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54,099 results for “Expression”
TCGA Glioblastoma Multiforme (GBM) Gene Expression
<p><strong>Abstract:</strong></p> <p>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.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>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</p> <p>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</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Gene Expression
<p><strong>Abstract:</strong></p> <p>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.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>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</p> <p>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</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
CGGA mRNA-array_301 Expression Data
<p><strong>Abstract:</strong></p> <p>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. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>z-normalization was performed on all the samples </p> <p><strong>Acknowledgments:</strong></p> <p>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.</p> <p>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</p> <p>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.</p> <p> </p> <p><strong>U-BRITE last update: </strong>07/27/2023</p>
CGGA mRNAseq_325 Gene Expression
<p><strong>Abstract:</strong></p> <p>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. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>z-normalization was performed on all the samples </p> <p><strong>Acknowledgments:</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>U-BRITE last update: </strong>07/27/2023</p>
How Clarinettists Play Music to Acheive Expressive Goals
<p>Dataset used in article 'How Clarinettists play Music to Acheive Expressive Goals' submitted to New Music Research on July 2023</p>
Predicting gene expression using morphological cell responses to nanotopography
<p>This dataset contains the raw files, results files and R workspace files (.RData) associated with the paper:</p> <p>Predicting gene expression using morphological cell responses to nanotopography</p> <p>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. </p>
Identification of emotional facial expressions in a lab and over the internet
<p>This dataset includes the data used for the analyses presented in the paper published (under the same title as this dataset) in the journal Psychology, Journal of the Higher School of Economics. </p> <p>Abstract of the publication:</p> <p>Collecting data over the internet is an approach that allows researchers to vastly expand the possible sample sizes of their studies, and enables the study of populations that may otherwise be difficult to access. However, to ensure that data collected over the internet is of the same level of quality as data collected in a lab, the comparability of internet-collected data with lab-collected data must first be assessed for individual areas of research and experimental approaches. To answer the question of whether internet data collection is suitable for experiments involving facial expressions, we conducted a deliberately difficult facial emotion-identification experiment where participants completed the same task either under supervision in our lab, or at an unsupervised location over the internet. Stimuli consisted of sad faces that participants were asked to identify as resembling either anger, fear, or disgust. Regardless of belonging to either the group tested in the lab or over the internet, participants showed highly similar response distributions, while differences between the groups were non-significant and of very low magnitude. We can therefore conclude from our findings that internet data collection is a viable method for experiments requiring the identification of emotional facial expressions, being able to produce similar results to those which can be obtained in a lab.</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>
Gene co-ordinates, expression levels; SNP identifiers and functions for Drosophila melanogaster (Sussex LHM population)
<p>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.</p>
In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells
