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.
3,076
datasets available to search
ShareScore release 0.9.0
Dataset results
3,076 results for “transcriptional profiling”
Chemical structures, Cell Painting and transcriptional profiles for compound bioactivity prediction.
<p>This is the related data, both input and produced for the paper <a href="https://doi.org/10.1101/2020.12.15.422887">"Predicting compound activity from phenotypic profiles and chemical structures"</a>.</p> <p>This data can be merged with <a href="https://github.com/CaicedoLab/2023_Moshkov_NatComm">paper's GitHub repository</a> for reproduction.</p> <p>Folders and files and are described below:</p> <pre><code>├── assay_data ├── assay_matrix_discrete_270_assays.csv Assay matrix with hits for assays (270) and compounds (16170). Note that this is the final file that we used to produce splits. ├── assay_metadata.csv Assay metadata ├── broad_ids.txt List of broad ids used in this study. That is an unfiltered list of compounds required by some analysis scripts. ├── smiles.txt Same as broad_ids.txt, but SMILES strings. ├── feature_data (for 16978 compounds, can be masked with ./misc/compounds16978to16170.npy) ├── cp.npz Classical chemical features ├── ge.npz Gene expression features ├── ge_scale.npz Gene expression scaled features ├── mo.npz Morphology features (not batch corrected) ├── mobc.npz Morphology features (batch corrected) ├── misc ├── compound_analysis.npz Compounds in the dataset identified as PAINS ├── compounds16978to16170.npy Used to filter features from the bigger set of compounds to the final one ├── fingerprints.npz Calculated fingerprints of compounds, those were then used to calculate similarity ├── similarity_fingerprints.npz Similarity matrix for compounds (16978) ├── population_normalized.csv.gz Well-level morphological profiles that were used for batch-correction ├── Table for PUMA Excel file with additional data and plots ├── predictions ├── scaffold_median(mean)_AUC.csv Aggregated median(mean) AUC scores over scaffold-based cross-validation splits. In the paper, median results were reported. ├── scaffold_median(mean)_EF.csv Aggregated median(mean) enrichment factor (EF) over scaffold-based cross-validation splits. In the paper, median results were reported. ├── toprank_chemical_cv{}_hitsnorm.csv Those files are needed to create enrichment plots and contain hit rate and top rank hit rate. ├── Each folder here stands for an experiment type, the number in the folder name is a number of the split. Inside each folder there are the following elements: ├── predictions Folder with predictions for each assay-compound pair for each modality ├── 2022_01_evaluation_all_data.csv File with AUC scores for each assay for the test set in the split ├── 2022_01_evaluation_all_data_EF.csv File with enrichment factor (EF) values for each assay for the test set in the split. Those files exist only for *chemical* folders. ├── assay_matrix_discrete_train(test)_old_scaff.csv Training and test subsets of data for the split. The first column contains broad_id. ├── assay_matrix_discrete_train(test)_old_scaff.csv Same, but SMILES strings in the first column. Those files are used as input to ChemProp! Experiments in this folder are the following: - chemical Scaffold-based 5-fold cross-validation splits, the main results in the paper are reported with this series of experiments. - chemical_bal Same splits as in chemical, but training were run with ChemProp built-in data balancing. - chemical_st Same splits as in chemical, but separate models were trained for each assay. - CV Random 5-fold cross-validation splits. - GE 5-fold cross-validation splits based on same-size clustering of gene expression features. - MOBC 5-fold cross-validation splits based on same-size clustering of batch-corrected morphology features. - random 10 random splits, ~80% of compounds in the training set and the rest in the test set. ├── splitting This folder contains numpy files which help to match compounds and features to create training and test sets for a split, which can be reused in the analysis notebook for data preparation. ├── scaffold_based_split.npz Splitting for scaffold-based splits. ├── random_split_{}.npz Random split indices of test set compounds (10 files). ├── cross_validation_indicies.npz Indices for random cross-validation splits ├── GE_clusters_size_constrained.npz Indicies of clusters of same-size clustering for gene-expression features. ├── MOBC_clusters_size_constrained.npz Indices of clusters of same-size clustering for batch-corrected morphology features.</code></pre> <p> </p>
Increased egg shell temperature during incubation leads to changes in transcriptional and epigenetic profiles in chicken lungs
<p>These RDS files contain <strong>DESeqDataSet </strong>objects subsets per broiler age and treatment. These objects are the result of DESeq2::DESeq( … ,betaPrior=FALSE).The .txt-objects contain the normalized sequencing counts per broiler age and treatment group. These objects are the result of DESeq2::counts( … , normalized=TRUE). Data was generated using STAR v2.7.10a and DESeq2 v1.36. Metadata is included as Excel file.