Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
25,372
datasets available to search
ShareScore release 0.7.1
Dataset results
25,372 results for “Transcriptomics”
Transcriptomic response of human cells to SARS-CoV-2, RSV and H1N1 (STAR + StringTie)
<p>These data represent results from:</p> <ol> <li>Processing reads from 20 experiments (part of GSE147507) by following a standard approach, which includes using STAR to align the reads to GRCh38 and StringTie to calculate the (raw) counts per experiment. These results depict the transcriptomic response of human cells to SARS-CoV-2, RSV and H1N1, and enrichment analyses based on genes differentially expressed in SARS-CoV-2 but not in RSV or H1N1. (Authors: V.A.-P., M.G.F. and A.G.)</li> <li>Aligning to SARS-CoV-2 and quantifying reads by using HISAT2 and StringTie. (Author: C.R.-A.)</li> </ol> <p>Disclaimer: These results were obtained during the virtual BioHackathon 2020. As such, they are subject to ongoing research and have thus NOT yet undergone any scientific peer-review. That is, none of the contents can be considered to be free of errors and must be taken with caution!</p>
Data for "Profiling the transcriptomic age of single-cells in humans"
<p>This is a supplementary data for the article titled "Profiling transcriptomic age of human single-cells". Data created in this project is shared here for the scientific community. </p> <p>Here we used available scRNA-seq data of 1,058,909 blood cells of 508 healthy, human donors, for developing cell-type-specific single-cell transcriptomic clocks and predicting the age of human blood cells. We also applied our clocks to different external datasets and evaluated the age of single cells originated from COVID-19 patients and human embryos.</p> <p>For the description of the content of the dataset see the ReadMe file.</p>
DATASET: De novo assembly and functional annotation of the heart + hemolymph transcriptome in the Caribbean spiny lobster Panulirus argus
<p>The spiny lobster <em>Panulirus argus</em> is an ecologically relevant species in shallow water coral reefs and target of the most lucrative fishery in the greater Caribbean region. This study reports, for the first time, the heart + hemolymph transcriptome of the Caribbean spiny lobster<em> Panulirus argus</em> assembled from short Illumina 150 bp PE raw reads. A total 80,152,094 raw reads were assembled using the Oyster River Protocol pipeline that aspires to become the standard protocol for <em>de novo</em> transcriptome assembly. The assembly resulted in a total of 254,773 transcripts. Functional gene annotation was conducted using the software package 'dammit' that also aspires to become the standard protocol for <em>de novo</em> transcriptome annotation. Lastly, gene enrichment analyses were conducted using the Gene Ontology (GO), KEGG pathway analyses (Kaas), and KOG (WebMGA) databases. This resource will be of utmost importance in future research aiming at exploring the effect of local and regional anthropogenic disturbances as well as global climate change on the molecular physiology of this overexploited species.</p>
Deep splicing plasticity of the human adenovirus type 5 transcriptome as a driver of virus evolution nanopore data 48hpi
<p>Adenovirus infected MRC5 cells direct RNA sequencing of the mRNA using nanopore. From the paper Deep splicing plasticity of the human adenovirus type 5 transcriptome as a driver of virus evolution. Both the uncorrected fastq files and the lordec corrected files together with the normalised illumina data used to correct the nanpore data are here.</p>
The evolution of genomic, transcriptomic, and single-cell protein markers of metastatic upper tract urothelial carcinoma
