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21,239 results for “RNA-seq”
2018-02-20_RNA-Seq_lesson
<p>Files for the RNA-Seq lesson on February 20, 2018.</p> <p>You have two compressed folder:</p> <ul> <li>The "RNAseq_lesson_only_start_files.zip" contains the necessary files to start. It does not contain the expected results.</li> <li>The "RNAseq_lesson_all_files.zip" contains all files necessary for the lesson + the expected results from Kallisto and Sleuth.</li> </ul>
Arabidopsis RNA-seq seedling small training set (chr1 100k)
<p>Arabidopsis seedling small training set for RNA-seq. FastQ files for reads mapping to the first 100k of the first chromosome of Arabidopsis are included. The sequencing was done using Illumina, 100 paired-end mode. </p>
Reference data for "Limitations of alignment-free tools in total RNA-seq quantification"
<p>This repository contains reference data for "Limitations of alignment-free tools in total RNA-seq quantification"</p>
Extension of Partial Gene Transcripts by Iterative Mapping of RNA-Seq Raw Reads
<p>Trinity assembled transcriptome of <em>Drosophila melanogaster</em> and <em>Osmia </em><em>bicornis</em></p>
Counts and design files - RNA-Seq workshop (2019-03-07)
<p>Counts (generated through usage of featureCounts on the BAM alignment files).</p> <p>Design file that gives the samples to experimental condition relationship.</p>
A Galaxy-based training resource for single-cell RNA-seq quality control and analyses
<p>This is the tutorial data for the 'Single-cell quality control with scater' tutorial on the Galaxy Training Network. The data is the same dataset that is used as the inbuilt example dataset within scater, but has been implemented as individual files.</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>
Supporting data for RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data.
<p>This is the data repository for <em>RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data</em>. To use this dataset, please refer to <a href="https://github.com/HowardGech/RCANE" target="_blank" rel="noopener">https://github.com/HowardGech/RCANE</a>.</p>
10X Single Cell RNA-Seq On WT and Mutation mouse models
<p>Four four-week-old mice, consisting of wild type, Pten mutation, MAP3K3 mutation, and MAP3K3+Pten mutation, were sampled at 4 weeks of age. Endothelial cells were enriched using CD31 magnetic beads, followed by 10X single-cell RNA sequencing.</p>
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
RNA-seq data of "Transcriptome analyses of leaves reveal that hexanoic acid priming differentially regulate gene expression in contrasting Coffea arabica cultivars"
<p>This dataset represent FASTQ gziped files from the study "Transcriptome analyses of leaves reveal that hexanoic acid priming differentially regulate gene expression in contrasting <em>Coffea arabica</em> cultivars" (<a href="https://doi.org/10.3389/fsufs.2021.735893">https://doi.org/10.3389/fsufs.2021.735893</a>). Sequencing was done using an Illumina Novaseq 6000 instrument, paired-sequencing (2 X150 bp). Sample details are also available at https://www.ebi.ac.uk/ena/browser/view/ERA6282544.</p> <p> </p> <p>All filenames have the following naming scheme:</p> <p>LCS7609_DS_AAA_leafBBB_(R1 or R2).fq.gz</p> <p>AAA stands for the abbreviations:</p> <p>- CC (Coffea arabica cv Catuai control)</p> <p>- CHx (Coffea arabica cv Catuai exposed to Hexanoic acid)</p> <p>- OC (Coffea arabica cv Obatã control)</p> <p>- OHx (Coffea arabica cv Obatã exposed to Hexanoic acid)</p> <p>BBB stands for the number of biological replicate (1, 2 or 3).</p> <p> </p> <p> </p> <p> </p>
Practico NGS 2021 - BIO252I - RNA-seq
<p>En este repositorio se encuentran los documentos para el práctico de NGS que tiene como objetivo descubrir los genes diferencialmente expresados en las células NHBE, cuando ellas son infectadas con el virus SARS-Cov-2. Se desea comparar células infectadas versus no infectadas (mock). Cada grupo cuenta con 3 réplicas. Este práctico está basado en datos reales (GEO GSE147507), pero que han sido modificados y escalados para este práctico.</p>
Data for: Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes
<p><span>Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in <em>Drosophila</em> starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study (Li et al., 2022). With over 44,000 nuclei and 6,000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, <em>in situ</em> hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function <em>in vivo</em>.</span></p>
RNA-seq - Definitive endoderm differentiation of human pluripotent stem cells in G1 phase
<p>Backup copy of the processed RNA-seq data at http://ngs.sanger.ac.uk/production/endoderm/</p>
RNA-Seq and proteomics of Crohn's disease: Terminal ileum of inflamed and non inflamed paired tissue biopsy
