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21,239 results for “RNA-Seq”
Bulk and single-cell RNA-seq of human fetal pancreatic organoids
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Influence of RNA-Seq library construction, sampling methods, and tissue harvesting time on gene expression estimation
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Single-cell RNA-seq of the rare virosphere reveals the native hosts of giant viruses in the marine environment
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Assembled RNA-seq data of Bougainvillea peruviana Thimma
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Single-cell RNA-seq of the embryonic zebrafish heads from wild-type siblings and betaPix CRISPR mutants at 1 dpf and 2 dpf
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RNA-seq reveals altered gene expression levels in proximal tubular cell cultures compared to renal cortex but not during early glucotoxicity
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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
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RNA-seq of Arabidopsis root growth responses to mechanical impedance
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RNA-seq data showing PLS role in gene expression control in Arabidopsis
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Data from: Subsets of tissue CD4 T cells display different susceptibilities to HIV infection and death: Analysis by CyTOF and single cell RNA-seq
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Evaluation of various RNA-seq approaches for identification of outrons in the flatworm Opisthorchis felineus
<p>As a result of spliced leader trans-splicing (SLTS), the original 5'-end (outron) of the transcripts is replaced by a short spliced leader sequence, donated from a specialized SL RNA. SLTS is involved in the RNA processing of more than half of <em>O. felineus</em> genes, making it hard to determine the structure of outrons and bona fide transcription start sites of the corresponding genes and operons, being based solely on mRNA-seq data. In the study, associated with the current dataset, we evaluated four experimental approaches for identifying outrons in <em>O. felineus</em> using low-depth massive parallel sequencing. Two of them were developed by us for targeted sequencing of already processed outrons. The first (<strong>SLi-RT</strong>) is based on a sequence-specific reverse transcription from the SL intron toward the 5'-end of the Y-branched outron. The second (<strong>SLi-BC</strong>) utilizes outron hybridization with an immobilized single-stranded DNA probe complementary to the SL intron. Additionally, two approaches to the sequencing of rRNA-depleted total RNA, the commercially available Zymo-Seq RiboFree kit (<strong>ZymoSeq</strong>) and digestion by Terminator<sup>TM</sup> 5´-Phosphate-Dependent Exonuclease (<strong>TermExo</strong>), were used to assess the identification of a wider range of transcripts compared to mRNA-seq. The dataset contains the raw paired-end data in FASTQ format for the corresponding RNA-seq libraries sequenced at a low depth on Illumina MiSeq platform.</p> <p> </p>
How to perform PCA on single-cell RNA-Seq data in three simple steps
<p>Video on YouTube: <a href="https://www.youtube.com/watch?v=IOe0X-q7FtE">https://www.youtube.com/watch?v=IOe0X-q7FtE</a></p> <p>My Twitter: <a href="https://twitter.com/flo_compbio">https://twitter.com/flo_compbio</a></p> <p>Savannah Bertrand’s fundraiser: <a href="https://www.gofundme.com/f/help-a-black-lesbian-academic-get-out?utm_source=twitter&utm_medium=social&utm_campaign=p_cf+share-flow-1">https://www.gofundme.com/f/help-a-black-lesbian-academic-get-out?utm_source=twitter&utm_medium=social&utm_campaign=p_cf+share-flow-1</a></p> <p>-------------------<br> References:</p> <p>Batson, Joshua, Loïc Royer, and James Webber. “Molecular Cross-Validation for Single-Cell RNA-Seq.” BioRxiv, September 30, 2019, 786269. <a href="https://doi.org/10.1101/786269">https://doi.org/10.1101/786269</a>.