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757 results for “RNA-seq data”
Supporting data for "Software pipelines for RNA-Seq, ChIP-Seq and Germline Variant calling analyses in Common Workflow Language (CWL)"
<p>Datasets produced during the validation of CWL-based pipelines, designed for the analysis of data from RNA-Seq, ChIP-Seq and germline variant calling experiments. Specifically, the workflows were tested using publicly available High-throughput (HTS) data from published studies on Chronic Lymphocytic Leukemia (CLL) (accession numbers: E-MTAB-6962, GSE115772) and Genome in a Bottle (GIAB) project samples (accession numbers: SRR6794144, SRR22476789, SRR22476790, SRR22476791).</p> <p>The supporting data include:</p> <ul> <li>Differential transcript and gene expression results produced during the analysis with the CWL-based RNA-Seq pipeline</li> <li>Bigwig and narrowPeak files, differential binding results, table of consensus peaks and read counts of EZH2 and H3K27me3, produced during the analysis with the CWL-based ChIP-Seq pipeline</li> <li>VCF files containing the detected and filtered variants, along with the respective hap.py () results regarding comparisons against the GIAB golden standard truth sets for both CWL-based germline variant calling pipelines</li> </ul>
Data from: Single cell RNA-seq data of E18 fetal thymocytes from HEB Vav-iCre and Id3-KO mice and their wild type littermate counterparts
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RNA-seq and label-free quantitative proteomics data from: KDM4A serves as an α-tubulin demethylase regulating microtubule polymerization and cell mitosis
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RNA-seq data for LS180 and NCI-H358 Cancer cell lines
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Data for: RNA-Seq dataset of RBL-2H3 (RPKM)
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A novel method for using RNA-seq data to identify imprinted genes in social Hymenoptera with multiply mated queens
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RNA-seq data for embryonic chicken wings series treated with sonidegib
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RNA-seq and RT-qPCR data showing MDF role in RNA splicing and gene expression control in Arabidopsis
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RNA-seq data for an embryonic chicken digit tissue time-series, treated in vivo with smoothened agonist to induce ectopic feathers
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Assembled RNA-seq data of Bougainvillea peruviana Thimma
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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 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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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>
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.
Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data
<p>ConDecon is a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. This repository contains the raw data of the benchmarking analyses presented in the original publication using the pipeline of Avila-Cobos et al. (10.1038/s41467-020-19015-1). We used this pipeline to evaluate the ability of ConDecon and 17 other deconvolution methods to infer discrete cell type abundances in bulk tissues. The compressed file in this repository contains the synthetic bulk data, ground truth cell type proportions, and the predicted cell type proportions for each method and dataset associated with these analyses. Additional details can be found in the Methods section of the ConDecon publication.</p>
Code and data for maximum likelihood estimation (MLE) of P. falciparum age from RNA-seq data
<p>The original R code was written by Avi Feller and Jacob Lemieux and is described in this publication:</p> <p>Lemieux JE, Gomez-Escobar N, Feller A, Carret C, Amambua-Ngwa A, Pinches R, Day F, Kyes SA, Conway DJ, Holmes CC and Newbold CI (2009): Statistical estimation of cell-cycle progression and lineage commitment in <em>Plasmodium falciparum </em>reveals a homogeneous pattern of transcription in <em>ex vivo</em> culture. <em>Proc Natl Acad Sci U S A</em> 106(18), 7559-7564 doi: 10.1073/pnas.0811829106 <a href="https://www.pnas.org/doi/full/10.1073/pnas.0811829106">https://www.pnas.org/doi/full/10.1073/pnas.0811829106</a></p> <p>In this deposition, the original R script is adapted for command-line use. For usage instructions, see file "README.md".</p> <p>Furthermore, this deposition contains RNA-seq data for <em>P. falciparum</em> 3D7 cultured with blood from individuals with high, normal or low iron status (experiment 1) or with blood from a healthy donor in the presence vs. absence of 0.7 µM hepcidin (experiment 2) at 6 – 9 and 26 – 29 hours post erythrocyte invasion (hpi). For more details see publication:</p> <p>Iron transport pathways in the human malaria parasite <em>Plasmodium falciparum</em> revealed by RNA-sequencing, Wunderlich et al. (<a href="https://www.biorxiv.org/content/10.1101/2024.04.18.590068v1" target="_blank" rel="noopener">biorxiv</a>).</p> <p>Finally, this deposition contains a variety of pre-processed datasets obtained from the following references:</p> <p>Bártfai R, Hoeijmakers WA, Salcedo-Amaya AM, Smits AH, Janssen-Megens E, Kaan A, Treeck M, Gilberger TW, Françoijs KJ and Stunnenberg HG (2010): H2A.Z demarcates intergenic regions of the <em>Plasmodium falciparum</em> epigenome that are dynamically marked by H3K9ac and H3K4me3. <em>PLoS Pathog</em> 6(12), e1001223 doi: 10.1371/journal.ppat.1001223</p> <p>Broadbent KM, Broadbent JC, Ribacke U, Wirth D, Rinn JL and Sabeti PC (2015): Strand-specific RNA sequencing in <em>Plasmodium falciparum</em> malaria identifies developmentally regulated long non-coding RNA and circular RNA. <em>BMC Genomics</em> 16(1), 454 doi: 10.1186/s12864-015-1603-4</p> <p>López-Barragán MJ, Lemieux J, Quiñones M, Williamson KC, Molina-Cruz A, Cui K, Barillas-Mury C, Zhao K and Su X-z (2011): Directional gene expression and antisense transcripts in sexual and asexual stages of <em>Plasmodium falciparum</em>. <em>BMC Genomics</em> 12(1), 587 doi: 10.1186/1471-2164-12-587</p> <p>Otto TD, Wilinski D, Assefa S, Keane TM, Sarry LR, Böhme U, Lemieux J, Barrell B, Pain A, Berriman M, Newbold C and Llinás M (2010): New insights into the blood-stage transcriptome of <em>Plasmodium falciparum</em> using RNA-Seq. <em>Mol Microbiol</em> 76(1), 12-24 doi: 10.1111/j.1365-2958.2009.07026.x</p> <p>Siegel TN, Hon CC, Zhang Q, Lopez-Rubio JJ, Scheidig-Benatar C, Martins RM, Sismeiro O, Coppée JY and Scherf A (2014): Strand-specific RNA-Seq reveals widespread and developmentally regulated transcription of natural antisense transcripts in <em>Plasmodium falciparum</em>. <em>BMC Genomics</em> 15(1), 150 doi: 10.1186/1471-2164-15-150</p> <p>Wichers JS, Scholz JAM, Strauss J, Witt S, Lill A, Ehnold LI, Neupert N, Liffner B, Lühken R, Petter M, Lorenzen S, Wilson DW, Löw C, Lavazec C, Bruchhaus I, Tannich E, Gilberger TW and Bachmann A (2019): Dissecting the gene expression, localization, membrane topology, and function of the <em>Plasmodium falciparum</em> STEVOR protein family. <em>mBio</em> 10(4), doi: 10.1128/mBio.01500-19</p>
ScienceDex guides
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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.