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2,614 results for “RNA-seq analysis”
Single-cell RNA-Seq and TCR-Seq analysis of PD-1+ CD8+ T-cells responding to anti-PD-1 and anti-PD-1/CTLA-4 immunotherapy in melanoma
<p><strong>This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference) </strong></p> <p> </p> <p><strong>*experimental design</strong></p> <p> Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*extract protocol</strong></p> <p> PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.</p> <p> </p> <p><strong>*library construction protocol</strong></p> <p> Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*library strategy</strong></p> <p> scRNA-seq and scTCR-seq</p> <p> </p> <p><strong>*data processing step</strong></p> <p> Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.</p> <p> Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).</p> <p> </p> <p> <strong>*genome build/assembly</strong></p> <p> Alignment was performed using prebuilt Cell Ranger human reference GRCh38.</p> <p> </p> <p><strong>*processed data files format and content</strong></p> <p> RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format.</p> <p>Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>file name</strong></p> </td> <td> <p><strong>file checksum</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>da2e006d2b39485fd8cf8701742c6d77</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>e125fc5031899bba71e1171888d78205</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>927241805d507204fbe9ef7045d0ccf4</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>8ca544d27f06e66592b567d3ab86551e</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>*processed data file </strong></p> </td> <td> <p><strong>antibodies/tags</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M1_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M1_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - C1_base_combined_therapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - C1_post_combined_therapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C2_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C2_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M2_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M2_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - M3_base_monotherapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - M3_post_monotherapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C3_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C3_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> </tbody> </table> <p> </p>
RNA-Seq analysis to identify differentially expressed genes in top and bottom leaves under Alternaria brassicicola infection
<p>The broccoli plants were infected with Alternaria brassicicola and RNA samples were extracted for control and inoculated plants at 10 days post inoculation. </p>
Galaxy tutorial for reference-based RNA-seq analysis
<p>https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html#functional-enrichment-analysis-of-the-de-genes</p> <p>the `compute` tool in the tutorial doesn't work. I used R to process the dataset and provide it so the students can continue with the tutorial.</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
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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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Codes for single-nucleus RNA-seq analysis of electrically treated spinal cord
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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>
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>
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>
Processed data - DegNorm: Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis
<p>Processed data from DegNorm:</p> <ul> <li>"_raw.txt": raw read counts matrix;</li> <li>"_DI.txt": Degradation index score matrix;</li> <li>"_DegNorm.txt": normalized read counts matrix from DegNorm output;</li> <li>"_coverage.Rdata": list of coverage matrix for the sample;</li> <li>"_countsTIN.txt": TIN normalized counts.</li> </ul> <p> </p>
Analysis of RNA-seq, DNA target enrichment, and Sanger nucleotide sequence data resolves deep splits in the phylogeny of cuckoo wasps (Hymenoptera: Chrysididae)
<p>The wasp family Chrysididae (cuckoo wasps, gold wasps) comprises exclusively parasitoid and kleptoparasitic species, many of which feature a stunning iridescent coloration and phenotypic adaptations to their parasitic life style. Previous attempts to infer phylogenetic relationships among the family's major lineages (subfamilies, tribes, genera) based on Sanger sequence data were insufficient to statistically resolve the monophyly and the phylogenetic position of the subfamily Amiseginae and the phylogenetic relationships among the tribes Allocoeliini, Chrysidini, Elampini, and Parnopini (Chrysidinae). Here, we present a phylogeny inferred from nucleotide sequence data of 492 nuclear single-copy genes (230,915 aligned amino acid sites) from 94 species of Chrysidoidea (representing Bethylidae, Chrysididae, Dryinidae, Plumariidae) and 45 outgroup species by combining RNA-seq and DNA target enrichment data. We find support for Amiseginae being more closely related to Cleptinae than to Chrysidinae. Furthermore, we find strong support for Allocoeliini being the sister lineage of all remaining Chrysidinae, while Elampini represent the sister lineage of Chrysidini and Parnopini. Our study corroborates results from a recent phylogenomic investigation which revealed Chrysidoidea as likely paraphyletic</p>
