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21,239 results for “RNA-seq”
RNA-seq based analysis of gene expression in thyroids of wild-type and Keap1 knockdown mice after exposure to excess iodide.
<p>C57BL/6J Keap1flox/flox mice were developed by Prof. Masayuki Yamamoto (DOI: 10.1016/j.bbrc.2005.10.185). These mice express lower levels of Keap1 because of the loxP site insertions (DOI: 10.1128/MCB.01591-09) and are designated as Keap1KD. 3-4 months old WT and Keap1KD mice fed a standard diet (KLIBA NAFAG 3242, Switzerland) containing 1.5 mg/kg sodium iodine were given normal tap water with or without 0.05% sodium iodide (Sigma, St Louis, MO, USA) for 7 days. Hence, the following groups of mice were included:</p> <p>WT mice on control water (n=8)-designated as wt in the file</p> <p>WT mice exposed to excess iodide (WT-IOD, n=6)- designated as wti in the file</p> <p>Keap1KD mice on control water (n=8)- designated as kp in the file</p> <p> and Keap1KD mice exposed to excess iodide (Keap1KD-IOD, n=6)- designated as kpI in the file. </p> <p> Mice were maintained in the animal facility of the Department of Physiology at the University of Lausanne in temperature-, light-, and humidity-controlled rooms with a 12-hour light/dark cycle. All animal procedures were in accordance with Swiss legislature and the study was approved by the Canton of Vaud SCAV.</p> <p>RNA from individual mouse thyroids was prepared as previously described (DOI: 10.1007/978-1-4939-3756-1_25) and was submitted to Alithea Genomics (Switzerland). The bulk RNA barcoding and sequencing (BRB-seq) libraries were generated and sequenced as described previously (DOI: 10.1186/s13059-019-1671-x) to a depth of approximately 1.2 million raw reads per sample.</p> <p>This research was funded by the Swiss National Science Foundation Research Grants 310030_212558, IZCOZ0_205415 and IZCOZ0_177070</p> <p></p> <p></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
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Data for: Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes
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Whole blood RNA-seq demonstrates an increased host immune response in individuals with cystic fibrosis who develop nontuberculous mycobacterial pulmonary disease
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RNA-Seq and proteomics of Crohn's disease: Terminal ileum of inflamed and non inflamed paired tissue biopsy
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Synthetic bulk RNA-Seq transcriptomic profiles representing 10 Cancer hallmarks
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Generating an expression matrix for droplet single-cell RNA-seq (dscRNA-seq) data
<p>This tutorial is adapted from the 'Generating an expression matrix' training session at the EBI (https://www.ebi.ac.uk/training/events/2019/single-cell-rna-seq-analysis-questions-clusters).</p>
Raw data of RNA-seq and IGV -2
<p>Raw data of RNA-seq and IGV of infected samples and mock infected samples</p>
Data from: Single cell RNA-seq analysis reveals that prenatal arsenic exposure results in long-term, adverse effects on immune gene expression in response to Influenza A infection
<p>Arsenic exposure via drinking water is a serious environmental health concern. Epidemiological studies suggest a strong association between prenatal<i> </i>arsenic exposure and subsequent childhood respiratory infections, as well as morbidity from respiratory diseases in adulthood, long after systemic clearance of arsenic.<i> </i>We investigated the impact of exclusive prenatal arsenic exposure on the inflammatory immune response and respiratory health after an adult influenza A (IAV) lung infection. C57BL/6J mice were exposed to 100 ppb sodium arsenite<i> in utero,</i> and subsequently infected with IAV (H1N1) after maturation to adulthood. Assessment of lung tissue and bronchoalveolar lavage fluid (BALF) at various time points post IAV infection reveals greater lung damage and inflammation in arsenic exposed mice versus control mice. Single-cell RNA sequencing analysis of immune cells harvested from IAV infected lungs suggests that the enhanced inflammatory response is mediated by dysregulation of innate immune function of monocyte derived macrophages, neutrophils, NK cells, and alveolar macrophages. Our results suggest that prenatal arsenic exposure results in lasting effects on the adult host innate immune response to IAV infection, long after exposure to arsenic, leading to greater immunopathology. This study provides the first direct evidence that exclusive prenatal exposure to arsenic in drinking water causes predisposition to a hyperinflammatory response to IAV infection in adult mice, which is associated with significant lung damage.</p>
