Find research datasets worth reusing

opencc-by-4.0Oct 2020View details →

Expression-based polygenic score from the amygdala 5HTT gene network

This pipeline intends to facilitate the calculation of biologically informed polygenic scores from collected genomic data. This template can be adapted to create other expression-based polygenic risk scores. Data is 1) step by step description and 2) a list of genes that compose the gene network.

opencc-by-4.0Feb 2022View details →

Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay

The dataset supplements the publication `Optimization of the TeraTox assay for preclinical teratogenicity assessment`.  <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” Toxicological Sciences 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.

opencc-by-nc-sa-4.0Feb 2019View details →

Lab513/Landscape of Gene Expression Dataset

Datasets from the article: A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies. by Zoran S Marinkovic1,2,3, Clément Vulin1,4,5, Mislav Acman1,3, Xiaohu Song2, Jean Marc Di Meglio1, Ariel B. Lindner*,2,3, Pascal Hersen*,1 eLife 2019;8:e47951 DOI: <a href="https://doi.org/10.7554/eLife.47951">10.7554/eLife.47951</a> first versions on BioRxiv : <a href="https://www.biorxiv.org/content/10.1101/527846v2">https://www.biorxiv.org/content/10.1101/527846v2</a>

opencc-by-sa-4.0Jul 2024View details →

Light-regulated gene expression and alternative splicing data from rice seedlings.

This data contains analyzed data from the experiment conducted on rice seedlings under dark and light conditions. Seeds of rice (Oryza sativa spp. japonica cv. Nipponbare) were sown in the dark and germinated on day 2 and continued to grow in the dark for another 6 days. 3 biological replicates of the dark-grown etiolated shoots were harvested on day 8 after sowing. The remaining dark-grown seedlings were exposed to continuous white light at 120 mol/m2/sec for 48 hours or another 2 days (Days 9 and 10 after sowing). Three replicates of the light-treated green-colored seedling samples were harvested at the end of day 10. Harvested samples were frozen in liquid nitrogen and stored at -80C until further processing.

opencc-by-4.0Sep 2020View details →

Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention

Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention

opencc-by-4.0May 2022View details →

Gene expression ATLAS of Arabidopsis thaliana (accession Columbia) across its lifecycle

