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757 results for “RNA-seq data”
RNA-seq data of HepG2 cultured in 2D or as organoids
<p>Cancer cells cultured as organoids could better represent cancer cells grown in vivo and were shown to harbor increased stemness compared with cancer cells in 2D culture. Hep G2 cell line was used as parental cell line to establish a liver cancer organoid line. Hep G2 2D culture and organoid culture were subjected to bulk RNA-seq. </p>
RNA-Seq data of common woodlice Porcellio scaber
<p>This dataset contains raw transcriptome sequences of common woodlice <em>Porcellio scaber</em> (Crustacea: Isopoda), an important model species in ecotoxicology, developmental biology, immunology and physiology studies. In this context, dataset (transcriptome) provides a cornerstone for all future gene-based research of <em>P. scaber</em>. </p> <p>For transcriptome construction, total RNA was extracted from the whole organism of <em>P. scaber</em>. The extracted RNA was checked for quantity, purity, and integrity and sent to Novogene (United Kingdom) for sequencing. The transcriptome was generated by high-throughput sequencing using an Illumina NovaSeq 6000 platform with 150 bp paired-end option. The dataset contains raw reads recorded in FASTQ format; Ps2_1.fq and Ps2_2.fq, containing Read 1 and Read 2 for paired-end sequencing, respectively.</p>
Simulated RNA-seq data for differential splicing analysis with covariates
<p>The repository includes alignments of simulated RNA-seq data for evaluating differential splicing detection with covariates. Starting from an empirical transcript expression matrix trained on an RNA-seq data set from lung fibroblasts (GenBank A# SRR493366) and using GENCODE v.41 as reference, 11.5 million 100 bp long paired-end reads were generated per sample, from 2,000 genes with two or more expressed isoforms. RNA-seq data was simulated for one ‘condition’, with values ‘control’, ‘disease’ and ‘stage2’, with one covariate, ‘biological sex’, with values ‘M’ and ‘F’. 5 samples each were simulated for each (condition x sex) category. Changes were simulated in the expression (DE) and/or the splicing ratio (DS) of genes as follows. Changes in expression (DE) were simulated by either halving or doubling the expression level of the gene. Changes in splicing ratios (DS) were simulated by swapping the expression levels of the gene’s top two transcript isoforms. All RNA-seq data was mapped to the hg38 genome with the spliced alignment tool STAR v2.7.10a.</p> <p> <em><u>Pairwise comparison alignment set</u></em>: Differences due to ‘condition’ between two states, ‘control’ and ‘disease’, were simulated at 600 genes, including 200 DE, 200 DS and 200 DE+DS genes. Differences in ‘biological sex’ (covariate) were represented as changes in 300 genes, including 100 from each of the DS, DE and DE+DS categories. Hence, the target gene set for differential splicing ratio (DSR)<em> pairwise comparisons </em>consists of the pooled 200 DS and 200 DS+DE genes differentially spliced between the ‘control’ and ‘disease’ states, while for differential splicing abundance (DSA)<em> pairwise comparisons </em>the target gene set is the set of 600 modified genes, 200 in each of the DS, DE and DS+DE categories.</p> <p> <em><u>Multiway (3-way) comparison alignment set:</u></em> Additional changes between ‘disease’ and ‘stage2’ were made to 100 of the previously modified genes, as well as to a set of 200 additional genes not encountered previously, for each of the categories DE, DS and DE+DS. Therefore, for <em>DSR three-way comparisons</em>, the target gene set represents the 800 genes simulated as being DS or DE+DS between any of the ‘control’, ‘disease’ and ’stage2’ categories, while for the <em>multi-way DSA comparisons</em> the target is the full set of 1,200 genes (400 DE, 400 DS and 400 DE+DS) simulated to have changed between any of the 'control', 'disease’ and ‘stage2’ states.</p> <p> <em><u>Further details:</u></em> See the ‘key’ directories in each package for the gene lists.</p>
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
<p>In this repository we provide the datasets for the results of the Cellstates method as shown in the article: “Identifying cell states in single-cell RNA-seq data at statistically maximal resolution” by Pascal Grobecker, Thomas Sakoparnig, and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: https://doi.org/10.1101/2023.10.31.564980</p> <p>There is a README file that describes the contents and formats of each of the tab-separated values files. There is one subdirectory containing files with Cellstates' results on the dataset of Zeisel et al. (DOI: 10.1016/j.cell.2018.06.021) which is accompanied by another README file describing the formats and contents of these files. </p>
Example RNA-Seq Data
<p>This is an RNA-Seq small example dataset by transforming data originally from Guilgur et al. (2012). It consists of data corresponding to a small selection of Drosophila melanogaster genes. This dataset includes sequencing reads as well as gene annotation.</p>
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>
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>
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>
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>
Single-cell RNA-seq count data used in differential expression benchmark study
<p>Count matrices and meta data tables from simulated and real world immune cell single-cell RNA-seq experiments.</p> <p>All files are in Rds format and can be read by R using "readRDS()". </p> <ul> <li>10k_*: These files contain a filtered version of the 10k Human PBMCs, 3' v3.1 data <a href="https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">published</a> by 10x Genomics</li> <li>blueprint_data.Rds: This file contains the bulk RNA-seq data downloaded from <a href="http://dcc.blueprint-epigenome.eu">BLUEPRINT</a></li> <li>blueprint_immune_comparisons.Rds: Results from running three bulk RNA-seq differential expression methods</li> <li>sim_data*: These files contain the count matrices and meta data tables for the simulated data. Every file contains a list of 13 replicates.</li> </ul> <p> </p>