</p> <p>Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA949139. </p> <p> </p> <p><strong>Study abstract</strong></p> <p>D. Schokker, J. de Vos, P.B. Stege, O. Madsen, H.J. Wijnen, S.K. Kar, and J.M.J. Rebel</p> <p>Health and resilience against respiratory diseases are important features for broiler chicken. In this study, epigenetic and transcriptomic changes in the lungs of broiler chickens of different ages during rearing that were either exposed to elevated egg shell temperature (HIGH) of 38.9°C during mid-incubation or normal egg shell temperature (control; CON). The objective was to better understand how environmental challenges, such as heat stress during egg incubation, affect the development of the immune system and health of broiler chicken at later age. To this end we generated both epigenetic and transcriptomic data of lung tissue of elevated HIGH and CON chicken, furthermore these chicken were challenged by introducing either an infectious E. coli or an IBV vaccination to monitor the respiratory response. Thousands of differential methylated sites were observed at days 15 and 33, when comparing HIGH vs. CON. Pathway enrichment analysis of HIGH vs. CON showed that differentially expressed genes were mainly involved in cilium, cytoskeleton, and immune processes. These findings provide insight into the underlying biological mechanisms of early life conditions, like elevated EST, and their potential role in health of broilers.</p>
Transcriptional profiling of peripheral blood mononuclear cells identifies inflammatory phenotypes in ataxia telangiectasia
<p>This is an AnnData object in h5ad (hdf5) format containing de-identified bulk RNA-seq gene expression matrices from PBMCs. These data are related to the study entitled "Transcriptional profiling of peripheral blood mononuclear cells identifies inflammatory phenotypes in ataxia telangiectasia". </p><p>This AnnData object contains a table of sample-specific metadata (`obs`), gene-specific metadata (`var`), and multiple gene expression matrices stored as `layers`. These layers include raw counts, DEseq2 normalized counts, vst normalized counts, and rlog normalized counts. Some additional layers include regressed versions of the previously mentioned counts matrices, where sequencing batch (`cohort` in the obs table) has been regressed out using the `combat` tool. The layer `rlog_combat_regressed_batch` is recommended for downstream processing, and has been loaded into the `X` slot of the anndata object for convenience. </p><p>The md5sum of this h5ad file is listed here: 04d7c6c549fb37cf730a5dca7897f86f</p><p>Opening and working with AnnData objects in h5ad format requires the use of the `anndata` python library (https://github.com/scverse/anndata). </p>
Data from: Whole blood transcriptional profiles and the pathogenesis of tuberculous meningitis
Open the record for dataset details and reuse information.
DisFull_Lnc: discover full-length long noncoding RNAs without transcriptional initiation profiles
<p>DisFull_Lnc aims to identify full-length lncRNAs directly from raw RNA-seq data without H3K4me3 profiles from the corresponding sample.</p>
Data from: Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation
<p>Macrophages play a key role in ozone-induced lung injury by regulating both the initiation and resolution of inflammation. These distinct activities are mediated by pro-inflammatory and anti-inflammatory/pro-resolution macrophages which sequentially accumulate in injured tissues. Macrophage activation is dependent, in part, on intracellular metabolism. Herein, we used RNA-sequencing (seq) to identify signaling pathways regulating macrophage immunometabolic activity following exposure of mice to ozone (0.8 ppm, 3 hr) or air control. Analysis of lung macrophages using an Agilent Seahorse showed that inhalation of ozone increased macrophage glycolytic activity and oxidative phosphorylation at 24 and 72 hr post exposure. An increase in the percentage of macrophages in the S phase of the cell cycle was observed 24 hr post ozone. RNA-seq revealed significant enrichment of pathways involved in innate immune signaling and cytokine production among differentially expressed genes at both 24 and 72 hr after ozone, while pathways involved in cell cycle regulation were upregulated at 24 hr and intracellular metabolism at 72 hr. An interaction network analysis identified tumor suppressor 53 (TP53), E2F family of transcription factors (E2Fs), Cyclin Dependent Kinase Inhibitor 1A (CDKN1a/p21), and Cyclin D1 (CCND1) as upstream regulators of cell cycle pathways at 24 hr and TP53, nuclear receptor subfamily 4 group a member 1 (NR4A1/Nur77), and estrogen receptor alpha (ESR1/ERα) as central upstream regulators of mitochondrial respiration pathways at 72 hr. These results highlight the complex interaction between cell cycle, intracellular metabolism, and macrophage activation which may be important in the initiation and resolution of inflammation following ozone exposure.</p>
Development of a pipeline for analyzing the gene expression profile of transcripts
<pre>Data corresponding to the scientific initiation project in bioinformatics. </pre> <pre>The data in the <em>parcial_report_input</em> folder are equivalent to raw counts, metadata and data of FPKM values of genes from the analysis performed during the first six months of the project. They correspond the counts and metadata from a previous study from renal cell carcinoma. As for the <em>F</em><em>inal_report_input</em>, it also contains counts and metadata, but from a previous study of ostesarcoma. The metadata and raw data files can be found under the accesion number hs000699.v1.p1 in dbGAP. The scripts wrote to perform pre-processing of samples, differential expression analysis, network analysis and functional annotation can be found in <a href="https://github.com/meidanis-lab/trans-pipeline">GitHub repository</a>. </pre>
Skin Transcriptional Profiles In Psoriatic Patients Under Adalimumab Biotherapy
ClinicalTrials.gov study NCT01872546. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation
Open the record for dataset details and reuse information.