<p>Dataset of the publication "In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells" by Bissa et al. on the journal Vaccines. </p><p>Each folder contains the original files reporting the data used to generate the manuscript.</p><p>For flowcytometry based assays the Flow panel is included in the folders. </p><p>For ELISA based assays the schemes of the plates are included in the folders. </p><p>The excel table "Bissa et al._Vaccines_2023_Animal IDs and viral acquisition" reports the IDs and grouping of the animals together with their viral acquisition</p><p>The excel table "Bissa et al._Vaccines_2023_Master table" reports each data used to generate the figures and supplemental materials included in the publication </p>
Regulation of Dye-decolorizing Peroxidases Gene Expression in Pleurotus ostreatus Grown on Glycerol as the Carbon Source
<p>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.</p> <p> </p> <p>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></p>
Gene expression count matrix for 4 T cell subtypes from ROSMAP participants
<p><span>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:<span> </span>CD4+CD45RO-, CD4+CD45RO+, CD8+CD45RO-, and CD8+CD45RO+.<span> </span>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</span><span>, adapted for extracted total RNA.<span> </span>RNA libraries were collected on a single 384-well plate and sequenced on the Illumina HiSeq </span><span>using the High-throughput 3<span>’</span> Digital Gene Expression (DGE) library</span><span>.<span> 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.</span></span></p>
Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems
<p>Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems</p>
MAGE: Multi-ancestry Analysis of Gene Expression
<p>MAGE comprises RNA-seq data from lymphoblastoid cell lines derived from 731 individuals from the <a href="https://doi.org/10.1038/nature15393" rel="nofollow">1000 Genomes Project (1KGP)</a>, representing 26 globally-distributed populations across five continental groups. These data offer a large, geographically diverse, open access resource to facilitate studies of the distribution, genetic underpinnings, and evolution of variation in human transcriptomes and include data from several ancestry groups that were poorly represented in previous studies.</p> <p>Briefly, this repo contains the following data:</p> <ol> <li>Sample metadata and sequencing metrics</li> <li>Gene expression and splicing matrices used for e/sQTL mapping and analyses of global trends of expression/splicing diversity</li> <li>cis-e/sQTL mapping results, including aFC estimates for cis-eQTLs</li> <li>Functional annotations of cis-e/sQTLs</li> <li>Results of colocalization analysis between MAGE e/sQTLs and complex trait GWAS from the <a href="https://doi.org/10.1038/s41586-019-1310-4" rel="nofollow">PAGE</a> study</li> <li>Results of analyses of global trends of expression/splicing diversity</li> <li>Jointly-generated top genotype PCs for samples in MAGE and other resources with paired WGS/RNA-seq data (Geuvadis, GTEx, AFGR)</li> </ol> <p>READMEs are provided for all data in the repo.</p>
Datasets for "The Venturia inaequalis effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins "
<p>Datasets for preprint entitled "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi"</p> <p><strong>1) ViAnnotation.gff3</strong><br> Gene annotation of <em>Venturia inaequalis</em> MNH120 (<a href="https://genome.jgi.doe.gov/Venin1/Venin1.home.html">https://genome.jgi.doe.gov/Venin1/Venin1.home.html</a>) generated as part of the study "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi". </p> <p>Gene reannotation was performed to include genes that would have been missed in the previous annotation by Deng et al. (2017), especially those genes encoding putative effector proteins, which are difficult to predict. For this purpose, we used a three-step approach. In the first step, coding sequences (CDSs) from <em>V. inaequalis</em> isolate 05/172, which were predicted as part of a previous study by Passey et al. (2018) (<a href="https://journals.asm.org/doi/full/10.1128/MRA.01062-18">https://journals.asm.org/doi/full/10.1128/MRA.01062-18</a>), were downloaded from the National Center for Biotechnology Information (<a href="https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/">https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/</a>) and mapped to the MNH120 genome using GMAP v2021-02-22. In the second step, RNA-seq reads from one biological replicate representing each <em>in planta</em> time point of <em>Malus domestica</em> infection by <em>V. inaequalis </em>(12 hour post-inoculation [hpi], 24 hpi, 2 days post-inoculation [dpi], 3 dpi, 5 dpi, 7 dpi), as well as one time point representing growth of the fungus in culture, were mapped to the MNH120 genome using HISAT2 v2.2.1. Then, a