</p> <p>Grün, Dominic, Lennart Kester, and Alexander van Oudenaarden. “Validation of Noise Models for Single-Cell Transcriptomics.” Nature Methods 11, no. 6 (June 2014): 637–40.<a href="https://doi.org/10.1038/nmeth.2930"> https://doi.org/10.1038/nmeth.2930</a>.</p> <p>Hafemeister, Christoph, and Rahul Satija. “Normalization and Variance Stabilization of Single-Cell RNA-Seq Data Using Regularized Negative Binomial Regression.” Genome Biology 20, no. 1 (23 2019): 296. <a href="https://doi.org/10.1186/s13059-019-1874-1">https://doi.org/10.1186/s13059-019-1874-1</a>.</p> <p>Hsu, Lauren L., and Aedin C. Culhane. “Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data.” Frontiers in Oncology 10 (2020). <a href="https://doi.org/10.3389/fonc.2020.00973">https://doi.org/10.3389/fonc.2020.00973</a>.</p> <p>Sun, Shiquan, Jiaqiang Zhu, Ying Ma, and Xiang Zhou. “Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single-Cell RNA-Seq Analysis.” Genome Biology 20, no. 1 (10 2019): 269. <a href="https://doi.org/10.1186/s13059-019-1898-6">https://doi.org/10.1186/s13059-019-1898-6</a>.</p> <p>Townes, F. William, Stephanie C. Hicks, Martin J. Aryee, and Rafael A. Irizarry. “Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model.” Genome Biology 20, no. 1 (23 2019): 295. <a href="https://doi.org/10.1186/s13059-019-1861-6">https://doi.org/10.1186/s13059-019-1861-6</a>.</p> <p>Tsuyuzaki, Koki, Hiroyuki Sato, Kenta Sato, and Itoshi Nikaido. “Benchmarking Principal Component Analysis for Large-Scale Single-Cell RNA-Sequencing.” Genome Biology 21, no. 1 (20 2020): 9. <a href="https://doi.org/10.1186/s13059-019-1900-3">https://doi.org/10.1186/s13059-019-1900-3</a>.</p> <p>Wagner, Florian, Dalia Barkley, and Itai Yanai. “Accurate Denoising of Single-Cell RNA-Seq Data Using Unbiased Principal Component Analysis.” BioRxiv, June 17, 2019, 655365.<a href="https://doi.org/10.1101/655365"> https://doi.org/10.1101/655365</a>.</p> <p>Wagner, Florian. “Monet: An Open-Source Python Package for Analyzing and Integrating ScRNA-Seq Data Using PCA-Based Latent Spaces.” BioRxiv, 2020. <a href="https://doi.org/10.1101/2020.06.08.140673">https://doi.org/10.1101/2020.06.08.140673</a>.</p>
BecomingLTi - Dataset : RNA-seq from Embryo Periphery and Fetal Liver at stage 13.5 and Single-cell RNA-seq for Embryo Periphery at stage 13.5 and 14.5
<p><strong>Dataset from article</strong> : Distinct waves from the hemogenic endothelium give rise to layered Lymphoid Tissue Inducer cell ontogeny</p> <p><strong>Summary:</strong> During embryogenesis Lymphoid Tissue Inducer (LTi) cells are essential for lymph node organogenesis. These cells are part of the Innate Lymphoid Cell (ILC) family. Although their earliest embryonic hematopoietic origin is unclear, other innate immune cells were shown to be derived from both early hemogenic endothelium in the yolk-sac as well as the aorta-gonad-mesonephros. A proper model to discriminate between these locations was unavailable. In this study, using a new Cxcr4-CreERT2 lineage tracing model, we identify a major contribution from embryonic hemogenic endothelium, but not yolk-sac, towards the LTi progenitors. Conversely, embryonic LTi cells are replaced by hematopoietic stem cell derived cells in adult. We further show that within the fetal liver common lymphoid progenitors differentiate into highly dynamic alpha-lymphoid precursor cells, which at this embryonic stage preferentially mature into LTi precursors and establish their functional LTi cell identity only after reaching the periphery.</p> <p><strong>Data </strong>:</p> <p>1. SPlab_BecomingLTi_Bulk_Stage13.5_2tissues_00_RawData : Bulk RNA-seq data for Mouse Embryo Periphery and Fetal Liver at Stage 13.5. It contains matrix of gene expression counts for each sample in the dataset</p> <p>2. SPlab_BecomingLTi_Stage13.5_Periphery_CellRangerV3_00_RawData : Single-cell RNA-seq data for Mouse Embryo Periphery at stage 13.5. It contains the full output of CellRanger count (v3) analysis.</p> <p>3. SPlab_BecomingLTi_Stage14.5_Periphery_CellRangerV3_00_RawData : Single-cell RNA-seq data for Mouse Embryo Periphery at stage 14.5. It contains the full output of CellRanger count (v3) analysis.</p>
