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23,030 results for “RNA_seq”
Single-cell RNA-seq profiles of lung adenocarcinoma patients and tumor-bearing mice
<p>single-cell RNA sequencing (scRNA-seq) profiles from eight patients with lung adenocarcinoma (LUAD) and four samples of tumor tissues from tumor bearing mice were performed. By integrating other scRNA-seq data and clinical information, we identified activated adaptive immune responses in older patients, reflected by enriched dysfunctional T cell signature scores and immune checkpoint molecules. Our study shows increased efficacy of immune checkpoint blockade therapy in older patients, addressing the prominent role of age when considering immunotherapy.</p>
Bulk RNA Seq of Electrically Stimulated Retinal Organoids
<p>Considering the significant role played by both intrinsic and extrinsic electric fields in the growth and maturation of the central nervous system, the impact of short exposure to external electric fields on the development and differentiation of retinal organoids was investigated.Organoids derived from human embryonic stem cells at day 80, a critical time point in their differentiation and maturation, were used. A single 60-minute exposure to a distinct biphasic electrical field influenced neuronal development and increased the population of photoreceptors. Initial immunohistochemistry and qPCR studies confirmed the influence of electrical stimulation on the development and differentiation of various RO cell types. RNA sequencing data revealed elevated expression of rod photoreceptors, Müller cells, horizontal cells, and amacrine cells, alongside the downregulation of retinal pigment epithelium and retinal ganglion cell genes.Furthermore, our study demonstrated varying degrees of organoid development and maturation depending on the specific electrical field applied. These findings highlight the significant impact of extrinsic electrical fields on early retinal development and suggest that optimizing electrical field parameters could effectively address certain limitations in retinal organoid technology, potentially reducing the reliance on chemicals and small molecules.</p>
scRNA-seq background RNA
<p>Data files to benchmark background RNA estimation and removal files<br>We uploaded 5 zip files, each corresponding to one 10X experiment of a mixture of mouse kidney cells from 3 mouse strains BL6, SVLMJ and CAST. The data contain information from genotype based demultiplexing also at a feature resolution.</p> <p>We added now also intermediate files of the genotype analysis.</p>
Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data
<p>These data files contain single-cell RNA-sequencing expression data (expression_data.zip) and pseudotime files (pseudotime.zip) used to conduct comparisons of network inference methods on six published single-cell RNA-sequencing datasets. The resulting networks generated from the network inference methods are also uploaded here (normalized_inferred_networks.zip and imputed_inferred_networks.zip). Finally, the gold standard networks we used as ground truth to measure accuracy of the inferred networks are uploaded here (gold_standard_datasets.zip).</p>
Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits
<p>Supporting data associated with the manuscript "Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits". These include:</p> <ul> <li><strong>StringTie2.438.GEUVADIS.gtf.gz</strong> - Assembled transcriptome using RNA-seq from 438 samples from the GEUVADIS project by StringTie2</li> <li><strong>puQTL_nominal_output.txt.gz</strong> - puQTL results from QTLtools nominal pass</li> <li><strong>puQTL_conditional_output.txt.gz</strong> - puQTL results from QTLtools conditional pass</li> <li><strong>eQTL_nominal_output.txt.gz</strong> - eQTL results from QTLtools nominal pass</li> <li><strong>eQTL_conditional_output.txt.gz</strong> - eQTL results from QTLtools conditional pass</li> </ul> <p>See <a href="https://qtltools.github.io/qtltools/">https://qtltools.github.io/qtltools/</a> for column descriptions of QTL mapping results.</p>
Data for manuscript "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data"
<p>Output files generated by rMATS-turbo for the two example datasets described in the manuscript titled "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data".</p> <table> <tbody> <tr> <td>File</td> <td>Description</td> <td>Cell lines</td> <td>BioProject</td> </tr> <tr> <td>PC3E-GS689.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 1 described in the manuscript</td> <td>PC3E and GS689 cell lines</td> <td>PRJNA438990</td> </tr> <tr> <td>CCLE.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 2 described in the manuscript</td> <td>1,019 CCLE human cancer cell lines</td> <td>PRJNA523380</td> </tr> </tbody> </table> <p>A detailed description of the output files is available in the manuscript and the rMATS-turbo software GitHub repository (https://github.com/Xinglab/rmats-turbo).</p>
single cell RNA seq data of 3 healthy sample from frontal lobe and temporal lobe
<p><span>Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We provided single-nucleus transcriptome analysis in “fresh” human FL and TL which are integrated with genetic susceptibility, gene dysregulation in neuropsychiatric disease, and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB-neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. these data provide prefound insight into brain frontotemporal lobe.</span></p>
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>
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.