<p>The cause of chronic inflammation manifestations such as inflammatory bowel disease (IBD) is still not yet fully understood. Evidence however points to the convergence of environmental factors, genetic factors, intestinal microbiota and the immune response at the intestinal epithelial surface that consequently leads to a breakdown of barrier function in IBD. An increasing number of theories implicate a dysfunctional intestinal epithelial surface in the pathogenesis of IBD and progression to complicated disease, which contributes significantly to the IBD burden. As yet, the significance of the intestinal epithelial surface in IBD manifestation and progression has not been explored using a systems biology approach, that compares the mucosa of healthy patients to those with IBD. In the context of dyregulation of the epithelial barrier, there is a scarcity of publications that explore the contribution of proteins- the effectors of the cells and RNA message in the pathways affected by CD and in particular the localization of inflammatory response. We aim to evaluate the functional pathways of chronic inflammation at the intestinal epithelial surface using a combination of transcriptomic and proteomic analyses on intestinal epithelial tissue samples from paired inflamed and non-inflammed Crohn's disease (CD) and healthy control subjects. Concordance of several biological pathways from both data sets was found to be altered in CD patients' epithelia when compared to healthy controls. This information could be helpful in identifying novel therapeutic targets that aim to restore barrier function at the intestinal epithelial surface and to guide therapy.</p>
Whole blood RNA-seq demonstrates an increased host immune response in individuals with cystic fibrosis who develop nontuberculous mycobacterial pulmonary disease
<p><strong>Background </strong></p> <p>Individuals with cystic fibrosis have an elevated lifetime risk of colonization, infection, and disease caused by nontuberculous mycobacteria. A prior study involving non-cystic fibrosis individuals reported a gene expression signature associated with susceptibility to nontuberculous mycobacteria pulmonary disease (NTM-PD). In this study, we determined whether people living with cystic fibrosis who progress to NTM-PD have a gene expression pattern similar to the one seen in the non-cystic fibrosis population. <strong> </strong></p> <p><strong>Methods</strong></p> <p>We evaluated whole blood transcriptomics using bulk RNA-seq in a cohort of cystic fibrosis patients with samples collected closest in timing to the first isolation of nontuberculous mycobacteria. The study population included patients who did (n = 12) and did not (n = 30) develop NTM-PD following the first mycobacterial growth. Progression to NTM-PD was defined by a consensus of two expert clinicians based on reviewing clinical, microbiological, and radiological information. Differential gene expression was determined by DESeq2.</p> <p><strong>Results</strong></p> <p>No differences in demographics or composition of white blood cell populations between groups were identified at baseline. Out of 213 genes associated with NTM-PD in the non-CF population, only two were significantly different in our cystic fibrosis NTM-PD cohort. Gene set enrichment analysis of the differential expression results showed that CF individuals who developed NTM-PD had higher expression levels of genes involved in the interferon (α and γ), tumor necrosis factor, and IL6-STAT3-JAK pathways. <strong> </strong></p> <p><strong>Conclusion</strong></p> <p>In contrast to the non-cystic fibrosis population, the gene expression signature of patients with cystic fibrosis who develop NTM-PD is characterized by increased innate immune responses.</p>
Bulk RNA-seq dataset of human embryonic stem cells (hESCs) differentiation
<p>The dataset consists of 31 bulk samples obtained from human embryonic stem cells undergoing undirected differentiation. The samples were collected at 16 time points on days 0 and 7-21 of differentiation. The file contains the raw read counts.</p> <p>More details can be found in:</p> <p>SPIRAL: Significant Process InfeRence ALgorithm for single cell RNA-sequencing and spatial transcriptomics\<br> Hadas Biran, Tamar Hashimshony, Tamar Lahav, Or Efrat, Yael Mandel-Gutfreund, and Zohar Yakhini</p> <p> <a href="https://doi.org/10.1101/2022.05.24.493189">https://doi.org/10.1101/2022.05.24.493189</a></p>
Data from: Single-nucleus RNA-seq and ATAC-seq in outbred rats with divergent cocaine addiction behaviors reveal long-term changes in gene regulation and GABAergic inhibition in the amygdala