<p>The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are unknown. The genomic and transcriptomic differences between primary and metastatic UTUC is not well described either. We combined whole-exome sequencing, RNA-sequencing, and Imaging Mass Cytometry<sup>TM</sup> (IMC<sup>TM</sup>) of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. IMC enables spatially resolved single-cell analyses to examine the evolution of cancer cell, immune cell, and stromal cell markers using mass cytometry with lanthanide metal-conjugated antibodies. We discovered that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature were stable across primary and matched metastatic UTUC. Molecular and immune subtypes were consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single-cells. Molecular subtyping at the single cell level was highly conserved between primary and metastatic UTUC tumors within the same patient.</p>
CAbiNet: Joint clustering and visualization of cells and genes for single-cell transcriptomics
<p>We here provide the data sets to reproduce the results in our manuscript "CAbiNet: Joint clustering and visualization of cells and genes for single-cell transcriptomics". Our package "CAbiNet" can be downloaded from https://github.com/VingronLab/CAbiNet. The scripts to reproduce the results in our manuscript can be found from https://github.com/VingronLab/CAbiNet_paper.</p><p>You can find the description of folders in 'Data.zip' in the README.md file.</p>
Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice: dataset bone marrow HbAA mice injected or not with heme
<p>The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease. Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease. All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection.</p> <p>This dataset contains the results of the HbAA mice with and without heme.</p> <p>The corresponding HbSS mice with and without heme are deposited under number 10.5281/zenodo.10962782</p> <p>Bone marrow RNA was extracted by Macherey Nagel kit, according to the manufacturer’s instructions. The quality and quantity of mRNA were evaluated using a 2100<br>bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters.<br>Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5).</p> <p>The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme. </p> <p> </p>
Genome- and transcriptome-wide association summary statistics for outcome from traumatic brain injury
<p>The dataset contains summary statistics for the genome- and transcriptome-wide association studies (GWAS, TWAS) of genetic effects on outcome in traumatic brain injury (TBI). The study participants attended hospital within 24 hours of TBI, and underwent head computed tomography imaging.</p> <p><strong>Study participants</strong></p> <p>European ancestry data set contains 4710 individuals; multi-ethnic cohort 5268 individuals, including Europeans (n = 4710), Africans (n = 245) and Admixed Americans (n = 313).</p> <p>The largest European population contribution was from CENTER-TBI (Collaborative European NeuroTrauma Effectiveness Research, https://www.center-tbi.eu), where each participating center (60 centers from 20 countries in Europe) recruited patients between December 2013 and December 2017. The patients recruited in CENTER-TBI were supplemented by subjects from cohorts recruited at two European centres (Cambridge, UK, and Turku, Finland).</p> <p>The majority of patients in the US cohort were recruited between 2014 and 2018 to TRACK-TBI (Transforming Research and Clinical Knowledge in TBI, https://tracktbi.ucsf.edu) by the 18 US participant sites. The subjects recruited to the US cohort from TRACK-TBI were supplemented by patients recruited to an institutional research initiative at Mass General Brigham (MGB).</p> <p><strong>Outcome definition</strong></p> <p>Outcomes were measured using the extended Glasgow Outcome Scale (GOSE), ranging from 1 (dead) to 8 (upper good recovery), measured 6 months post-TBI. TBI severity was specified using the Glasgow Coma Score (GCS), with TBI classified as mild (GCS 13-15), moderate (GCS 9-12), or severe (GCS 3-8).</p> <p>To account for the effect of injury severity on outcome, sliding dichotomization was used to categorize outcome as favourable or unfavourable. A GOSE ≤ 4 was used to define an unfavourable outcome for patients with either moderate (GCS 9-12) or severe (GCS 3-8) TBI, while the unfavourable group was extended to patients with GOSE ≤ 7 if they had mild (GCS 13-15) TBI.