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21,239 results for “RNA-seq”
Mammary single-cell RNA-seq analysis and prostate cancer survival as a function of H2AFJ expression for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial cells
<p>H2A.J is a poorly studied mammalian-specific variant of histone H2A. We used immunohistochemistry to study its localization in various human and mouse tissues. H2A.J showed cell-type specific expression with a striking enrichment in luminal epithelial cells of multiple glands including those of breast, prostate, pancreas, thyroid, stomach, and salivary glands. H2A.J was also highly expressed in many carcinoma cell lines and in particular, those derived from luminal breast and prostate cancer. H2A.J thus appears to be a novel marker for luminal epithelial cancers. Knocking-out the H2AFJ gene in T47D luminal breast cancer cells reduced the expression of several estrogen-responsive genes which may explain its putative tumorigenic role in luminal-B breast cancer.</p>
Datasets, reproducible codes, and results for evaluating differential expression analysis methods on population-level RNA-seq data
<p>This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, <em>Genome Biology</em> 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.</p>
Joint embedding of vertebrate brain single-cell RNA-Seq using sequence or structure
<p>Embeddings of single-cell RNA-Seq data from three adult vertebrate brain datasets into Orthogroup feature space or Structural cluster feature space. Orthogroups were generated using OrthoFinder v5.5.0; Structural clusters were assigned by using FoldSeek to cluster AlphaFold-v4 structural predictions.<br> <br> The three datasets used as the basis for these embeddings were:</p> <ul> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3768152">"Brain8"</a> from the <a href="https://www.frontiersin.org/articles/10.3389/fcell.2021.743421/full">Jiang et al. 2021</a> zebrafish cell atlas (files beginning with GSM3768152)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2906405">"Brain1"</a> from the <a href="https://www.sciencedirect.com/science/article/pii/S0092867418301168#sec4">Han et al. 2018</a> mouse cell atlas (files beginning with GSM2906405)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM6214268">"Xenopus_brain_COL65"</a> from the <a href="https://www.nature.com/articles/s41467-022-31949-2">Liao et al. 2022</a> Xenopus laevis adult cell atlas (files beginning with GSM6214268)</li> </ul> <p>For each dataset, we also generated a standardized cell type annotation file based on the author's originally provided cell type annotation data. The first column is the cell barcode for that species and the second column is the original study's cell type annotation for that cell.</p> <p>For the Xenopus brain data, we removed around ~18k cells that were not annotated in the original data to simplify data analyses - these are reflected in the files with the "subsampled" suffix. Subsampled versions of the data are also available for the joint embedding space (prefixed with "DrerMmusXlae").</p> <p>For the final datasets used in our analyses, we also provide features x cell matrices as .h5ad files for smaller file sizes and faster loading using Scanpy. </p> <p>For visualizing our UMAP plots of our top200 embedding space, we provide ".tsv" files with a variety of metrics and the x and y positions of each cell in the UMAP. See "DrerMmusXlae_adultbrain_FoldSeek_plotlydata.tsv" and "DrerMmusXlae_adultbrain_OrthoFinder_plotlydata.tsv"</p> <p>These data are part of the Arcadia Science Pub titled <a href="https://doi.org/10.57844/arcadia-vw5e-2670">"Comparing gene expression across species based on protein structure instead of sequence"</a>.</p>
RNA-Seq data from: Hox genes modulate physical forces to differentially shape small and large intestinal epithelia
<p>Hox genes are highly conserved, master regulators of spatial patterning in the embryo, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of the small and large intestines early in embryogenesis, which ultimately leads to their specialization in both form and function. While the midgut forms villi, the hindgut develops flat, brain-like sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements and mathematical modeling, we demonstrate that the posterior Hox gene Hoxd13 regulates biophysical phenomena that shape the hindgut lumen. We further show that Hoxd13 acts through the TGFβ pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme; together, these features lead to hindgut surface buckling. TGFβ, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional genetic identity that direct posterior intestinal morphogenesis. </p> <p>To identify genes and pathways that are directly or indirectly regulated by Hoxd13 to affect posterior gut morphogenesis in the chick, we compared mesodermal transcriptomes of wild-type midgut and hindgut intestinal samples, as well as mesodermal samples from a Hoxd13-overexpressing midgut at E12 and E14. Tissues were dissected and endoderm layers were removed manually before RNA extraction and downstream processing. Unbiased clustering was used to identify genes commonly differentially expressed in the hindgut and Hoxd13-misexpressing midgut. This submission contains bulk RNA-seq raw data (fastq.bz2 files) and processed .txt files with read counts. Experiment information is provided in .xlsx Metadata file used for NCBI GEO submission.</p>