RNA-seq and RT-qPCR data showing MDF role in RNA splicing and gene expression control in Arabidopsis
<p>Plants respond to environmental stresses through controlled stem cell maintenance and meristem activity. One level of transcriptional control is RNA alternative splicing. However the mechanistic link between stress, meristem function and RNA splicing is poorly understood. The MERISTEM-DEFECTIVE (MDF)/DEFECTIVELY ORGANIZED TRIBUTARIES (DOT2) gene of Arabidopsis encodes a SR-related family protein, required for meristem function and leaf vascularization, and is the likely orthologue of the human SART1 and yeast snu66 splicing factors. MDF is required for the correct splicing and expression of key transcripts associated with root meristem function. We identified RSZ33 and ACC1, both known to regulate cell patterning, as splicing targets required for MDF function in the meristem. MDF expression is modulated by osmotic and cold stress, associated with differential splicing and specific isoform accumulation and shuttling between nucleus and cytosol, and acts in part via a splicing target SR34. We propose a model in which MDF controls splicing in the root meristem to promote stemness and repress stress response and cell differentiation pathways.</p>
Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data
<p>Flnc is software that can accurately identify full-length long noncoding RNAs (lncRNAs) from human RNA-seq data. lncRNAs are linear transcripts of more than 200 nucleotides that do not encode proteins. The most common approach for identifying lncRNAs from RNA-seq data which examines the coding abilities of assembled transcripts will result in a very high false-positive rate (30%-75%) of lncRNA identification. The falsely discovered lncRNAs lack transcriptional start sites and most of them are RNA fragments or result from transcriptional noise. Unlike the false-positive lncRNAs, true lncRNAs are full-length lncRNA transcripts that include transcriptional start sites (TSSs). To exclude these false lncRNAs, H3K4me3 chromatin immunoprecipitation sequencing (ChIP-seq) data had been used to examine transcriptional start sites of putative lncRNAs, which are transcripts without coding abilities. However, because of cost, time, and the limited availability of sample materials for generating H3K4me3 ChIP-seq data, most samples (especially clinical biospecimens) may have available RNA-seq data but lack matched H3K4me3 ChIP-seq data. This Flnc method solves the problem of lacking transcriptional initiation profiles when identifying lncRNAs.</p> <p>Flnc integrates seven machine-learning algorithms built with four genomic features. Flnc achieves state-of-the-art prediction power with a AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the common approach to over 85% on five independent datasets without requiring matched H3K4me3 ChIP-seq data. In addition to the stranded polyA-selected RNA-seq data, Flnc can also be applied to identify lncRNAs from stranded RNA-seq data of ribosomal RNA depleted samples or unstranded RNA-seq data of polyA-selected samples.</p>
TCGA RNA-Seq normalized rsem data, TCGA clinical data and mutational signature profiles
<p>Normalized rsem RNA-Seq data for each of the 33 TCGA tumor types named as "<em>tumortype</em>rnaSeq.tar", a file containing TCGA clinical data ("cliDat_tcga_18.tar") and a text file with single base substitution mutational signatures for each sample from COSMIC (https://cancer.sanger.ac.uk/cosmic, “signatureProfileSample.txt.zip”).</p>
RNA-seq data showing PLS role in gene expression control in Arabidopsis
<p class="MsoNormal">The vital plant ethylene receptor (ETR1) contains an essential Cu(I) ion, and as for other vesicular cupro-proteins, the final step of Cu(I) maturation at the endoplasmic reticulum (ER) is undefined. We previously discovered that mutants in the <em>Arabidopsis</em>gene <em>POLARIS</em> (<em>PLS</em>), encoding a 36 amino acid peptide, exhibit constitutive ethylene signalling responses. Here we report a 1:2 thiol-dependent Cu(I):PLS<sub>2</sub> complex, with an affinity of 3.79 (±1.5) x10<sup>19</sup> M<sup>-2</sup>. We demonstrate interactions with the transmembrane domain of ETR1, the Cu(I) chaperones ATX1 and CCH, and Cu(I)-transporting P<sub>1B</sub>-type ATPase RAN1 at the ER. Formation of Cu(I)-dependent PLS-cuproprotein adducts at the ER provides a mechanism to modulate the metalation of ETR1, thereby regulating its activity and representing a distinct mechanism for plant hormone receptor regulation.</p>
Data for: RNA-Seq dataset of RBL-2H3 (RPKM)
<p><span>The coordination between actin and microtubule network is crucial, yet our understanding of the underlying mechanisms remains limited. In this study, </span><span>we used travelling waves in the cell cortex to characterize the collective dynamics of cytoskeletal networks. Our findings show that Cdc42 and </span><span>F-BAR</span><span>-dependent actin waves in mast cells are mainly driven by formin-mediated actin polymerization, with the microtubule-binding formin FH2 domain-containing protein 1 (FHDC1) as an early regulator. </span><span>FHDC1, as well as its interaction with microtubule is required for actin waves. In addition, </span><span>the phase of microtubule depolymerization coincides with the nucleation of actin waves and microtubule stabilization inhibit actin waves, leading us to propose that microtubule shrinking and </span><span>the concurrent release of FHDC1 locally regulate actin nucleation. Lastly, we show that FHDC1 is crucial for multiple cellular processes such as cell division and migration. </span><span>Our data provided molecular insights into the nucleation mechanisms of actin waves and uncover an antagonistic interplay between microtubule and actin polymerization in their collective dynamics.</span></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.