genome-guided <em>de novo</em> transcriptome assembly was performed using Trinity v2.12.0 and likely CDSs were identified using Transdecoder v5.5.0 (<a href="https://github.com/TransDecoder/TransDecoder">https://github.com/TransDecoder/TransDecoder</a>) in conjunction with a minimum open frame (ORF) length of 50 amino acids. Finally, in the third step, all annotations were visualized in Geneious v9.05, together with the previous annotation from Deng et al. (2017), and a manual curation was performed to create a consensus prediction. Note: this reannotation was generated with the aim of identifying as many genes as possible, and as a result, it contains many spurious genes. </p> <p><strong>2) Protein_sequences_ViAnnotation.fasta</strong></p> <p><strong>3) ECs_Families_AlphaFold.zip</strong></p> <p>This dataset is made up of predicted protein tertiary structures representing the main member of each up-regulated <em>V. inaequalis</em> effector candidate family. Structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). In cases where the effector candidate had less than 30 proteins with amino acid sequence similarity in the NCBI database, a custom multiple sequence alignment (MSA) was generated and used as input for AlphaFold2. Here, mature protein sequences were used.</p> <p><strong>4) singletons_AlphaFold_OpenSourceCASP14.zip</strong></p> <p>This dataset set is made up of predicted protein tertiary structures representing up-regulated<em> V. inaequalis</em> singleton effector candidates. Structures were predicted using AlphaFold (<a href="https://github.com/deepmind/alphafold">https://github.com/deepmind/alphafold</a>) open source code v2.0.1 and v2.1.0, with pre-set casp14, max_template_date: 2020-05-14. Mature protein sequences were used as input. </p> <p><strong>5) ECs_Avrs_phytopathogens_AlphaFold.zip</strong></p> <p>Predicted tertiary structures of avirulence (Avr) proteins or candidate Avr proteins from other fungal pathogens included in the "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi" study. These structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). Mature protein sequences were used as input. </p> <p>If you have any questions about the datasets, please contact us.<br> Mercedes Rocafort: <a href="mailto:m.rocafort.ferrer@massey.ac.nz">m.rocafort.ferrer@massey.ac.nz</a><br> Carl Mesarich: <a href="mailto:c.mesarich@massey.ac.nz">c.mesarich@massey.ac.nz</a></p>
COLONOMICS - predictive models for normal colon gene expression and DNA methylation for TWAS and MWAS
<p>We provide significant SNP prediction models derived from the COLONOMICS data (<a href="https://www.colonomics.org">https://www.colonomics.org</a>). Genotypes were obtained by Affymetrix 6.0 array, imputed to TopMed panel. Gene expression was obtained from Affymetrix U219 array, DNA methylation was obtained with Illuminan 450K array and miRNA expression was obtained by NGS. We provide SNP prediction models for 1,758 genes, 30,530 CpG probes and 38 miRNAs obtained from colon normal biopsy samples. These features can be predicted from SNPs located within ±1Mb, which we assumed they act through cis mechanisms. We include the model’s summary statistics and corresponding SNP weights in SQLite objects. Models were trained using the elastic net procedure employed in the PredictDB pipeline (<a href="https://predictdb.org/">https://predictdb.org</a>), according to which only models with a predictive performance p-value < 0.05 and R<sup>2</sup> > 0.1 are considered significant. We adjusted the models by basic covariates, i.e., sex, age, tissue type and colon anatomic location where biopsies were collected (left and right colon). Genome coordinates refer to GRCh37/hg19.</p>
Gene expression count data from human post-mortem spinal cord
<p>Gene expression data from human post-mortem tissue for three spinal cord sections (cervical, thoracic and lumbar) from amyotrophic lateral sclerosis (ALS) patients and non-neurological disease controls. RNA sequencing performed as part of the New York Genome Center ALS Consortium.