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21,239 results for “RNA-Seq”
Data and script for Laass et. al "Characterization of the transcriptome of Haloferax volcanii with mixed RNA-Seq"
<p>Data and script to accompany the manuscript "Characterization of the transcriptome of Haloferax volcanii with mixed RNA-Seq".</p>
Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries
<p>Processed data files for manuscript: "Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries" <a href="https://doi.org/10.1093/nar/gky1204">https://doi.org/10.1093/nar/gky1204</a> . Scripts for generating figures are found here: https://github.com/rnabioco/scrna-subsets</p>
The reference index files used for RNA-seq workflow benchmark in CWL-metrics manuscript
<p>The reference files used in the RNA-Seq workflow benchmark in the manuscript "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection" (https://doi.org/10.1101/456756).</p>
The output and the log files from RNA-Seq workflow benchmark for CWL-metrics manuscript
<p>The output files and log files generated by the workflow executions for RNA-Seq workflow benchmark by CWL-metrics, from the manuscript "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection" (https://doi.org/10.1101/456756).</p>
Training material for analysis small RNA-seq data (Galaxy Training Network tutorial)
<p>The data provided here is part of the Galaxy Training Network tutorial for analysis of small RNA-seq (sRNA-seq) data using mirdeep2 and miranda. This dataset is provided by INRA (Le Rheu, France).</p>
Dataset for: A graph-based algorithm for RNA-seq data normalization
<p>mRNA-seq assays on mouse tissues were downloaded from the ENCODE project and consolidated into matrices of expression</p>
Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA Data Archive
<p>[This repository contains the source data for the workflow presented in the manuscript "<strong>Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA</strong>". The workflow can be found here: http://gitlabscottgroup.med.usherbrooke.ca/gaspard/snakemake_blockbuster ]</p> <p>The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 115 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new tRNA and snoRNA genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distort the perceived architecture of the human transcriptome.</p>
Time-resolved single-cell RNA-seq using scSLAM-seq and GRAND-SLAM
<p>This is the example data for the protocol "Time-resolved single-cell RNA-seq using scSLAM-seq and GRAND-SLAM".</p> <p>The test data comprises sequencing data from 12 different cells. Five of them in mock condition with 4sU, five infected with MCMV with 4sU and 2 uninfected cells without 4sU. For each sample, paired-end sequencing was conducted and therefore two files exist for each sample. The files can be found at data/fastq.</p> <p>The reads should be mapped against the mouse genome and the MCMV genome, which are located in data/genome. The sequence is in fasta-fomat and the features in gtf-format.The scripts conducting quality control, read mapping and GRAND-SLAM analysis can be found in the processing folder. For each computational step in this protocol there is a shell script for running the necessary steps. Executing all scripts will reproduce the analysis of our test data</p>
Predicting Phenotypic Traits Using Conglomerate RNA-seq Datasets -- Datasets
<p>Phenotype Annotations and Corresponding Gene Expression Matrices created for the paper Hadish et al. [REF]. Raw Phenotypic Annotations were downloaded and processed from NCBI BioProject and raw RNA-seq data was downloaded from NCBI SRA. All annotations are contained within a single file for either Tissue or Age. Gene expression matrices are for each of the 12 species used in this project in their unproccessed form. </p> <p>Scripts used in this project are located here: </p> <p>GitLab Repository: </p> <p>https://gitlab.com/ficklin-lab/predicting-phenotypic-traits-using-conglomerate-rna-seq-datasets</p> <p>Paper Citation: </p> <p>[REF]</p>
Benchmarking Illumina RNA-seq fusion transcript detection methods - cancer cell lines RNA-seq
<p>Cancer cell line RNA-seq data (reads or names of reads from CCLE data) used for benchmarking Illumina-based fusion detection methods as used in:</p> <p>Haas, B.J., Dobin, A., Li, B. <em>et al.</em> Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. <em>Genome Biol</em> <strong>20</strong>, 213 (2019). https://doi.org/10.1186/s13059-019-1842-9</p> <p> </p> <p>For CCLE data, direct sharing of fastq files was not possible. CCLE data must be obtained from:</p> <p> https://portals.broadinstitute.org/ccle/home</p> <p>Instead, the identifiers for the reads leveraged as part of our study are made available, and these reads can be extracted from the CCLE fastq files directly once obtained from the primary source.</p> <p><br>For the non-CCLE data, the exact reads leveraged by our study are made directly available here in fastq format.</p>
Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks
<p>Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks” </p> <p>Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle </p> <p>Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health</p> <p>Description: </p> <p>This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:</p> <ul> <li> <p>all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study. </p> </li> <li> <p>all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration </p> </li> <li> <p>precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.</p> </li> <ul> <li> <p>The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds</p> </li> <li> <p>For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X. </p> </li> <li> <p>We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”</p> </li> <li> <p>The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables. </p> </li> </ul> <li> <p>WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors. </p> </li> </ul>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples - Time course data
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes only the 10X Flex time course data and analysis.</strong></p>
RNA-Seq data for multiple appendage regeneration in Bger
<p>The RNA-Seq data for the paper named "Hox-determined appendage regeneration restores cockroach courtship rituals"</p>
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>
Encompassing view of spatial and single-cell RNA-seq renews the role of the microvasculature in human atherosclerosis
<p>Here we generate an integrative high-resolution map of human atherosclerotic plaques combining single-cell RNA-seq from multiple studies and spatial transcriptomics data from 12 human specimens, with different stages of atherosclerosis. We show cell-type and atherosclerosis-specific expression changes and spatially constrained alterations in cell-cell communication.</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>
RNA-seq reveals altered gene expression levels in proximal tubular cell cultures compared to renal cortex but not during early glucotoxicity
AbstractCell cultures are often used to study physiological processes in health and disease. It is well-known that cells change their gene expression in vitro compared to in vivo, but it is rarely experimentally addressed. High glucose is a known trigger of apoptosis in proximal tubular cells (PTC). Here we used RNA-seq to detect differentially expressed genes in cultures of primary rat PTC, 3 days old, compared to cells retrieved directly from rat outer renal cortex and between PTC exposed to 15 mM glucose and control for 8 h. The expression of 6,174 genes was significantly up- or downregulated in the cultures of PTC compared to the cells in the outer renal cortex. Most altered were mitochondrial and metabolism related genes. Gene expression of proapoptotic proteins were upregulated and gene expression of antiapoptotic proteins were downregulated in PTC. Expression of transporter related genes were generally downregulated. After 8 h, high glucose had not altered the gene expression in PTC. The current study provides evidence that cells alter their gene expression in vitro compared to in vivo and suggests that short-term high glucose exposure can trigger apoptosis in PTC without changing the gene expression levels of apoptotic proteins.
RNA-seq data and results from in-vitro experiments for "Genetic control of fetal placental genomics contributes to development of health and disease" (Bhattacharya et al 2021)
<p>This dataset includes data and results from RNA-seq data collected from in-vitro experiments presented in "Genetic control of fetal placental genomics contributes to development of health and disease" (Bhattacharya et al 2021). Please check the README.txt file for more details.</p>
ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data (repository for simulated ONT RNA-seq data)
<p>Simulated ONT direct RNA and 1D cDNA sequencing data of varying sequencing depths (0.5 million, 1 million, 3 million, and 5 million simulated reads) used for benchmark evaluations of transcript discovery and quantification in our paper "ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data". All details can be found in the <strong>Materials and Methods</strong> section of the paper. </p> <p><em>HEK293T_DirectRNA.transcriptome_quantification.tsv</em> and <em>HEK293T_DirectRNA.transcriptome_quantification.tsv </em>are tab-separated files containing estimated raw read counts and normalized abundance values (in TPM) of transcripts annotated in GENCODE v34lift37. Transcript quantification was done using NanoSim (version 3.1.0). </p> <p><em>HEK293T_DirectRNA.NanoSim_500k.fastq.gz</em>,<em> </em><em>HEK293T_DirectRNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_DirectRNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_DirectRNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT direct RNA sequencing reads respectively. </p> <p><em>HEK293T_1DcDNA.NanoSim_500k.fastq.gz</em>,<em> HEK293T_1DcDNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_1DcDNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_1DcDNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT 1D cDNA sequencing reads respectively. </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>
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