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors
<p>This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file. </p>
Data for "Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis"
<p>The files named <code>df_scran.csv</code>, <code>df_seurat.csv</code>, <code>df_zinbwave.csv</code>, <code>df_dca.csv</code>, and <code>df_scvi.csv</code> contain one row per configuration that we ran successfully.</p> <p>The files named <code>DATASET.METHOD.h5ad</code> are encoded with anndata <code>v0.7.0</code> (be careful as they are not readable with previous versions) and contain 100 embeddings each. The embeddings are in the <code>obsm</code> attribute of the object. All the embeddings can be listed with the <code>obsm_keys()</code> method. The name of the embedding contains the parameters used to generate that embedding and are written like that <code>method=zinbwave.dims=10.epsilon=1000.features=300.gene_covariate=0</code>.</p> <p> </p> <p>For questions on this dataset please contact fraimundo@google.com</p>
Single cell RNA-seq Data - Dissecting the functional reprogramming of the microenvironment in bone marrow fibrosis at the single cell level
<p>We provide results regarding the bioinformatic analysis of scRNA-seq from distinct bone marrow fibrosis mouse models and human samples.</p> <p> </p> <p>These include:</p> <p>Robjects&Markdown - R markdown and R objects with QC statistics, UMAP and final scRNA-seq data sets.</p> <p>Markers - Excel tables with cluster specific marker genes.</p> <p>DE Genes - Excel table with DE genes when comparing cells in control vs. disease condition per cluster.</p> <p>GO Analysis - Gene enrichment analysis of either DE genes. These are divided by either UP or down regulated genes.</p>
EI Single-Cell RNA-Seq Workshop 2020
<p>Datasets to be used for the "Single-Cell RNA-Seq Workshop 2020" at the Earlham Institute, Norwich, UK.</p>
Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq
<p><strong><a href="https://gitlab.com/bu_cnio/Beyondcell">Beyondcell</a> </strong>is a methodology for the identification of drug vulnerabilities in single cell RNA-seq data. To this end, <strong>Beyondcell</strong> focuses on the analysis of drug-related commonalities between cells by classifying them into distinct therapeutic clusters. We have validated the tool in a population of MCF7-AA cells exposed to 500nM of bortezomib and collected at different time points: t0 (before treatment), t12, t48 and t96 (72h treatment followed by drug wash and 24h of recovery) obtained from <a href="https://www.nature.com/articles/s41586-018-0409-3"><strong><em>Ben-David U, et al., Nature, 2018</em></strong></a>. Here, you can find the integrated Seurat object obtained from this analysis. This object is meant to help users follow <strong>Beyondcell's</strong> <a href="https://gitlab.com/bu_cnio/Beyondcell/-/tree/master/tutorial/analysis_workflow">analysis workflow</a>.</p> <p> </p>
Differentiation of human monocytes into macrophages (RNA-seq, Salmon 1.4.0, GENCODE 36)
<p>RNA-seq of differentiation of human monocytes into macrophages as described in:</p> <p>Phanstiel et al "Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development" Molecular Cell, Volume 67, Issue 6, 2017, Pages 1037-1048.e6.</p> <p>https://doi.org/10.1016/j.molcel.2017.08.006</p> <p>See publication for full author list.</p> <p>Data from publication was reprocessed by Michael Love. Paired end reads were quantified with Salmon 1.4.0 and GENCODE 36 human transcripts.</p>
RNA-Seq read counts from monocyte- and ips-derived macrophages
<p>This file contains raw RNA-Seq read counts from monocyte-derived macrophages (MDMs) and induced pluripotent stem cell-derived macrophages (IPSDMs) before and after 6 hours stimulation after LPS.</p> <p>The file was created using featureCounts. The first two columns are:</p> <ul> <li>gene_id: Ensembl 74 gene id.</li> <li>length: Length of the gene in bp.</li> </ul> <p>The MDM samples are:</p> <ul> <li>B1_ctrl</li> <li>B1_LPS</li> <li>B4_ctrl</li> <li>B4_LPS</li> <li>B5_ctrl</li> <li>B5_LPS</li> <li>B2_ctrl</li> <li>B2_LPS</li> <li>B3_ctrl</li> <li>B3_LPS</li> </ul> <p>The IPSDM samples are:</p> <ul> <li>CRL1_ctrl</li> <li>CRL1_LPS</li> <li>S7RE_ctrl</li> <li>S7RE_LPS</li> <li>FSPS10C_ctrl</li> <li>FSPS10C_LPS</li> <li>FSPS11B_ctrl</li> <li>FSPS11B_LPS</li> </ul> <p> </p>
Training material for small RNA-seq data analysis (Galaxy Training Network tutorial)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes small RNA-seq (sRNA-seq) data from a study published by Harrington et al. (DOI:10.1186/s12864-017-3692-8) to detect differential abundance of various classes of endogenous short interfering RNAs (esiRNAs). The goal of this study was to investigate "connections between differential retroTn and hp-derived esiRNA processing and cellular location, and to investigate the potential link between mRNA 3’ end cleavage and esiRNA biogenesis." To this end, sRNA-seq libraries were constructed from triplicate <em>Drosophila</em> tissue culture samples under conditions of either control RNAi or RNAi knockdown of a factor involved in mRNA 3’ end processing, <em>Symplekin</em>. This dataset (GEO Accession: GSE82128) consists of single-end, size-selected, non-rRNA-depleted sRNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to a subset of interesting transcript features including: (1) transposable elements, (2) <em>Drosophila</em> piRNA clusters, (3) <em>Symplekin</em>, and (4) genes encoding mass spectrometry-defined protein binding partners of <em>Symplekin</em> from Additional File 2 in the indicated paper by Harrington et al. More details on features 1 and 2 can be found here: https://github.com/bowhan/piPipes/blob/master/common/dm3/genomic_features (piRNA_Cluster, Trn). All features are from the <em>Drosophila</em> genome Apr. 2006 (BDGP R5/<em>dm3</em>) release.</p>