</p> <p><strong>Genotype data and imputation</strong></p> <p>Genotyping was completed at FIMM Technology Center for CENTER-TBI, Cambridge, Turku patients and the Broad Institute for TRACK-TBI, using the Illumina Global Screening Array (GSA-24v2-0 + Multi-Disease). The MGB cohort were genotyped using Illumina’s Multi-Ethnic Global array (MEGA) and the pre-releases forms, including MEGA and MEGA-Ex arrays at Illumina at the MGB Translational Genomics Core.</p> <p>A unified quality control procedure was applied for each study cohort and the array-based genotypes were imputed using the Haplotype Reference Consortium panel. Autosomal chromosomes were considered, post-imputation data was filtered by imputation quality (INFO > 0.4 for CENTER-TBI, Cambridge and Turku; R2 > 0.4 for TRACK-TBI and MGB) and MAF > 1%.</p> <p><strong>Genome-wide association analysis and meta-analysis</strong></p> <p>Genome-wide single-marker scans were performed using a penalized likelihood-based Firth logistic regression, and implemented in PLINK v2.0. Using favourable outcome as reference, models were fitted on the basis of imputed allelic dosages. Age, sex, major extracranial injury, pupillary reactivity, and the first 10 principal components were included as covariates. Study cohort (CENTER-TBI, Cambridge, Turku) was an additional covariate in the CENTER-TBI GWAS.</p> <p>Fixed-effects meta-analysis of the three European ancestry GWAS was performed using METAL. For trans-ethnic meta-analysis, summary statistics of five GWASs in patients of European, African and Admixed Americans were aggregated via MR-MEGA.</p> <p><strong>Transcriptome-wide association study</strong></p> <p>Genetically regulated gene expression (GREx) was imputed using a regression model fitted on a separate gene expression database. Elastic net models provided by PrediXcan for all available GTEx brain tissues and whole blood were used. For TWAS, the same sliding dichotomy model for outcome with the same set of covariates as in the GWAS, but PCA components were replaced with the top five principal components of the respective gene expression data. </p> <p><strong>Column headers - GWAS</strong></p> <p>rsID: variant rsID<br> Chrom: chromosome<br> Pos: position (build GRCh38)<br> A1: effect allele<br> A2: reference allele<br> EAF: allele frequency of effect allele<br> Effect: effect size of effect allele<br> StdErr: standard error of effect size<br> P: p value of association (with genomic correction)<br> N: sample size</p> <p>Note. 'Effect' and 'StdErr' are only available for the European ancestry meta-analysis.</p> <p><br> <strong>Column headers - TWAS</strong></p> <p>tissue: GTEx tissue type<br> id: ensembl gene id<br> coef: model coefficient<br> se: model standard error for coefficient<br> p: model-based p value<br> symbol: gene symbol<br> name: gene name written out<br> chr: chromosome<br> start: gene start position (build GRCh38)</p>
Ramonda serbica de novo transcriptome database
<p>Ramonda serbica de novo transcriptome database translated into amino acid seq. Hydrated and desiccated leaf tissue</p>
Spatial characterization of the motor and non-motor somal and axonal transcriptome in adult healthy and mutant FUS mice
<table> <tbody> <tr> <td> <p>Here we investigated the transcriptome of motor and non-motor axons and cell bodies in the context of mutant FUS-related amyotrophic lateral sclerosis (ALS). We applied Nanostring GeoMX Digital Spatial Profiler platform to profile the transcriptome of subcellular compartments in the lower motor circuitry of a mouse model ricapitulating ALS motor symptoms. This work sheds light for the first time on the transcriptomic alterations in axons and in somas which may contribute to axonal degeneration and neuromuscular junction denervation, early features of ALS.</p> </td> </tr> </tbody> </table>
Data From: The Oyster River Protocol: A multi assembler and kmer approach for de novo transcriptome assembly.