Becoming LTi - Dataset : Single-cell RNA-seq of Embryo Fetal Liver tissue at stage 13.5 days
<p><strong>Dataset from article</strong> : Distinct waves from the hemogenic endothelium give rise to layered Lymphoid Tissue Inducer cell ontogeny</p> <p><strong>Summary:</strong> During embryogenesis Lymphoid Tissue Inducer (LTi) cells are essential for lymph node organogenesis. These cells are part of the Innate Lymphoid Cell (ILC) family. Although their earliest embryonic hematopoietic origin is unclear, other innate immune cells were shown to be derived from both early hemogenic endothelium in the yolk-sac as well as the aorta-gonad-mesonephros. A proper model to discriminate between these locations was unavailable. In this study, using a new Cxcr4-CreERT2 lineage tracing model, we identify a major contribution from embryonic hemogenic endothelium, but not yolk-sac, towards the LTi progenitors. Conversely, embryonic LTi cells are replaced by hematopoietic stem cell derived cells in adult. We further show that within the fetal liver common lymphoid progenitors differentiate into highly dynamic alpha-lymphoid precursor cells, which at this embryonic stage preferentially mature into LTi precursors and establish their functional LTi cell identity only after reaching the periphery.</p> <p><strong>Data </strong>:</p> <p>1. SPlab_BecomingLTi_Stage13.5_FetalLiver_00_RawData : Single-cell RNA-seq data for Mouse Embryo Fetal Liver at stage 13.5. It contains the full output of CellRanger count (v3) analysis.</p>
RNA-Seq - membrane-type 1 matrix metalloproteinase
<p>Membrane-type 1 matrix metalloproteinase (MT1-MMP, MMP-14), a transmembrane proteinase with a short cytoplasmic tail, is a major effector of extracellular matrix (ECM) remodeling. Genetic silencing of MT1-MMP in mouse (<i>Mmp14<sup>-/-</sup></i>) and man causes dwarfism, osteopenia, arthritis and lipodystrophy, abnormalities ascribed to defective collagen turnover. We have previously shown non-proteolytic functions of MT1-MMP mediated by its cytoplasmic tail, where the unique tyrosine (Y573) controls intracellular signaling. The Y573D mutation blocks TIMP2/MT1-MMP-induced Erk and Akt signaling without affecting proteolytic activity. Here we report that a mouse with the MT1-MMP Y573D mutation (<i>Mmp14<sup>Y573D/Y573D</sup></i>) shows abnormalities similar to, but also different from those of <i>Mmp14<sup>-/-</sup></i> mice. Skeletal stem cells (SSC) of <i>Mmp14<sup>Y573D/Y573D</sup></i> mice show defective differentiation consistent with the mouse phenotype, which is rescued by wild-type SSC transplant. These results provide the first <i>in vivo</i> demonstration that MT1-MMP modulates bone, cartilage and fat homeostasis by controlling SSC differentiation through a mechanism independent of proteolysis.</p>
Data from: "De novo transcriptome assembly of the mountain fly Drosophila nigrosparsa using short RNA-seq reads" in Genomic Resources Notes Accepted 1 August 2014-30 September 2014
Drosophila (Drosophila) nigrosparsa is a habitat specialist restricted to the European montane/alpine zone (Bächli 2008). Mountain biodiversity is considered highly vulnerable to ongoing climate warming (IPCC 2013), and organisms at high altitudes have only limited possibility to shift to cooler habitats at elevations above (Pertoldi & Bach 2007). For such species, rapid evolution may offer a solution for long-term survival. We are establishing D. nigrosparsa as a model system to test the extent and tempo of adaptive evolution under thermal stress in the laboratory. In this study, we used Illumina high-throughput sequencing to assemble the species' transcriptome using the pooled mRNA from 22 developmental and physiological stages.