T-DNAreader: fast and precise identification of T-DNA insertion sites using RNA-seq data
<p><em><span>Agrobacterium</span></em><span>-mediated plant transformation, which enables the delivery of DNA using transfer DNA (T-DNA) binary vectors, is an essential technique in plant research. T-DNAs randomly integrate into the host genome and multiple T-DNAs can integrate in a single transformation event, necessitating the development of efficient, reliable tools to identify the T-DNA insertion sites (TISs). Here, we developed T-DNAreader to identify TISs from RNA-sequencing (RNA-seq) data with high precision, sensitivity, and speed, outperforming existing tools. T-DNAreader detected previously unknown TISs in characterized mutant plants. Overall, T-DNAreader enables the simple identification of TISs within transcribed regions and standardizes the characterization of T-DNA-containing transgenic plants.</span></p>
The PODER cross-ancestry long-read RNA-seq dataset
<p>Table legends:</p> <ul> <li>column_descriptions.docx: Extended description of columns in selected tables.</li> <li>00_sample_metadata: Sample and experimental metadata pertaining to each LR-RNA-seq sample.</li> <li>01_uma_gtf: Unfiltered merged annotation (UMA) GTF for all spliced intron chains discovered by each of the tools.</li> <li>02_uma_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in the UMA annotation used to filter transcripts.</li> <li>03_poder_gtf: Final PODER GTF, including predicted CDSs for novel transcripts and annotated CDSs for known transcripts.</li> <li>04_poder_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in PODER.</li> <li>06_poder_t_counts: PODER transcript counts in each sample as quantified by lr-kallisto.</li> <li>07_poder_g_counts: PODER gene counts in each sample as quantified by lr-kallisto.</li> <li>08_mage_t_counts_poder: Transcript counts for the MAGE RNA-seq dataset computed using the PODER annotation with kallisto.</li> <li>09_mage_t_counts_gencode: Transcript counts for the MAGE RNA-seq dataset computed using the GENCODE annotation with kallisto.</li> <li>10_mage_t_counts_enh: Transcript counts for the MAGE RNA-seq dataset computed using the Enhanced GENCODE annotation with kallisto.</li> <li>11_astu: Allele-specific transcript usage results.</li> <li>12_ase: Allele-specific expression results.</li> <li>13_mage_tau: Tau values for Enhanced GENCODE transcripts computed on the MAGE RNA-seq dataset.</li> <li>14_gwas_enrichments: GWAS enrichment results for ASTU genes overlapping GWAS genes.</li> <li>16_inter_catalog_overlap: Boolean detection of transcripts across PODER, annotations (GENCODE, RefSeq) and other RNA-seq / LR-RNA-seq-derived transcript catalogs (CHESS, GTEx, ENCODE4).</li> <li>17_personalized_hg38: Isoforms and their intron chains detected using personalized-GRCh38s.</li> <li>18_enh_gencode: Enhanced GENCODE GTF with novel transcripts from PODER added to all annotated transcripts from GENCODE v47</li> </ul>
Critical Assessment of RNA-Seq Differential Expression
<p><strong>Warden and Wu Preprint</strong>: <a href="https://www.biorxiv.org/content/10.1101/2024.02.10.579728v1">v1</a></p> <p>In general, this primarily focuses on the following types of comparisons:</p> <ol> <li>Cell line experiments with over-expression or knock-down to define a known causal gene, with processing starting with public reads.</li> <li>Processed TCGA (The Cancer Genome Atlas) data for breast cancer (BRCA) to compare gene expression by immunohistochemistry status (ER/ESR1, PR/PGR, or HER2/ERBB2).</li> </ol> <p>Differential expression methods include the following:</p> <ul> <li><em>edgeR (GLM)</em></li> <li><em>edgeR-robust (GLM)</em></li> <li><em>edgeR (QL)</em></li> <li><em>edgeR-robust (QL)</em></li> <li><em>DESeq1</em></li> <li><em>DESeq2</em></li> <li><em>limma-voom</em></li> <li><em>limma-trend (CPM)</em></li> <li><em>limma-trend (FPKM/RPKM)</em></li> <li><em>ANOVA (log2 FRPKM/RPKM)</em></li> </ul> <p>The most common preprocessing strategies include STAR, TopHat2, and Salmon. However, a limited amount of additional processing with HISAT2, kallisto, Bowtie2 (+eXpress), and Bowtie1 (+RSEM) is also provided.</p> <p>Most STAR and TopHat2 alignments use htseq-count for quantification, as well as running cuffdiff (for single variable 2-group comparisons). However, a limited amount of additional processing with featureCounts is also provided.</p> <p>Most STAR and TopHat2 alignments start with the public <strong>forward</strong> reads, even if paired-end data was available.</p>
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>
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>
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.