<p>Characterizing transcriptomes in non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a <em>de novo</em> transcriptome assembly, which is a technically complicated process involving several discrete steps. The Oyster River Protocol (ORP), described here, implements a standardized and benchmarked set of bioinformatic processes, resulting in an assembly with enhanced qualities over other standard assembly methods. Specifically, ORP produced assemblies have higher Detonate and TransRate scores and mapping rates, which is largely a product of the fact that it leverages a multi-assembler and kmer assembly process, thereby bypassing the shortcomings of any one approach. These improvements are important, as previously unassembled transcripts are included in ORP assemblies, resulting in a significant enhancement of the power of downstream analysis. Further, as part of this study, I show that assembly quality is unrelated with the number of reads generated, above 30 million reads. Code Availability: The version controlled open-source code is available at <a href="https://github.com/macmanes-lab/Oyster_River_Protocol">https://github.com/macmanes-lab/Oyster_River_Protocol</a>. Instructions for software installation and use, and other details are available at <a href="http://oyster-river-protocol.rtfd.org/">http://oyster-river-protocol.rtfd.org/</a>.</p>
Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants
<p>This dataset consists of the reference data files, metadata and processed results files for the paper "Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants," which investigates clonality in normal human dermal fibroblast cell populations in 32 cell lines from distinct donors, using bulk whole-exome sequencing and single-cell RNA-sequencing data.</p> <p>This dataset contains everything required to reproduce the results presented in the paper from processed data and results of our data processing workflows. Our analyses can be reproduced using the <a href="https://github.com/davismcc/fibroblast-clonality">source code</a> and instructions available at our <a href="https://davismcc.github.io/fibroblast-clonality/">project website</a>.</p> <p>The <em>entire</em> analysis workflow from raw data to final results is also reproducible but is substantially more complicated and computationally intensive. It also requires large datasets to be obtained from other repositories. Specifically, single-cell RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-7167. Whole-exome sequencing data is available through the HipSci portal (www.hipsci.org). Combined with the dataset in this repository and following the instructions on the project website, it is possible to run our entire analysis pipeline.</p> <p> </p>
Comparative profiling of skeletal muscle models reveals heterogeneity of transcriptome and metabolism
<p>This dataset is a complement to the following publication: Ahmed M. Abdelmoez, Laura Sardón Puig, Jonathon AB. Smith, Brendan M. Gabriel, Mladen Savikj, Lucile Dollet, Alexander V. Chibalin, Anna Krook, Juleen R. Zierath, and Nicolas J. Pillon. <a href="https://doi.org/10.1152/ajpcell.00540.2019">Comparative profiling of skeletal muscle models reveals heterogeneity of transcriptome and metabolism. </a>Am J Physiol Cell Physiol. 2019 Dec 11.</p> <p>METHODS: Publicly available data from myotubes and skeletal muscle tissues were selected from the GEO database. Raw files were downloaded and robust multi array (RMA) normalization was performed in unison for all samples from the same platform. For each human ENSEMBL, the rat and mouse orthologs were found using the R package BioMart and the arrays were merged based on the human ENSEMBL annotation. The database was then aggregated according to the official human gene symbol. When multiple ENSEMBL were found for a single gene symbol, an average was calculated.</p>
Transcriptome, metaranscriptome, and translatome data for RIBOSS