Data from: Characterization of the transcriptome and gene expression of four different tissues in the ecologically relevant sea urchin Arbacia lixula using RNA-seq
The sea urchin Arbacia lixula is a keystone species in Mediterranean ecosystems that drive landscape changes in littoral communities. However, genomic information available for the whole order Arbacioida is very limited. Using RNA-seq techniques, we have characterized the transcriptome of four different tissue types in A. lixula: the 'somatic' tissues (coelomocytes and digestive tissue) and the 'reproductive' tissues (ovary and testis), from two replicated cDNA libraries for each sample. Additionally, we performed a de novo assembly to build the 'reference' transcriptome, pooling reads of the four tissues, to analyse the differential expression (DE) in pairwise comparisons between tissues. The complete de novo assembly yielded 186 084 transcripts, with a sequence size limit of 100 nt, being 31% of them spliced isoforms. Approximately 21% of the transcripts had blast hits against proteins of metazoans (E < 10−5), being less than 2.2% functionally annotated. Between coelomocytes and digestive, 30 794 transcripts showed DE (~11.8% of them with blast hit), and 19 567 transcripts did so between testis and ovary (~28.5% of them with blast hit). Major GO-term categories upregulated in somatic tissues were those related to muscle contraction and energy generation in digestive, and lipid metabolism associated with immune response in coelomocytes. Between reproductive tissues, the major upregulated GO categories were related to energy generation in testis, and negative regulation of nucleotide metabolism in ovary. We particularly screened for a collection of target genes in each tissue because of their relevance for further studies on evolution and adaptation of echinoids.
Data from: Accuracy of allele frequency estimation using pooled RNA-Seq
For non-model organisms, genome-wide information that describes functionally relevant variation may be obtained by RNA-Seq following de novo transcriptome assembly. While sequencing has become relatively inexpensive, the preparation of a large number of sequencing libraries remains prohibitively expensive for population genetic analyses of non-model species. Pooling samples may be then an attractive alternative. To test whether pooled RNA-Seq accurately predicts true allele frequencies, we analyzed the liver transcriptomes of 10 bank voles. Each sample was sequenced both as an individually barcoded library and as a part of a pool. Equal amounts of total RNA from each vole were pooled prior to mRNA selection and library construction. Reads were mapped onto the de novo assembled reference transcriptome. High-quality genotypes for individual voles, determined for 23,682 SNPs, provided information on "true" allele frequencies; allele frequencies estimated from the pool were then compared to these values. "True" frequencies and those estimated from the pool were highly correlated. Mean relative estimation error was 21% and did not depend on expression level. However, we also observed a minor effects of inter-individual variation in gene expression and allele specific gene expression influencing allele frequency estimation accuracy. Moreover we observed strong negative relationship between minor allele frequency and relative estimation error. Our results indicate that pooled RNA-Seq exhibits accuracy comparable to pooled genome resequencing, but variation in expression level between individuals should be assessed and accounted for. This should help in taking account the difference in accuracy between conservatively expressed transcripts and these which are variable in expression level.