</p> <p>Analysis workbooks: <a href="https://jackhump.github.io/ALS_SpinalCord_QTLs/">https://jackhump.github.io/ALS_SpinalCord_QTLs/</a> </p> <p>Preprint describing results: <a href="https://www.medrxiv.org/content/10.1101/2021.08.31.21262682v1">https://www.medrxiv.org/content/10.1101/2021.08.31.21262682v1</a> </p> <p><strong>Sample sizes:</strong></p> <table align="left"> <tbody> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p><strong>Control</strong></p> </td> <td> <p><strong>ALS</strong></p> </td> </tr> <tr> <td> <p>Cervical</p> </td> <td> <p>35</p> </td> <td> <p>139</p> </td> </tr> <tr> <td> <p>Thoracic </p> </td> <td> <p>10</p> </td> <td> <p>42</p> </td> </tr> <tr> <td> <p>Lumbar</p> </td> <td> <p>32</p> </td> <td> <p>122</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><strong>Library preparation</strong></p> <p>RNA was extracted from flash-frozen postmortem tissue using TRIzol (Thermo Fisher Scientific) chloroform, followed by column purification (RNeasy Minikit, QIAGEN). RNA integrity number (RIN) was assessed on a Bioanalyzer (Agilent Technologies). RNA-Seq libraries were prepared from 500ng total RNA using the KAPA Stranded RNA-Seq Kit with RiboErase (KAPA Biosystems) for rRNA depletion and Illumina-compatible indexes (NEXTflex RNA-Seq Barcodes, NOVA-512915, PerkinElmer, and IDT for Illumina TruSeq UD Indexes, 20022370). Pooled libraries (average insert size: 375 bp) passing the quality criteria were sequenced either on an Illumina HiSeq 2500 (125 bp paired end) or an Illumina NovaSeq (100 bp paired-end). The samples had a median sequencing depth of 42 million read pairs, with a range between 16 and 167 million read pairs.</p> <p><strong>Data processing</strong></p> <p>Samples were uniformly processed using RAPiD-nf, an efficient RNA-Seq processing pipeline implemented in the NextFlow framework. Following adapter trimming with Trimmomatic (version 0.36), all samples were aligned to the hg38 build (GRCh38.primary_assembly) of the human reference genome using STAR (2.7.2a), with indexes created from GENCODE, version 30. Gene expression was quantified using RSEM (1.3.1) using GENCODE v30. Quality control was performed using SAMtools and Picard, and the results were collated using MultiQC. Various technical metrics for sequencing quality control are provided in the metadata. Estimated read counts and normalised transcripts per million (TPM) matrices provided for each tissue.</p> <p><strong>Provided data:</strong></p> <p><em>gencode.v30.gene_meta.tsv.gz</em> - tab separated table with columns "genename", the HGNC gene symbol, and "geneid" the Ensembl ID, as set in the GENCODE v30 comprehensive annotation.</p> <p>For {tissue} in Cervical_Spinal_Cord, Thoracic_Spinal_Cord, Lumbar_Spinal_Cord:</p> <p><em>{tissue}_metadata.tsv.gz </em>- metadata describing each sample. Each row describes a sample. Descriptions of each column below.</p> <p><em>{tissue}_gene_tpm.tsv.gz</em> - the normalised TPM values from RSEM for all 58,884 genes in GENCODE v30. Each row describes a gene and each column describes a sample.</p> <p><em>{tissue}_gene_counts.tsv.gz</em> - the estimated read counts from RSEM for all 58,884 genes in GENCODE v30. Each row describes a gene and each column describes a sample.</p> <p><strong>Metadata Column Description</strong></p> <p><em>rna_id</em> - de-identified sample ID for each unique RNA-seq sample</p> <p><em>dna_id</em> - de-identified donor ID for each patient enrolled in the study</p> <p><em>site_id</em> - de-identified site name for each contributing site</p> <p><em>tissue</em> - name of tissue/region</p> <p><em>age_rounded</em> - age at death, rounded to nearest decade</p> <p><em>sex</em> - biological sex of donor</p> <p><em>subject_group</em> - long form disease group</p> <p><em>disease</em> - short form disease group</p> <p><em>site_of_motor_onset </em>- for ALS donors, where did symptoms start?</p> <p><em>disease_duration</em> - for ALS donors, how long did donor live with disease? </p> <p><em>mutations </em>- any known ALS gene mutations</p> <p><em>library_prep</em> - type of library preparation method used</p> <p><em>seq_platform </em>- sequencing platform used for sequencing</p> <p><em>rin</em> - RNA integrity number, 0-10</p> <p><em>c9orf72_repeat_size</em> - estimated C9orf72 repeat expansion size</p> <p><em>gPC1 - gPC5 </em>- principal component of genetic ancestry from whole genome sequencing</p> <p>Remaining metadata columns are from Picard - see here: <a href="http://broadinstitute.github.io/picard/picard-metric-definitions.html#RnaSeqMetrics">http://broadinstitute.github.io/picard/picard-metric-definitions.html#RnaSeqMetrics</a> </p> <p> </p>
Mars Express thermal power dataset