A novel method for using RNA-seq data to identify imprinted genes in social Hymenoptera with multiply mated queens
<p>Genomic imprinting results in parent-of-origin dependent gene expression biased towards either the maternally- or paternally-derived allele at the imprinted locus. The kinship theory of genomic imprinting argues that this unusual expression pattern is a manifestation of intra-genomic conflict between the maternally- and paternally-derived halves of the genome that arises because they are not equally related to the genomes of social partners. The theory thus predicts that imprinting may evolve wherever there are close interactions among asymmetrically related kin. The social Hymenoptera with permanent caste differentiation are suitable candidates for testing the kinship theory because haplodiploid sex determination creates strong relatedness asymmetries and nursing workers interact closely with kin. However, progress in the search for imprinted genes in the social Hymenoptera has been slow, in part because tests for imprinting rely on reciprocal crosses that are impossible in most species. Here, we develop a method to systematically search for imprinting in haplodiploid social insects without crosses, using instead samples of pooled individuals collected from natural colonies. We tested this protocol using data available for the leaf-cutting ant <i>Acromyrmex echinatior</i>, providing the first genome-wide search for imprinting in any ant. While we identified several genes as potentially imprinted, none of the four genes tested could be verified as imprinted using digital droplet PCR, highlighting the need for higher quality genomic assemblies that accurately map duplicated genes.</p>
Raw mouse mammary RNA-Seq data (fastq)
<p>12 fastq files with 1000 reads each, 4 index files for chr 1 for mm10, targets files with sample information.</p> <p>References</p> <ul> <li><a href="http://www.ncbi.nlm.nih.gov/pubmed/25730472">http://www.ncbi.nlm.nih.gov/pubmed/25730472</a></li> <li><a href="http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60450">http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60450</a></li> <li><a href="http://hgdownload.soe.ucsc.edu/goldenPath/mm10/chromosomes/">http://hgdownload.soe.ucsc.edu/goldenPath/mm10/chromosomes/</a></li> <li><a href="http://www.ncbi.nlm.nih.gov/pubmed/20921232">http://www.ncbi.nlm.nih.gov/pubmed/20921232</a></li> <li><a href="http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18508">http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18508</a></li> </ul>
Sample dataset from RNA-Seq analysis of Ostreococcus tauri
<p>Sample dataset from:</p> <p>Lelandais et al. "<em>Ostreococcus tauri</em> is a new model green alga for studying iron metabolism in eukaryotic phytoplankton", <em>BMC Genomics</em> (2016).<br> DOI <a href="https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-2666-6">10.1186/s12864-016-2666-6</a></p>
Breast Cancer TGCA Microarray and RNA-seq Data for RNA-seq Titration Project
<p>Breast cancer from The Cancer Genome Atlas (TCGA; Cancer Genome Atlas Network, 2012) gene expression data from two platforms, microarray and RNA-seq, in PCL format and accompanying clinical data. Used as a test case for cross-platform normalization for machine learning applications because these data contain "matched" samples that were run on both platforms.</p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes the FACS, 10X 3', Parse, 10X Flex, and Hive data and analysis.</strong></p>
Datasets associated with the manuscript "Differential detection workflows for multi-sample single-cell RNA-seq data"
<p>In this Zenodo repository, we share the data that is required to reproduce all the analyses from our publication "Differential detection workflows for multi-sample single-cell RNA-seq data".</p> <p>This repository includes all* input data, intermediate results and final outputs that are represented in our manuscript. For a more elaborate description of the data, we refer to the companion GitHub. https://github.com/statOmics/DD_benchmarks for the benchmarks and https://github.com/statOmics/DD_cases for the case studies, respectively.</p>
Transcription start sites from capped small RNA-seq of rat nucleus accubmens and prefrontal cortex