Data from: Natural selection and the genetic basis of osmoregulation in Heteromyid rodents as revealed by RNA-seq
One adaptation of ecological and evolutionary interest is the extraordinary ability of desert rodents to retain water during waste production. Much is known regarding the unique kidney physiology of kangaroo rats (Dipodomys spp.) and their ability to retain water during waste production, yet the genetic basis of these physiological adaptations is relatively unknown. Herein, we utilized RNA-seq data to conduct a comparative study to identify osmoregulatory genes expressed in Heteromyid rodents. We sequenced kidney tissue from two temperate desert species (Dipodomys spectabilis and Chaetodipus baileyi) from two separate subfamilies of the Heteromyidae and compared these transcriptomes to a tropical mesic species (Heteromys desmarestianus) from a third subfamily. The evolutionary history of these subfamilies provided a robust phylogenetic control that allowed us to separate shared evolutionary history from convergence. Using two methods to detect differential expression (DE), we identified 1,890 genes that showed consistent patterns of DE between the arid and mesic species. A three-species reciprocal BLAST analysis revealed 3,511 sets of putative orthologues that, upon comparison to known Mus musculus sequences, revealed 323 annotated and full length genic regions. Selection tests displayed evidence of positive selection (dn/ds >1) on 6 genes in the two desert species and remained significant for one of these genes after correction for multiple testing. Thus, our data suggest that both the coding sequence and expression of genes have been shaped by natural selection to provide the genetic architecture for efficient osmoregulation in desert-adapted Heteromyid rodents.
Summary statistics of eQTLs obtained from single-nuclei RNA-seq in 8 major brain cell-types for mendelian randomisation
<p>This dataset contains <em>cis</em>-eQTL summary statistics for 8 brain cell-types, generated on a snRNA-seq dataset on post-mortem brains from 391 individuals (full), as well as a controls-only (subset of full, 183 individuals).</p> <p>Genotype dosage matrices were obtained with <code>SeqArray</code>, where 0 = homozygous alt, 1 = heterozygous, 2 = homozygous ref https://bioconductor.org/packages/release/bioc/manuals/SeqArray/man/SeqArray.pdf . The eQTL models as applied by <code>MatrixEQTL</code> therefore use "ref" as the effect allele (as implemented in their additive model).</p> <p>The eQTL summary statistics for within the "full" and "controls-only" dataset have been packed into <code>.tar.gz</code> files for each cell-type, where unpacking will yield summary statistics by chromosome. Each file contains the following columns;</p> <p>1. <code>SNP</code> (in rsid format)</p> <p>2. <code>gene</code> (in symbol format)</p> <p>3. <code>t.stat</code> (t-statistic as determined by MatrixEQTL)</p> <p>4. <code>p.value</code> (linear model association p-value)</p> <p>5. <code>FDR</code> (false discovery rate as determined by MatrixEQTL)</p> <p>6. <code>beta</code> (effect size / slope of the linear model)</p> <p>7. <code>chrom</code> (chromosome in "chrN" format)</p> <p>8. <code>position</code> (SNP position, hg38 build)</p> <p>9. <code>effect_allele</code> (this is the "ref" allele as described above)</p> <p>10. <code>other_allele</code> (alternate allele)</p> <p>11. <code>maf</code> (minor allele frequency, as determined by SeqArray on this dataset)</p> <p> </p> <p>In addition, single-cell expression matrices in count format are available in the <code>single-cell_data.tar</code> archive for the full 391 individuals (2,348,438 cells). This archive contains processed single-cell counts as described in our manuscript for the 4 datasets included; "BRYOIS_192" (separated into "MS" and "AD" as per their publication), "MATTHEWS", "ROCHE_MPD92" and "MRC_60". In addition, a cell-level metadata file containing covariates and cell-type labels across all datasets is included (<code>cell_level_metadata.rds</code>). Cell barcodes and individual IDs have been renamed to preserve anonymity.</p> <p> </p> <p><strong>January 2025 update: </strong>Now published at <strong><em>Nature Genetics</em></strong>. <strong>https://www.nature.com/articles/s41588-024-02050-9</strong></p> <p><strong>May 2025 update: </strong>Added the aggregated pseudo "Bulk" eQTLs as seen in Fig 1. d. </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.