<p>This dataset contains several features related to the power resources and general situation of <a href="https://www.esa.int/Science_Exploration/Space_Science/Mars_Express">ESA's Mars Express</a> spacecraft. Its purpose is the design of predictive models related to thermal power consumption. Having an efficient planning on the allocation of power is critical, as it allows to maximize the scientific gain and extend the life-time of the overall mission.</p> <p>The data has been split into two sets:</p> <ul> <li>MEX1 containing 5795 orbits from 01.01.2015 to 18.08.2019</li> <li>MEX2 containing 1507 orbits from 08.08.2019 to 31.10.2020</li> </ul> <p>MEX1 can be seen as a training set. It was used to fit a reference model (THP 2.0 - Thermal Power Model) which is available in the <strong>thp2 </strong>column. MEX2 can be seen as a test set, providing previously unseen orbits.</p> <p>All the features are aggregated for each full orbit of the spacecraft. They include:</p> <ul> <li><strong>timestamp</strong>: time [unix timestamp]</li> <li><strong>sme</strong>: the value of Sun-Mars-Earth angle [deg]</li> <li><strong>s_m_distance</strong>: Sun-Mars distance in [km]</li> <li><strong>orbital_p</strong>: length of orbital period [s]</li> <li><strong>average_power</strong>: measured average power consumption [W]</li> <li><strong>right_flag</strong>: Guidance flag, North/South flag, determines whether LVA is exposed to sun, 1 = yes, 0 = no </li> <li><strong>eclipse_l</strong>: length of the eclipse in orbit [s]</li> <li><strong>full_off</strong>: whether full off or drive off, full off = 1, only drive off = 0</li> <li><strong>average_xtx</strong>: average current drawn by transmitter component [A] (multiply by 28V to get W)</li> <li><strong>gsep_dur</strong>: time spent in GSEP [s]</li> <li><strong>lvah</strong>: Heating of the LVA (launch vehicle adapter) [combined feature]</li> <li><strong>sh</strong>: Solar heating [combined feature]</li> <li><strong>thp2</strong>: output of the reference model THP2 model [W]</li> </ul> <p><strong>lvah </strong>is a heating related to the orientation of the spacecraft and is computed by </p> <p><span class="math-tex">\(LVAH = SME \cdot \text{right_flag} \)</span></p> <p><strong>sh</strong> is another aggregated feature representing the total solar power influx. It is computed by:</p> <p><span class="math-tex">\(SH = \left( 1 - \frac{\text{eclipse_d}}{\text{orbital_p}}\right) \frac{3.846 \cdot 10^{26} W}{4 \cdot \pi \cdot (\text{s_m_distance} \cdot 1000)^2} \)</span></p> <p><strong>average_power</strong> is the target of prediction and thus of interest to estimate from other parameters.</p> <p><strong>thp2 </strong>provides a reference model that has been developed by ESA engineers to predict <strong>average_power.</strong></p>
Diversity in the Expressed Genomic Host Response to Myocardial Infarction - Validation Dataset
<p>External validation was performed by separately hierarchically clustering 934 patients with STEMI in an independent cohort[1] into 2 groups (232 and 702 individuals) based on Illumina HT12v4-profiled PBMC expression (median time 21 hour between cardiac catheterization and blood sampling). Probes with most variable expression intensities (SD≥0.5, 216 probes, excluding ribosomal genes) were used. From the 20 most differentially expressed genes in the discovery cohort described in the manuscript Toma et al. 2022 [2], 19 were available in the validation cohort.</p> <p>Column names include the Illumina identifyer and the mapped gene name as used in the discovery cohort. Values are log2-transformed, quantile-normalized, batch-corrected values, see also [1] for methodological details.</p> <p>Acknowledgement:</p> <p>This work is supported by LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and by means of the Free State of Saxony within the framework of the excellence initiative.</p> <p> </p> <p>1) Teren A, Kirsten H, Beutner F, Scholz M, Holdt LM, Teupser D, Gutberlet M, Thiery J, Schuler G, Eitel I. Alteration of multiple leukocyte gene expression networks is linked with magnetic resonance markers of prognosis after acute st-elevation myocardial infarction. <em>Scientific Reports</em>. 2017;7:41705</p> <p>2) Toma A, dos Santos C, Burzyńska B, Góra M, Kiliszek M, Stickle N, Kirsten H, Kosyakovsky L, Wang B, van Diepen S, Epelman S, Szekely Y, Marshall JC, Billia F, Lawler PR (2022), Diversity in the Expressed Genomic Host Response to Myocardial Infarction, submitted.</p>
Data of FigS7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS7, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS7.PNG). The Corresponding raw data and subsequent data analysis obtained contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt) and one file as csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv).</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.