Abstract: Arabidopsis thaliana (accession- Columbia) is an important model plant. RNA-Seq based study of 36 gene expression libraries was carried out to explore transcriptional programs operating in different plant parts (seedling, rosette, root, inflorescence, flower, fruit silique, and seed) and developmental stages (2-leaf stage, 6-leaf stage, 12-leaf stage, senescence stage, dry mature and imbibed seed stage). For each tissue type and developmental stage, three individual plants were used as biological replicates. <div>Organism part: inflorescence, whole plant, seed, root, silique fruit, flower, rosette</div> <div> </div> <div>Developmental stage: LP.02 two leaves visible stage, IL.00 inflorescence just visible stage, fruit size 30 to 50% stage, LP.12 twelve leaves visible stage, root development stage, fruit size 70% to final stage, LP.06 six leaves visible stage, dry seed stage, flowering stage, seed imbibition stage, sporophyte senescent stage, inflorescence development stage</div> <div> </div> <div> <div>Organism: Arabidopsis thaliana</div> <div> </div> <div>Ecotype: Col-0</div> <div> </div> <div>Genotype: wild type genotype</div> <div> </div> <div>Age: Samples are from 20-day, 49-day, 39-day, 15-day, 21-day, 9-day, 22-day, 55-day, 26-day, 45-day</div> <div> </div> <div>Experimental Designs: growth chamber study<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://purl.obolibrary.org/obo/EO_0007269" target="_blank" rel="noopener"> EFO</a>, development or differentiation design<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0001746" target="_blank" rel="noopener"> EFO</a>, organism part comparison design<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0001750" target="_blank" rel="noopener"> EFO</a></div> <div> </div> <div>For more description of the data and sample types see the file <a href="../api/records/11133989/draft/files/PRJEB24664_Sample_descriptors.xlsx/content" target="_blank" rel="noopener noreferrer">PRJEB24664_Sample_descriptors.xlsx or visit </a>  or visit <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422/sdrf">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422/sdrf</a></div> <div> </div> <div>Original data was submitted from </div> <div> <ul> <li>EMBL-EBI ArraExpress: <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422</a></li> <li>NCBI SRA: <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24664">https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24664</a></li> </ul> Protocol description: <table> <tbody><tr> <th>Name</th> <th>Type</th> <th>Description</th> <th>Hardware</th> </tr> </tbody><tbody> <tr> <td>P-MTAB-71349</td> <td>growth protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0003789" target="_blank" rel="noopener"> EFO</a></td> <td>Seeds were planted in pots containing commercial potting mix with fertilizers. Pots were covered with clear perforated plastic wrap and kept at 4 degrees celsius for 3 days to break the dormancy. After 3 days plants were transferred to the Intellus Ultra growth chamber (Percival Scientific, IA, USA) which was set to temperature 22-23 degrees celsius, light intensity 120-150 micromol/m2sec under the cycle of 16h light and 8h dark. Soil was kept moist by gently spraying with water every 72 hours to maintain humidity to 50-60%. Sampling time point is given in days after germination.</td> <td> </td> </tr> <tr> <td>P-MTAB-71350</td> <td>nucleic acid extraction protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0002944" target="_blank" rel="noopener"> EFO</a></td> <td>Total RNA from frozen samples was extracted as a method described in Filichkin et al., 2010. Total RNA was used to isolate large RNA as per manufacturer's protocol for miRNeasy Mini kits (Qiagen Inc., USA), and RNase-free DNase (Life Technologies Inc., USA).</td> <td> </td> </tr> <tr> <td>P-MTAB-71351</td> <td>nucleic acid library construction protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0004184" target="_blank" rel="noopener"> EFO</a></td> <td>True-Seq kit (Illumina Inc.) was used to prepare RNA-seq libraries, according to the manufacturer’s protocol.</td> <td> </td> </tr> <tr> <td>P-MTAB-71352</td> <td>nucleic acid sequencing protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0004170" target="_blank" rel="noopener"> EFO</a></td> <td>101bp paired-end sequencing of mRNA was performed by using the standard protocols on Illumina HiSeq 3000.</td> <td>Illumina HiSeq 3000</td> </tr> </tbody> </table> </div> </div>

opencc-by-4.0Jul 2021View details →

Arabidopsis thaliana circadian mRNA-seq gene expression processed tables from Romanowski et al., TPJ 2020.

This dataset is an add-on for Romanowski et al., TPJ 2020 (https://doi.org/10.1111/tpj.14776) containing processed files for the circadian RNAseq data in tab delimited txt format. Here, you can the raw counts file, the normalized CPM values, and the full JTK result (without recalculated circadian phases, just the original ones). All genes with a read density > 0.05 in at least one timepoint were considered expressed. The read density is calculated as the amount of reads divided by the effective length of a gene (total reads / length). Genes rd file is also included. Some useful notes: 1) Counts were assigned using ASpli and the AtRTDv2 annotation (34,212 genes). 2) After filtering by rd we had a total of 18,503 expressed genes. 3) 13,256 genes passed the QL F-tests. 4) 9,127 genes were rhythmic according to JTK_cycle.  For detailed protocols, please see Romanowski et al., TPJ 2020 (https://doi.org/10.1111/tpj.14776) The RNA-seq raw data supporting the conclusions of this article have been deposited in ArrayExpress (Kolesnikov et al., 2015) at EMBL-EBI (www.ebi.ac.uk/arrayexpress), under accession numbers E-MTAB-7933. All relevant custom r scripts are available at https://github.com/aromanowski/Circadian_rhythms_and_alternative_splicing

opencc-by-4.0Mar 2023View details →

Supplemental data files: Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans

This dataset holds all non-GEO-hosted supplemental data files for manuscript "Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans". Please see the linked preprint/publication for full details. The PDF _guide_to_datafiles.pdf gives details on the format and content of each of the included files.

TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about HNSC, a type of cancer that originates in the squamous cells lining the mucosal surfaces of the head and neck region, including the oral cavity, throat, and larynx.  The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

TCGA Glioblastoma Multiforme (GBM) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about GBM, an aggressive and highly malignant brain tumor that arises from glial cells, characterized by rapid growth and infiltrative behavior. The gene expression profile was measured experimentally using the Affymetrix HT Human Genome U133a microarray platform by the Broad Institute of MIT and Harvard University cancer genomic characterization center.  The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

TCGA Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about CESC, a type of cancer that affects the cells lining the cervix and can have squamous cell or adenocarcinoma histological subtypes. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

CGGA mRNAseq_325 Gene Expression

Abstract: The Chinese Glioma Datasets (CGGA) are comprehensive and valuable collections of data related to glioma, a type of brain tumor, originating from Chinese patients. The CGGA is a data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples. This dataset's gene expression profile was measured experimentally using Agilent-014850 Whole Human Genome Microarray. The Sample IDs serve as unique identifiers for each sample.  Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: z-normalization was performed on all the samples  Acknowledgments: Zhao, Z., Zhang, KN., Wang, QW., et al. Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients (2021). Genomics, Proteomics & Bioinformatics 19(1):1-12. Fang, S., Liang, J., Qian, T., et al. (2017). Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area. AMERICAN JOURNAL OF NEURORADIOLOGY. 38(10): 1990-1997. Wang, Y., Wang, Y., Fan, X., et al. (2017). Putamen involvement and survival outcomes in patients with insular low-grade gliomas. JOURNAL OF NEUROSURGERY. 126(6): 1788-1794. U-BRITE last update: 07/27/2023

opencc-by-4.0Jan 2020View details →

Predicting gene expression using morphological cell responses to nanotopography

This dataset contains the raw files, results files and R workspace files (.RData) associated with the paper: Predicting gene expression using morphological cell responses to nanotopography Please note that this dataset is separated according to the Figure presented in the published and peer-reviewed version of the manuscript. Particular folders contain its own README file to facilitate reproduction/replication of results and figures.

opencc-by-4.0Nov 2020View details →

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

Abstract for overall study: 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. Specific description for this dataset: 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

opencc-by-4.0Jun 2017View details →

Gene co-ordinates, expression levels; SNP identifiers and functions for Drosophila melanogaster (Sussex LHM population)

Data for SNP context information to add to GWAS results. Specifically, SNP functions, sex-bias in gene expression, official SNP idenfiers from NCBI dbSNP, and gene positions and names (from UCSC Genome Browswer). Most of the input files are on-line and their URLs are stated in the code (make_dmel_accessory_data.sh). Also includes code, logs, and exploratory graphs.

opencc-by-4.0Feb 2024View details →

Regulation of Dye-decolorizing Peroxidases Gene Expression in Pleurotus ostreatus Grown on Glycerol as the Carbon Source

This dataset contains the raw data and code necessary to reproduce the results of: Regulation of dye peroxidas gene expression in Pleurotus ostreatus grown on glycerol as the carbon source.   These data are also available at github: <a href="https://github.com/JLuisCuamatzi/Pleurotus_ostreatus_CarbonSources">JLuisCuamatzi/Pleurotus_ostreatus_CarbonSources: Data and scripts to reproduce the analysis performed at Regulation of dye peroxidas gene expression in Pleurotus ostreatus grown on glycerol as the carbon source (github.com)</a>

opencc-by-4.0Mar 2024View details →

Gene expression count matrix for 4 T cell subtypes from ROSMAP participants

Peripheral blood mononuclear cells (PBMCs) from participants in the Rush Religious Orders Study/Memory and Aging Project (ROSMAP) were isolated by Ficoll gradient centrifugation, then sorted by high-speed flow cytometry into the following T cell subtypes:  CD4+CD45RO-, CD4+CD45RO+, CD8+CD45RO-, and CD8+CD45RO+.  Total RNA was extracted using buffer TCL (Qiagen), then RNA-seq libraries were prepared according to the Single Cell RNA Barcoding and Sequencing method originally developed for single-cell RNA-seq, adapted for extracted total RNA.  RNA libraries were collected on a single 384-well plate and sequenced on the Illumina HiSeq using the High-throughput 3’ Digital Gene Expression (DGE) library.  The "RNA count matrix" file is the raw counts from the 384-well plate, while the "ROSMAP_Tcell_DGE_PlateMap" file contains metadata for the wells on the plate, by well position.