<p>This record contains long- and short-read sequence alignment files to test <a href="https://github.com/lcscs12345/riboss">RIBOSS</a>.</p> <p>To reproduce the results in <a href="https://github.com/lcscs12345/riboss_paper/blob/master/jupyter_notebooks/styphimurium.ipynb">styphimurium.ipynb</a>, clone the <a href="https://github.com/lcscs12345/riboss_paper">RIBOSS_paper</a> repository, and create a <code>conda</code> environment according to <code>README</code>.</p> <p>Create new directories <code>mkdir -p doc/ doc/metatranscriptome doc/styphimurium/ doc/styphimurium/rnaseq doc/styphimurium/riboseq</code>.</p> <p>Download the alignment files for <em>S. enterica</em> transcriptome and metatranscriptome (a cocktail of <em>S. enterica</em> serovar Enteritidis, <em>Escherichia coli</em> O157:H7, and <em>Listeria monocytogenes</em>).</p> <ul> <li>SRR11215003.bam and SRR11215004.bam: Nanopore long-read direct RNA-seq. Download and <code>mv SRR24781620.bam doc/metatranscriptome</code>.</li> <li>SRR11215663.bam and SRR11215664.bam: Illumina short-read RNA-seq. Download and <code>mv SRR24781620.bam doc/metatranscriptome</code>.</li> <li>SRR24781620.bam: Nanopore long-read cDNA sequencing. Download and <code>mv SRR24781620.bam doc/styphimurium/rnaseq</code>.</li> </ul> <p>Download <em>S. enterica</em> serovar Typhimurium ribosome profiling data and <code>mv ERR913094*.out.bam doc/styphimurium/riboseq</code>.</p> <ul> <li><code>ERR9130942Aligned.out.bam</code>: RNase I, 1000 U.</li> <li><code>ERR9130943Aligned.out.bam</code>: RNase I, 500 U.</li> <li><code>ERR9130946Aligned.out.bam</code>: matched RNA-seq.</li> </ul>
Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain - Datasets and Python notebooks
<p>This dataset and the associated Python notebooks and R-code are related to the publication "Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain".</p>
Plant regeneration in leaf culture of Centaurium erythraea Rafn. Part 3: de novo transcriptome assembly and validation of housekeeping genes for studies of in vitro morphogenesis
<p>Six centaury transcriptomes (embryogenic calli, globular somatic embryos, cotyledonary somatic embryos, adventitious buds, leaves and roots of <em>in vitro</em> grown plants) were sequenced and <em>de novo</em> assembled using <a href="https://github.com/trinityrnaseq/trinityrnaseq/wiki">Trinity</a> .</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/CE_Assembly.tar.gz">CE_Assembly.tar.gz</a> - Centaury referent transcriptome comprises of 160.839 Trinity transcripts grouped in 105.726 Trinity genes.</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/CE_Assembly_fpkm.tar.gz">CE_Assembly_fpkm.tar.gz</a> - fpkm normalized read counts of the assembled transcripts in the six sequenced centaury tissues.</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/nt.db_CE_assembly.tar.gz">nt.db_CE_assembly.tar.gz</a> - annotation of assembled transcripts by mapping them against NCBI nucleotide (NT) database using BLASTn . The obtained results were filtered with E-value E ≤ 10<sup>-3</sup>.</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/swissprot.db_CE_assembly.tar.gz">swissprot.db_CE_assembly.tar.gz</a> - annotation of assembled transcripts by mapping them against NCBI nucleotide (<a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/swissprot.db_CE_assembly.tar.gz">s</a>wissprot) database using BLASTx . The obtained results were filtered with E-value E ≤ 10<sup>-3</sup>.</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/pfam30.db_CE_assembly.tar.gz">pfam30.db_CE_assembly.tar.gz</a> - annotation of assembled transcripts by mapping them against Pfam30 domain database using hmmer3. The obtained results were filtered with independent E-value E ≤ 10<sup>-3</sup>.</p>
Preprocessed and Harmonised Transcriptomics Datasets for Psoriasis and Atopic Dermatitis
<p>The datasets uploaded within this record contain transcriptomics data of psoriasis and atopic dermatitis patients retrieved from the NCBI Gene Expression Omnibus and EBI ArrayExpress repositories. Overall, we retrieved 39 transcriptomics datasets, produced through both DNA microarrays and RNA-Sequencing technologies, along with relative meta-data tables. After data collection, each dataset was quality checked and preprocessed in order to obtain a harmonised source of data, ready-to-use for the research community. Beside, a thorough quality check was carried out on the retrieved meta-data, and data dictionaries were created (both for DNA microarry and RNA-Seq datasets) in order to homogenise the phenotypic data, enabling the comparability across the datasets. </p>
Transcriptome analysis of the effect of over-expressing H2A.J mutants in proliferating WI38 fibroblasts for the paper entitled: The H2A.J histone variant contributes to Interferon-Stimulated Gene expression in senescence by its weak interaction with H1 and the derepression of repeated DNA sequences
