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21,239 results for “RNA-Seq”
Codes for single-nucleus RNA-seq analysis of electrically treated spinal cord
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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>
UCSC Toil RNA-Seq Recompute - TCGA TARGET GTEX recompute
<p>This is a re-upload for long-term-storage of the TCGA, TARGET GTEX recompute by the UCSC Toil consortium found in Xenabrowser.</p> <p>Original files may be found on Xenabrowser <a href="https://xenabrowser.net/datapages/?cohort=TCGA%20TARGET%20GTEx&amp;removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443">here</a>. The original publication can be found at the following DOI: <a href="https://doi.org/10.1038/nbt.3772">https://doi.org/10.1038/nbt.3772</a><br><br>Respectively, direct download URLs for these two files are:<br>- `expression_matrix.tsv.gz`: <a href="https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/TcgaTargetGtex_gene_expected_count.gz">https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/TcgaTargetGtex_gene_expected_count.gz</a><br>- `expression_matrix_metadata.txt.gz`: <a href="https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/TcgaTargetGTEX_phenotype.txt.gz">https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/TcgaTargetGTEX_phenotype.txt.gz</a></p> <p>Permission for re-upload was asked and obtained from the consortium.</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>
RNA-seq raw data
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Easy BAM files from practising RNA-seq and exome-seq visualization
<p>Two sets of small BAM files for practising RNA-seq and WES visualization (human) </p> <p>All data was processed from open access SRA datasets. </p> <p>Description:</p> <table> <tbody> <tr> <td>NAME</td> <td>RNA-EMT</td> <td>WES-LUNG</td> </tr> <tr> <td>SOURCE</td> <td>Yang et al. https://doi.org/10.1128/mcb.00019-16, SRA: PRJNA304419</td> <td>Ju et al. https://doi.org/10.1101/gr.133645.111; SRA: ERP001071)</td> </tr> <tr> <td>DESCRIPTION</td> <td>Human RNA-seq BAM files </td> <td>Human WES BAM files: Lung cancer</td> </tr> <tr> <td>SEQUENCING TYPE</td> <td>Illumina paired-end, stranded</td> <td>Illumina paired-end</td> </tr> <tr> <td>METHODS</td> <td>Original data are from cell lines subjected to epthithelium-mesenchymal transition. We took replicates corresponding to Day 0 (uninduced cells) and Day 7 (7 days after induction). Reads were aligned with STAR. BAM files are restricted to chromosome 18. Gene of interest: CDH2, up in mesenchymal state.</td> <td>WES were obtained from tumor and blood tissues from a non small cell lung cancer patient. Reads were aligned with BWA. A fraction of sequences corresponding to a panel of genes were retained. Genes of interest: DUSP27, KRAS</td> </tr> <tr> <td>MAPPED_TO</td> <td>HG19</td> <td>HG19</td> </tr> <tr> <td>SAMPLING RATE</td> <td>0.50%</td> <td>0.10%</td> </tr> <tr> <td>FILE SIZE</td> <td>2*40Mb</td> <td>2*7Mb</td> </tr> </tbody> </table>
Teaching Datasets for Single-Cell RNA-seq Analysis Course
<p>This repository contains teaching datasets used in the Single-Cell RNA-seq Analysis Course, which is part of the SeuratExtend project (<a href="https://github.com/huayc09/SeuratExtend">https://github.com/huayc09/SeuratExtend</a>). The course materials are available at <a href="https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1">https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1</a>, with code and scripts hosted at <a href="https://github.com/huayc09/single-cell-course">https://github.com/huayc09/single-cell-course</a>.</p> <p>The datasets include:</p> <ol> <li>3k Peripheral Blood Mononuclear Cells (PBMCs)</li> <li>Paired PBMC samples processed with 10x Genomics' 3' kit</li> <li>Paired PBMC samples processed with 10x Genomics' 5' kit</li> </ol> <p>All original data were obtained from 10x Genomics (<a href="https://www.10xgenomics.com/resources/datasets">https://www.10xgenomics.com/resources/datasets</a>) and processed for educational purposes.</p>
Ascorbic acid supports ex vivo generation of plasmacytoid dendritic cells from circulating hematopoietic stem cells: RNA-seq dataset
<p>Plasmacytoid dendritic cells (pDCs) constitute a rare type of immune cell with multifaceted functions, but their potential use as a cell-based immunotherapy is challenged by the scarce cell numbers that can be extracted from blood. Here, we systematically investigate culture parameters for generating pDCs from hematopoietic stem and progenitor cells (HSPCs). Using optimized conditions combined with implementation of HSPC pre-expansion, we generate an average of 465 million HSPC-derived pDCs (HSPC-pDCs) starting from 100,000 cord blood-derived HSPCs. Furthermore, we demonstrate that such protocol allows HSPC-pDC generation from whole blood HSPCs, and these cells display a pDC phenotype and function. Using GMP compliant medium, we observe a remarkable loss of TLR7/9 responses, which is rescued by ascorbic acid supplementation. Ascorbic acid induces transcriptional signatures associated with pDC-specific innate immune pathways suggesting an undescribed role of ascorbic acid for pDC functionality. This constitutes the first protocol for generating pDCs from whole blood, and lay the foundation for investigating HSPC-pDCs for cell-based immunotherapy.</p>
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