<p>Small RNAs of ∼15–60 nt were size selected by denaturing gel electrophoresis starting from total RNA extracted from 14 rat brain tissue dissections. For csRNA libraries, cap selection was followed by decapping, adapter ligation, and sequencing. For input libraries, 10% of small RNA input was used for decapping, adapter ligation, and sequencing. After library quality check by gel electrophoresis, the samples were sequenced using the Illumina NextSeq 500 platform using 75 cycles single end. Sequencing reads were aligned to the rat mRatBN7.2 genome assembly using STAR v2.5.3a aligner with default parameters. Transcriptional start regions were defined using HOMER’s findPeaks tool. </p> <p>Duttke, S.H., Montilla-Perez, P., Chang, M.W., Li, H., Chen, H., Carrette, L.L.G., de Guglielmo, G., George, O., Palmer, A.A., Benner, C., et al. (2022). Glucocorticoid Receptor-Regulated Enhancers Play a Central Role in the Gene Regulatory Networks Underlying Drug Addiction. Front. Neurosci. 16, 858427.</p>
The VSMC RNA-seq datasets for "Strategies for Arterial Grafts Optimization at Single Cell Level"
<p>This dataset collection contains the human vascular smooth muscle cell (VSMC) bulk RNA-seq data generated in the study <strong><em>Strategies for Arterial Grafts Optimization at Single Cell Level</em></strong>.</p> <p>Primary human VSMCs (CP-H167, Pricella) were cultured in DM/F12 medium supplemented with FBS, EGF, bFGF, insulin, and penicillin (CM-P167, Pricella). The media was replaced per 48 hours. VSMCs were treated with 100ng/ml MIF (HY-P7387, MCE) for 24 hours in the absence or presence of 50 µMol ISO1 (HY-16692, MCE). The VSMCs with the passage number 3 to 5 were used in the study.</p> <p>Total RNA was extracted from cultured VSMC under different treatments using RNeasy Mini Kit (74104, Qiagen). A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. The library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. The index of the reference genome was built using Hisat2 v2.0.5. and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5.</p> <p>The sample info for the BAM files and their index files are listed as follows:</p> <table> <tbody> <tr> <td><strong>SampleCode</strong></td> <td><strong>Sample Description</strong></td> </tr> <tr> <td>C1</td> <td>VSMC (replicate 1)</td> </tr> <tr> <td>C2</td> <td>VSMC (replicate 2)</td> </tr> <tr> <td>C3</td> <td>VSMC (replicate 3)</td> </tr> <tr> <td>M1</td> <td>VSMC treated with MIF (replicate 1)</td> </tr> <tr> <td>M2</td> <td>VSMC treated with MIF (replicate 2)</td> </tr> <tr> <td>M3</td> <td>VSMC treated with MIF (replicate 3)</td> </tr> <tr> <td>M_I1</td> <td>VSMC treated with MIF and ISO1 (replicate 1)</td> </tr> <tr> <td>M_I2</td> <td>VSMC treated with MIF and ISO1 (replicate 2)</td> </tr> <tr> <td>M_I3</td> <td>VSMC treated with MIF and ISO1 (replicate 3)</td> </tr> </tbody> </table>
Isosceles paper simulated ovarian cell line ONT data (bulk RNA-Seq)
<div> <p>Simulated ovarian cell line ONT data (bulk RNA-Seq) for the Isosceles paper - more details can be found in the <a href="https://github.com/Genentech/Isosceles_Paper" target="_blank" rel="noopener">Isosceles_Paper</a> repository.</p> </div>
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>
Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
<p> </p> <p><strong>Abstract</strong></p> <p><strong>Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.</strong></p> <p><em><strong>SourceCode</strong></em></p> <p><strong><em>Main.R</em></strong><br> Please execute code along with the guide in this script</p> <p><em><strong>CreateBASiCS.R</strong></em><br> Create BASiCS chain objects, which include mean and noise data, from the raw count matrix. This step takes ~48 hours. Use of HPC is recommended</p> <p><em><strong>DifferenceTestAll.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from all cells. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>DifferenceTestCluster.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from cells belonging to the same cluster. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>STP12_detail_analysis.Rmd</strong></em><br> Output results mentioned in the discussion. Code is placed within the chunk.</p> <p><em><strong>CreateSupFig2.R</strong></em><br> Generate supplement figure 2</p> <p><em><strong>CreateFigure4AndS6.R</strong></em><br> Generate figure 4 and supplement figure 6</p> <p><strong>YeastGeneNameList.csv</strong><br> Data that convert ORF id to the common name<br> This data includes 3 columns, Name, ORF, and SGD.<br> - Name: Common name (e.g., TSC3)<br> - ORF: ORF id with Yeastract format (e.g., YBR058C.A)<br> - SGD: ORF id with SGD format (e.g., YBR058C-A)</p> <p><br> <strong>GeneticInteraction(ORF).csv</strong><br> Genetic interaction data downloaded from Yeastract. This data includes two-column, V1 and V2.<br> The interaction is described as two matrices such as V1 output to V2.</p> <p><strong>NoiseChangeGeneList</strong><br> This folder contains data required for generating tables 1 and S2.</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.