<p>Abstract for overall study:</p> <p>The histone variant H2A.J was previously shown to accumulate in senescent human fibroblasts with persistent DNA damage to promote inflammatory gene expression, but its mechanism of action was unknown. We show that H2A.J accumulation contributes to weakening the association of histone H1 to chromatin and increasing its turnover. Decreased H1 in senescence is correlated with increased expression of some repeated DNA sequences, increased expression of STAT/IRF transcription factors, and transcriptional activation of Interferon-Stimulated Genes (ISGs). The H2A.J-specific Val-11 moderates the transcriptional activity of H2A.J, and H2A.J-specific Ser-123 can be phosphorylated in response to DNA damage with potentiation of its transcriptional activity by the phospho-mimetic S123E mutation. Our work demonstrates the functional importance of H2A.J-specific residues and potential mechanisms for its function in promoting inflammatory gene expression in senescence.</p> <p>Specific description for this dataset:</p> <p>H2A.J differs from canonical H2A only by a valine at position 11 instead of alanine, and the 7 C-terminal amino acids containing a potential minimal phosphorylation site SQ for DNA-damage response kinases. To test the functional importance of these H2A.J-specific sequences, we mutated Val-11 to Ala as is found in all canonical H2A sequences, and we mutated Ser-123 to either Glu to mimic a phospho-serine residue or to Ala to prevent phosphorylation. We also substituted the C-terminus of H2A.J with the C-terminus of H2A. These mutants, WT-H2A.J and canonical H2A-type1 were ectopically expressed in proliferating fibroblasts, and their microarray transcriptomes were compared to that of proliferating and senescent fibroblasts without ectopic histone expression. Genome-wide transcriptome analysis indicated that senescent fibroblasts clustered distinctly from proliferating fibroblasts, and proliferating fibroblasts expressing the H2A.J-V11A and H2A.J-S123E mutants clustered distinctly from fibroblasts expressing the other H2A.J mutants, WT-H2A.J, and H2A. Hallmark gene set enrichment analysis of the transcriptomes of fibroblasts expressing H2A.J-V11A or H2A.J-S123E versus control proliferating fibroblasts indicated that they showed the same highly significant enrichment for the Epithelial-Mesenchyme Transition, TNF-Alpha Signaling Via NF-kB, and Inflammatory Response gene sets. Notable inflammatory genes including IL1A, IL1B, IL6, CXCL8, and CCL2 are contained in these gene sets and are often induced in senescence as part of the senescence-associated secretory phenotype. Heat maps showed that the H2A.J-V11A and H2A.J-S123E mutants were particularly apt at activating the expression of these inflammatory genes in proliferating fibroblasts</p>
Raw and processed GO term data to support running GCEA analyses using ensemble-based nulls, as described in the manuscript, 'Overcoming bias in gene category enrichment analyses of brain-wide transcriptomic data'.
<p>Data to support a toolbox for performing gene category enrichment analyses, including against ensembles of null phenotypes.</p> <p>Descriptions of how these data files can be used for this purpose are in the documentation for the toolbox, at https://github.com/benfulcher/GCEA_FalsePositives</p>
Training material for de novo transcriptome reconstruction from RNA-seq data
<p>The data provided here are part of a Galaxy tutorial that analyzes RNA-seq data from a study published by Wu et al., 2014 (DOI:10.1101/gr.164830.113). The goal of this study was to investigate "the dynamics of occupancy and the role in gene regulation of the transcription factor Tal1, a critical regulator of hematopoiesis, at multiple stages of hematopoietic differentiation." To this end, RNA-seq libraries were constructed from multiple mouse cell types including G1E - a GATA-null immortalized cell line derived from targeted disruption of GATA-1 in mouse embryonic stem cells - and megakaryocytes. This RNA-seq data was used to determine differential gene expression between G1E and megakaryocytes and later correlated with Tal1 occupancy. This dataset (GEO Accession: GSE51338) consists of biological replicate, paired-end, polyA selected RNA-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 chromosome 19 and a subset of interesting genomic loci identified by Wu et al.</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.