<p>A detailed list of the selected scRNA-seq datasets used in the paper, also provided in Additional file <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1898-6#MOESM1">1</a>: Table S1-S2.</p>
Data from: RNA-Seq reveals adaptive genetic potential of the rare Torrey pine (Pinus torreyana) in the face of Ips bark beetle outbreaks
<p>The ability of tree species to adapt to water stress and increased frequency of bark beetle outbreaks with climate change may increase with population size and standing genetic variation, calling into question the resilience of small, rare plant populations. The Torrey pine (<i>Pinus torreyana</i>) is a rare, genetically depauperate conifer that occurs naturally in a mainland and island population in southern California. Due to recent declines in the mainland population coinciding with drought and <i>Ips paraconfusus</i> bark beetle outbreaks, the species would benefit from an assessment of adaptive genetic diversity. Here, we use RNA-Seq to survey gene-coding diversity across 40 individuals to 1) characterize patterns of genetic diversity in the species and 2) test for genetic differentiation between trees that succumbed to beetle attack or survived following an outbreak. Consistent with previous studies, we found few genetic variants, with most SNPs occurring as fixed differences between populations. However, we found structure within the mainland and polymorphisms segregating in both populations. Interestingly, we found differentiation in genotypes between attacked and surviving trees and 11 SNPs associated with survival status, several of which had defense-related functions. While low diversity suggests limited adaptive capacity, genetic associations with survival in functionally relevant genes suggest adaptive potential for bark beetle defense. This initial study prompts future research to explore the genetic basis of putative resistance and suggests conservation efforts should protect surviving genotypes and the full spectrum of genetic diversity across populations to preserve the evolutionary potential of the species.</p>
single-cell RNA-seq of EHT process
<p>This file 'atfr.rds' contains onject including the raw and log-normalizd expression counts, and TF activity matrix generated from metaRegulon. In addition, the file 'cell_weight_pseudotime.rds' contains the pseudotime information.</p>
RUV-III-PRPS normalised data of the TCGA READ, COAD and BRCA RNA-seq studies.
<p>This repository contains the RUV-III-PRPS normalised data of the TCGA READ, COAD and BRCA RNA-seq studies. These studies were used to show how to use RUV-III-PRPS to remove unwanted variation from RNA-seq data. We refer to our bioRxiv paper for more details https://www.biorxiv.org/content/10.1101/2021.11.01.466731v1.</p>
Single-cell RNA-seq supplementary data
<p>These files are supplementary data for the article Laczik M, Erdős E, Ozgyin L, Hevessy Zs, Csősz É, Kalló G, Nagy T, Barta E, Póliska Sz, Szatmári I and Bálint BL: Extensive proteome and functional genomic profiling of variability between genetically identical human B-lymphoblastoid cells. The files are outputs from the 10x Genomics software Cell Ranger and Loupe Browser, they contain QC data for the single cell sequencing described in the article, and also comparative analyses between 3 datasets (GM22648old, GM22648new, GM22649). For further details please see the article.</p>
Gene-level read counts from bulk RNA-seq data for 38 follicular lymphoma diagnostic biopsies
<p>Conventional (bulk) RNA-sequencing was performed on unfractionated cell suspension or snap frozen whole tissue material. Total RNA was isolated with TRIzol reagent followed by purification over PureLink RNA Mini Kit columns (Invitrogen). RNA-seq was performed using a polyA-enriched strand-specific library construction protocol (doi: 10.1016/j.ccell.2016.02.009) and paired-end 75bp sequencing on an Illumina HiSeq 2500 instrument.</p> <p>Raw reads were aligned to the reference human genome assembly GRCh37 (hg19) using STAR (v2.5.2.a). To improve spliced alignment, STAR was provided with exon junction coordinates from the reference annotations (Gencode v19). We applied a modified version of a bioinformatics workflow for normalization of raw read counts and differential gene expression analysis<span> (</span>doi: 10.12688/f1000research.9005.3). Gene-level read counts were quantified using HTSEQ-count (v0.11.0; intersection-strict, reverse mode) (doi: 10.1093/bioinformatics/btu638). Genes showing low read counts (i.e., genes not showing counts per million (cpm) > 1.0 in at least 10% of samples) were removed from further analysis. Raw counts from expressed genes were then TMM-normalized and scaled to counts per million (CPM) using the edgeR (v3.22.2) package (doi: 10.1093/bioinformatics/btp616).</p> <p>Sample IDs correspond to those referenced in Wang X et al, Nature Communications (2022).</p>
Single-cell RNA-seq datasets derived from human parasitic nematode Brugia malayi microfilariae
<p>Single-cell RNA-seq data of the parasitic nematode <em>Brugia malayi</em> in the microfilariae development stage. Data includes untreated cellular transcriptional states and the transcriptional response to ivermectin (1 µM). </p> <p>The unfiltered gene expression matrix is provided as both a Seurat and AnnData object. Filtered datasets are provided as .csv files for direct import into R. </p>
