for Step2-7</li> <li>Human_genesets.zip: This includes the geneset signatures (.gmt) files we used in the protocol and original study (Roy et al, 2021)</li> <li>Human_HSPC_All.SingCellaR.rdata: The SingCellaR objects generated in Step 4 on a local computer </li> <li>ABM_1.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>eFL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>PBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>meta.data.txt: meta data includes the donor and batch information </li> </ul>
Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"
<p>Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"</p>
Processed read counts from macrophage RNA-seq and ATAC-seq experiments
<p><strong>RNA-seq files:</strong></p> <ol> <li>RNA_count_matrix.txt.gz - raw read counts</li> <li>RNA_cqn_matrix.txt.gz - read counts quantile normalised with the cqn R package</li> <li>RNA_gene_metadata.txt.gz - information about the genes</li> <li>RNA_sample_metadata.txt.gz - information about the samples</li> </ol> <p><strong>ATAC-seq files:</strong></p> <ol> <li>ATAC_count_matrix.txt.gz - raw read counts</li> <li>ATAC_cqn_matrix.txt.gz - read counts quantile normalised with the cqn R package</li> <li>ATAC_peak_metadata.txt.gz - peak coordinates and other metadata</li> <li>ATAC_sample_metadata.txt.gz - sample metadata</li> <li>ATAC_consensus_peaks.gff3.gz - GFF3 file containing the GRCh38 coordinates of the peaks</li> </ol>
Processed RNA expression count data and metadata from Gupta et al.:Systems genomics of salinity stress response in rice
<p>We assessed gene expression variation in a population of 130 accessions of rice (Oryza sativa) belonging to the major varietal group indica. The field experiment was conducted in the dry season of 2017 at IRRI in Los Banos, Laguna, Philippines. Seeds from each accession were sown on December 16, 2016, and seedlings were then transplanted into the experimental fields at 17 days after sowing (DAS), on January 5, 2017. The field experiments was conducted across two locations close-by: one non-salinized normal field and the other salinized field. Both field environments used a randomized complete block experimental design with each accession planted in three replicates. Each experimental plot included the accessions NSICRC 222 and NSICRC 182 serving as border rows. The application of salt in Block L5 started on January 19, 2017 when the plants were 31 days old. The salinity level was monitored by recording electrical conductivity (EC), using EC meters installed in each of the parcels at a depth of 30 cm. The salinity levels were then maintained at 6 dS/m (considered mild to moderate salinity stress) until maturity. Management and maintenance of the fields included the application of basal fertilizer, spraying of insecticides against thrips and removal of plants potentially infected with the rice tungro virus disease. Tissue collection for transcriptome. Briefly, leaf collection was done at 38 DAS (8 days after the beginning of the salt treatment) in the non-saline and saline field. Transcript levels were measured using a liquid automation-based 3 prime mRNA-seq quantification approach. Samples were multiplexed in batches of 96 per library. Raw sequencing data are available at the SRA in BioProject accession number PRJNA1010833. A key to the raw sequencing data in this BioProject can be found in the metadata of the processed RNA expression count data here</p>
single-cell RNA-seq of EHT process
<p>This file 'atfr.rds' contains onject including the raw and log-normalizd expression counts, and TF activity matrix generated from metaRegulon. In addition, the file 'cell_weight_pseudotime.rds' contains the pseudotime information.</p>
Processed data - DegNorm: Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis
<p>Processed data from DegNorm:</p> <ul> <li>"_raw.txt": raw read counts matrix;</li> <li>"_DI.txt": Degradation index score matrix;</li> <li>"_DegNorm.txt": normalized read counts matrix from DegNorm output;</li> <li>"_coverage.Rdata": list of coverage matrix for the sample;</li> <li>"_countsTIN.txt": TIN normalized counts.</li> </ul> <p> </p>
Processed Single-cell RNA-seq Data for Exercise Rejuvenation Intervention on Mouse Subventricular Zone
<p>Exercise single-cell RNA-seq data of mouse subventricular zone (in processed Seurat object) used in the publication "Cell type-specific aging clocks to quantify aging and rejuvenation in regenerative regions of the brain" (preprint available at <a href="https://doi.org/10.1101/2022.01.10.475747">https://doi.org/10.1101/2022.01.10.475747</a>).</p>
Processed Single-cell RNA-sequencing data from adult recurrent respiratory papillomatosis
<p>.rds file (read in R) that contains processed single-cell RNA-sequencing data from 13 untreated adult recurrent respiratory papillomatosis clinical samples. The associated raw RNA sequencing data is available through The Database of Genotypes and Phenotypes (dbGaP), accession number phs003349.v1.p1. Sequences were also alsigned to a, HPV reference to allow quantification of HPV 6 or 11 gene expression. </p>
Clinical Manifestations and Biomarkers in Amyotrophic Lateral Sclerosis Type 4 and Other Inherited Neurological Disorders of RNA Processing
ClinicalTrials.gov study NCT04394871. IPD Sharing: NO. Countries: 1. Publications: 2.
Imada Fasting Cancer - single cell RNA-Seq Processed Data R object
Open the record for dataset details and reuse information.
adata_rna_processed
<p>adata_rna_processed</p>
A Point Mutation in the RNA Recognition Motif of CSTF2 Associated with Intellectual Disability in Humans Causes Defects in 3′ End Processing [3'prime-seq]
GEO Series GSE152975. Mus musculus. 8 samples. Type: Other.
A comprehensive RNA handling and transcriptomics guide for high-throughput processing of Plasmodium blood stage samples
GEO Series GSE151557. Plasmodium falciparum. 22 samples. Type: Expression profiling by high throughput sequencing.
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