Genome-wide investigation and transcriptional profiling of the oxidosqualene cyclase (OSC) genes in wheat (Triticum aestivum L.)
Open the record for dataset details and reuse information.
Data from: Transcriptional profiling of the murine airway response to acute ozone exposure
<p>Ambient ozone (O<sub>3</sub>) exposure has serious consequences on respiratory health, including airway inflammation and injury. Decades of research have yielded thorough descriptions of these outcomes; however, less is known about the molecular processes that drive them. The aim of this study was to further describe the cellular and molecular responses to O<sub>3</sub> exposure in murine airways, with a particular focus on transcriptional responses in two critical compartments: conducting airways (CA) and airway macrophages (AM). After exposing adult, female C57BL/6J mice to filtered air, 1 or 2 ppm O<sub>3</sub>, we assessed hallmark responses including airway inflammation (cell counts and cytokine secretion) and injury (epithelial permeability), followed by gene expression profiling of CA and AM by RNA-seq. As expected, we observed concentration-dependent increases in airway inflammation and injury. CA and AM both exhibited changes in gene expression to both 1 and 2 ppm O<sub>3</sub> that were largely compartment-specific. In CA, genes associated with epithelial barrier function, detoxification processes, and cellular proliferation were altered, while O<sub>3</sub> affected genes involved in innate immune signaling, cytokine production, and extracellular matrix remodeling in AM. Further, CA and AM also exhibited notable differences in concentration-response expression patterns for large numbers of genes. Overall, our study has described transcriptional responses to acute O<sub>3</sub> exposure, revealing both shared and unique gene expression patterns across multiple concentrations of O<sub>3</sub> and in two important O<sub>3</sub>-responsive tissues. These profiles provide broad mechanistic insight into pulmonary O<sub>3</sub> toxicity, and reveal a variety of targets for focused follow-up studies.</p> <div> </div>
Data from: Brain transcriptional profiles of male alternative reproductive tactics and females in bluegill sunfish
Bluegill sunfish (Lepomis macrochirus) are one of the classic systems for studying male alternative reproductive tactics (ARTs) in teleost fishes. In this species, there are two distinct life histories: parental and cuckolder, encompassing three reproductive tactics, parental, satellite, and sneaker. The parental life history is fixed, whereas individuals who enter the cuckolder life history transition from sneaker to satellite tactic as they grow. For this study, we used RNAseq to characterize the brain transcriptome of the three male tactics and females during spawning to identify gene ontology (GO) categories and potential candidate genes associated with each tactic. We found that sneaker males had higher levels of gene expression differentiation compared to the other two male tactics. Sneaker males also had higher expression in ionotropic glutamate receptor genes, specifically AMPA receptors, compared to other males, which may be important for increased spatial working memory while attempting to cuckold parental males at their nests. Larger differences in gene expression also occurred among male tactics than between males and females. We found significant expression differences in several candidate genes that were previously identified in other species with ARTs and suggest a previously undescribed role for cAMP-responsive element modulator (crem) in influencing parental male behaviors during spawning.
Transcriptional and epigenetic profiling of Arabidopsis thaliana exposed to low dose ionizing radiation
<p>RNA and methylation profiling of Arabidopsis seedlings to low dose ionizing radiation</p>
Transcriptional profiling of ARPE-19 cells infected with high capacity MCMV vector.