Example RNA-seq analysis of data from GSE119855
<p>Analysis of four samples of GEO accession GSE119855 with the IBU RNA-seq pipeline</p>
Reference-based RNA-seq data analysis (training data)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes RNA-Seq data from a study published by <a href="http://genome.cshlp.org/content/21/2/193.long">Brooks <em>et al.</em> 2011</a> to identify genes and exons that are regulated by Pasilla gene.</p>
Analysis of RNA-seq, DNA target enrichment, and Sanger nucleotide sequence data resolves deep splits in the phylogeny of cuckoo wasps (Hymenoptera: Chrysididae)
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Data from: RNA-Seq analysis identifies genes associated with differential reproductive success under drought-stress in accessions of wild barley Hordeum spontaneum
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RNA-seq analysis explored the mechanism of BHLHE22's involvement in TNBC progression
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RNA-seq analysis reveals the role of Omp16 during Brucella infected RAW264.7 cells
<p>Brucellosis is an endemic zoonotic infectious disease in the majority developing country that causes huge economic losses. As immunogenic and protective antigens at the surface of <i>Brucella</i> spp, outer membrance proteins (Omps) are particularly attractive for developing vaccine and could have more relevant role in host-pathogen interactions. Omp16, a homologue to peptidoglycan-associated lipoproteins (pals), is essential for <i>Brucella</i> survival in vitro. At present, the functions of Omp16 has been poorly studies. Here, the gene expression profile of RAW264.7 cells infected with <i>Brucella. suis</i> vaccine strain 2 (<i>B. suis</i> S2) and Omp16 mutant was analyzed by RNA-seq to investigate the cellular response immediately after <i>Brucella</i> entry. The RNA-sequence analysis revealed that a total 303 genes were significantly regulated by <i>B. suis</i> S2<i> </i>24 h postinfection. Of these, 273 differential expressed genes (DEGs) were up-regulated and 30 DEGs were down-regulated. These DEGs was mainly involved in innate immune signaling pathways, including pattern recognition receptors (PRRs), proinflammatory cytokines and chemokines by KEGG analysis. In Omp16 mutant infected cells, the expression of 52 total cells genes were significantly upregulated and that of 9 total cells genes were down-regulated compared to <i>B. suis</i> S2 infected RAW264.7 cells. The KEGG pathway analysis showed that several upregulated genes were proinflammatory cytokines and chemokines, such as interleukin-6 (IL-6), IL-11, IL-12β, CCL2 (C-C motif chemokine), and CCL22. All together, we clearly demonstrate that <i>Brucella </i>Omp16 can alter macrophage immune-related pathways to increase proinflammatory cytokines and chemokines to against <i>Brucella</i> infection, which provide insights into illuminating the <i>Brucella</i> pathogenic strategies.</p>
Healthy woodchuck genome with viral sequences appended used for single-cell RNA-seq analysis
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Data from: Understanding the genomic basis of adaptive response to variable osmotic niches in freshwater prawns: a comparative intraspecific RNA-Seq analysis of Macrobrachium australiense
Understanding the molecular basis of adaptive response to variable environmental conditions is a central goal of evolutionary biology. Here we sought to identify potential outlier SNPs (single nucleotide polymorphisms) in three wild populations of a freshwater prawn (Macrobrachium australiense) that are exposed to differing osmotic niches by using a comparative transcriptomics approach. De novo assembly of approximately 542 million (75 nt) pair end reads collected from 10 individuals revealed 123,396 longer contigs/transcripts of variable length, that showed 97.38% transcriptome assembly completeness. Differential gene expression (DGE) analysis of major osmoregulatory genes revealed that Calreticulin, Na+/H+ exchanger and V-type (H+) ATPase showed the highest expression levels in the Blunder Creek (low ionic) population, while Crustacean cardiovascular peptide (CCP), Na+/K+-ATPase, Na+/K+/2Cl- Co-transporter (NKCC) and Na+/HCO3 exchanger showed the highest expression levels in the Bulimba Creek (higher ionic) population. In total, 16 gene ontology (GO) term categories were functionally enriched among the three studied populations. We identified 4144 raw and 835 high quality filtered SNPs in the three M. australiense populations, of which 84 SNPs were identified as outliers. Outliers were detected in 4 important osmoregulatory genes that include: Calreticulin, Na+/H+ exchanger, Na+/K+-ATPase and V-type-(H+)-ATPase. All outliers in the osmoregulatory genes were located in non-coding regulatory regions (untranslated regions, UTRs) of the gene. We hypothesize that the outlier SNPs identified here in M. australiense populations exposed naturally to different osmotic conditions influence specific gene expression patterns that allow individuals to respond to local environmental conditions.
GO Enrichment Analysis on Single-Cell RNA-Seq Data
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