RNA-Seq data of Raji cells transduced with retrovirus expressing WT or mutant IRF4
<p>Total RNA was extracted using RNAiso Plus (TaKaRa) from Raji cells transduced with retrovirus expressing WT or mutant IRF4. RNA-seq was performed by BGI (Beijing Genomic Institute, ShenZhen, China) using the BGISEQ-500 platform, pair end 100 bases read lengths. The sequencing data were filtered with SOAPnuke (v1.5.2). The clean reads were mapped to the reference genome using HISAT2 (v2.0.4). After that, Ericscript (v0.5.5) and rMATS (V3.2.5) were used to identify genes and differentially spliced genes (DSGs), respectively. Bowtie2 (v2.2.5) was applied to align the clean reads to the gene set, a database for this organism built by BGI with known and novel coding transcripts included, then the expression level of gene was calculated by RSEM (v1.2.12).</p>
CWL run of RNA-seq Analysis Workflow (CWLProv 0.5.0 Research Object)
<p>This workflow adapts the approach and parameter settings of <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL">Trans-Omics for precision Medicine (TOPMed)</a>. The <a href="https://w3id.org/cwl/view/git/018d344b12e9e1b888e21e0819096f9b337d371d/topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl">RNA-seq pipeline</a> originated from the Broad Institute. There are in total five steps in the workflow starting from:</p> <ol> <li>Read alignment using <strong>STAR</strong> which produces aligned BAM files including the Genome BAM and Transcriptome BAM.</li> <li>The Genome BAM file is processed using <strong>Picard MarkDuplicates.</strong> producing an updated BAM file containing information on duplicate reads (such reads can indicate biased interpretation).</li> <li><strong>SAMtools index</strong> is then employed to generate an index for the BAM file, in preparation for the next step.</li> <li>The indexed BAM file is processed further with <strong>RNA-SeQC</strong> which takes the BAM file, human genome reference sequence and Gene Transfer Format (GTF) file as inputs to generate transcriptome-level expression quantifications and standard quality control metrics.</li> <li>In parallel with transcript quantification, isoform expression levels are quantified by <strong>RSEM</strong>. This step depends only on the output of the STAR tool, and additional RSEM reference sequences.</li> </ol> <p>For testing and analysis, the workflow author provided example data created by down-sampling the read files of a TOPMed public access data. <em>Chromosome 12</em> was extracted from the <em>Homo Sapien Assembly 38</em> reference sequence and provided by the workflow authors. The required GTF and RSEM reference data files are also provided. The workflow is well-documented with a detailed set of instructions of the steps performed to down-sample the data are also provided for transparency. The availability of example input data, use of containerization for underlying software and detailed documentation are important factors in choosing this specific CWL workflow for CWLProv evaluation.</p> <p>This dataset folder is a <strong>CWLProv Research Object</strong> that captures the Common Workflow Language execution provenance, see <a href="https://w3id.org/cwl/prov/0.5.0">https://w3id.org/cwl/prov/0.5.0</a> or use <a href="https://pypi.org/project/cwl">https://pypi.org/project/cwl</a></p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built with:</p> <ul> <li>Processor 2.8GHz Intel Core i7</li> <li>Memory: 16GB</li> <li>OS: macOS High Sierra, Version 10.13.3</li> <li>Storage: 250GB</li> </ul> <ol> <li> <p>Install <strong>cwltool</strong></p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223</code></pre> </li> <li> <p>Install <strong>git lfs</strong><br> The data download with the git repository requires the installation of Git lfs:<br> <a href="https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs">https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs</a></p> </li> <li> <p>Get the data and make the analysis environment ready:</p> <pre><code class="language-bash">git clone https://github.com/FarahZKhan/cwl_workflows.git cd cwl_workflows/ git checkout CWLProvTesting ./topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/download_examples.sh</code></pre> </li> <li> <p>Run the following commands to create the CWLProv Research Object:</p> <pre><code class="language-bash">cwltool --provenance rnaseqwf_0.6.0_linux --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/Dockstore.json zip -r rnaseqwf_0.5.0_mac.zip rnaseqwf_0.5.0_mac sha256sum rnaseqwf_0.5.0_mac.zip > rnaseqwf_0.5.0_mac_mac.zip.sha256</code></pre> </li> </ol> <p>The <a href="https://github.com/FarahZKhan/cwl_workflows">https://github.com/FarahZKhan/cwl_workflows</a> repository is a frozen snapshot from <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL">https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL</a> commit <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL/tree/027e8af41b906173aafdb791351fb29efc044120">027e8af41b906173aafdb791351fb29efc044120</a></p>
Truncated RNA-Seq ENCODE Dataset. Chr 22 only
<p>Truncated from ENCSR754WLW: https://www.encodeproject.org/experiments/ENCSR754WLW/</p>
RNA-seq Data for Bacteroides fragilis Toxin Suppresses METTL3-Mediated m6A Modification in Macrophage to Promote Inflammatory Bowel Disease
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iSLK cell line RNA-Seq
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