<p><span>To gain a detailed knowledge about the virus cycle of murine cytomegalovirus (MCMV) and its high capacity vector in cross-species settings, we analyzed the early and late viral and host transcriptome upon infection of human cells. ARPE-19, A549, and 911 cells (5.0×10<sup>5 </sup>cells/well) were infected in a 24-well format 4 h post-seeding with MCMV-wt or Q4-LRBAs-GLuc, a high capacity replication competent vector based on MCMV, at an MOI of 3. As control, we used infection of mouse embryonic fibroblast, which is the natural host of the wild type MCMV, treated similarly. Harvesting occurred at 8 hpi and 31 hpi through centrifugation at 1.000 g for 5 min. Cell pellets were washed with PBS, re-suspended in 350 µL of RLT buffer, and processed using the RNeasy Mini kit as per the manufacturer’s instructions in independent triplicates. At least 1.500 ng of RNA in a 30 µL volume was isolated and sent for Illumina next-generation sequencing, resulting in paired-end sequencing with a read length of 2×100 bp and a depth of 20 million reads. This submission contains the reads, we obtained analyzing the infections of ARPE-19 cells. The data sheet for the samples and the reference genomes (.gb), which we used in the analysis published in Riedl et al. Vaccines 2024 can be found in the REFERENCES_ARPE-19.zip folder. Please, find the control reads for uninfected cells in a separate upload entitled: Mock infected controls for transcriptional profiling of infections with high capacity MCMV vector (DOI 10.5281/zenodo.1250410).</span></p>
Transcriptional profiling of 911 cells infected with high capacity MCMV vector.
<p>To gain a detailed knowledge about the virus cycle of murine cytomegalovirus (MCMV) and its high capacity vector in cross-species settings, we analyzed the early and late viral and host transcriptome upon infection of human cells. ARPE-19, A549, and 911 cells (5.0×10<sup>5 </sup>cells/well) were infected in a 24-well format 4 h post-seeding with MCMV-wt or Q4-LRBAs-GLuc, a high capacity replication competent vector based on MCMV, at an MOI of 3. As control, we used infection of mouse embryonic fibroblast, which is the natural host of the wild type MCMV, treated similarly. Harvesting occurred at 8 hpi and 31 hpi through centrifugation at 1.000 g for 5 min. Cell pellets were washed with PBS, re-suspended in 350 µL of RLT buffer, and processed using the RNeasy Mini kit as per the manufacturer’s instructions in independent triplicates. At least 1.500 ng of RNA in a 30 µL volume was isolated and sent for Illumina next-generation sequencing, resulting in paired-end sequencing with a read length of 2×100 bp and a depth of 20 million reads. This submission contains the reads, we obtained analyzing the infections of 911 cells. The data sheet for the samples and the reference genomes (.gb), which we used in the analysis published in Riedl et al. Vaccines 2024 can be found in the REFERENCES_911.zip folder. Please, find the control reads for uninfected cells in a separate upload entitled: Mock infected controls for transcriptional profiling of infections with high capacity MCMV vector (DOI 10.5281/zenodo.1250410).</p>
Transcriptional profiling of the response to starvation and fattening reveals differential regulation of autophagy genes in mammals
<p>Nutrient deprivation (starvation) induced by fasting and hypercaloric regimens are stress factors that can influence cell and tissue homeostasis in mammals. One of the key cellular responses to changes in nutrient availability is the cell survival pathway, autophagy. While there has been much research into the protein networks regulating autophagy, less is known about the gene expression networks involved in this fundamental process. Here, we applied a network algorithm designed to analyze omics datasets, to identify sub-networks that are enriched for induced genes in response to starvation. This enabled us to identify two prominent active modules composed of key stress-induced transcription factors, including members of the Jun, Fos, and ATF families, and the other comprising autophagosome sub-network genes, including ULK1. The results were validated in the brain, liver, and muscle of fasting mice. Moreover, differential expression analysis of autophagy genes in the brain, liver, and muscle of high-fat diet-exposed mice, showed significant suppression of GABARAPL1 in the liver. Finally, our data provide a resource that may facilitate the future identification of regulators of autophagy.</p>
Early On-treatment Transcriptional Profiling as Predictor of Response in Early-stage HER2-positive Breast Cancer
ClinicalTrials.gov study NCT05912062. IPD Sharing: NO. Countries: 1. Publications: 1.
Transcriptional profiling of the response to starvation and fattening reveals differential regulation of autophagy genes in mammals
Open the record for dataset details and reuse information.
Data from: Transcriptional profiling of the murine airway response to acute ozone exposure
Open the record for dataset details and reuse information.
Data from: Brain transcriptional profiles of male alternative reproductive tactics and females in bluegill sunfish
Open the record for dataset details and reuse information.
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.