<p>This dataset accompanies our publication titled: "Single-nucleus RNA-seq and ATAC-seq in outbred rats with divergent cocaine addiction behaviors reveal long-term changes in gene regulation and GABAergic inhibition in the amygdala."</p> <p><strong>Files Included:</strong></p> <p><strong>1. geno.N26.vcf.gz</strong><br> - Description: Contains genotypes for 26 Heterogeneous Stock rats whose gene expression was predicted.</p> <p><strong>2. pred_expr.Brain.N26.tsv</strong><br> - Description: This tab-delimited table contains predicted relative gene expression in the brain for 26 Heterogeneous Stock rats. <br> - Details: Predictions were made for 8,997 genes from linear models based on cis-eQTLs from whole brain hemisphere tissue downloaded from the <a href="https://ratgtex.org/download/">RatGTEx Portal</a>. A gene is included in the table if it had at least one significant cis-eQTL, and if its predicted expression in these 26 animals had nonzero variance. The values in the table give the predicted log2(relative expression), where log2(2) = 1 is the baseline expression from the two haplotypes of the gene if it had only reference alleles at all its regulatory loci.<br> - Additional Info: Predictions were generated using <strong>gene_expr_pred.py</strong> available at https://github.com/PejLab/gene_expr_pred<br> An explanation of the prediction model is given in https://doi.org/10.1101/2022.01.28.478116</p> <p><strong>3. Behavioral data.xlsx</strong><br> - Description: Contains behavioral data for the Heterogeneous Stock (HS) rats.<br> - Organization: Each sheet in the file corresponds to data for a specific figure.</p> <p><strong>Additional Dataset Locations:</strong></p> <p>The primary datasets generated during this study can be found on the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE212417">GSE212417</a></p> <p><strong>Publicly Available Datasets Utilized:</strong></p> <p>- Rattus norvegicus Ensembl v98 reference genome and genome assembly: <a href="http://useast.ensembl.org/Rattus_norvegicus/Info/Index">Rnor_6.0 </a><br> - JASPAR2022 transcription factor binding profiles for vertebrates: <a href="https://jaspar.genereg.net/">JASPAR</a><br> - ENCODE Honeybadger 2 ChIP-seq: <a href="https://personal.broadinstitute.org/meuleman/reg2map/">Broad Institute</a><br> - Liu et al. 2019106 GWAS for tobacco and nicotine addiction summary statistics: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358542/">PubMed</a><br> - RatGTEx Portal tissue-specific cis-eQTLs: <a href="https://ratgtex.org/download/">RatGTEx Portal </a><br> - 1000 Genomes European reference panel: <a href="https://alkesgroup.broadinstitute.org/LDSCORE/">Alkes Group</a><br> - KEGG pathways: <a href="https://www.kegg.jp/kegg/rest/keggapi.html">KEGG API</a></p>
Supplementary Data: Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS
<p>Supplementary data for the SCINPAS analysis as part of the manuscript "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS".</p> <p>It includes the output of SCINPAS, available here: <a href="https://github.com/zavolanlab/SCINPAS">https://github.com/zavolanlab/SCINPAS</a>, used for the analyses. Additionally, the scripts and data for the comparison with other tools is provided. Please read the README.md for more information.</p>
Sugar signal manipulation by T6P for yield on wheat grain - whole grain RNA-seq
<p>Trehalose 6-phosphate (T6P) is a powerful internal sugar signal in plants yet cannot be directly added nor<br>fully genetically controlled. A timed microdose of a plant-permeable T6P signalling precursor, DMNB-T6P, causes substantial yield improvements. This repository contains normalised counts table for a RNA-seq of wheat (Triticum aestivum) whole grains after spray with mock/DMNB-T6P.</p> <p>DESeq2 normalised count table for raw reads deposited in ncbi SRA under Bioproject PRJNA1007614. Related manuscript in press in Nature Biotechnology.</p> <p> </p> <p>Methods extract below:</p> <p>(...) Following RNA integrity analysis and quantitation (Bioanalyser; Agilent, USA), poly-A enriched cDNA libraries were generated and sequenced on an Illumina Novaseq 6000 sequencing platform generating 30–50 million 150 bp paired-end reads per sample. Low-quality reads and adaptor sequences were removed with Trimmomatic (trimmomatic-0.39.jar PE ILLUMINACLIP:TruSeq3-PE.fa:2:30:10:2:True TRAILING:30 MINLEN:40). The reads were aligned to the wheat reference genome (<em>Triticum aestivum</em> iwgsc_refseqv2.1 using HISAT2/2.2.1-foss-2019b with default parameters and converted to Bam format with SAMtools. Gene abundance was quantified using featureCounts with the High Confidence iwgsc_refseqv2.1 annotation (counting only primary alignments of read pairs with a quality cut-off of 10). Raw counts were normalized using the trimmed mean of M-values method by DESeq2.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.