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54,099 results for “Expression”
Gene expression counts from induced Pluripotent Stem Cells
<p><strong>File description:</strong></p> <ol> <li> <p>Gene-level counts using the gtf file from the release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a></p> </li> <li> <p>Split counts spanning from one exon to another using an annotation-free algorithm, therefore capturing new splice sites</p> </li> <li> <p>Non-split counts covering exon-intron boundaries</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the workflow DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Maintainer: </strong>Vicente A. Yépez, <a href="mailto:yepez@in.tum.de">yepez@in.tum.de</a></p> <p><strong>URL:</strong> <a href="https://github.com/gagneurlab/drop/">https://github.com/gagneurlab/drop/</a></p> <p> </p> <p><strong>Title: </strong>induced Pluripotent Stem Cells<br> <strong>Number of samples:</strong> 330<br> <strong>Tissue:</strong> iPSCs<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34<br> <strong>Disease:</strong> None<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True<br> <strong>Dataset contact:</strong> Marc Bonder, marcj89 at gmail.com</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p>
Data from: Differential gene expression during recall of behaviorally conditioned immune enhancement in rats: a pilot study
<p><strong>Background:</strong> Behaviorally conditioned immune functions are suggested to be regulated by bidirectional interactions between CNS and peripheral immune system <em>via</em> the hypothalamic-pituitary-adrenal (HPA) axis, sympathetic nervous system (SNS), and the parasympathetic nervous system (PNS). Since the current knowledge about biochemical pathways triggering conditioned immune enhancement is limited, the aim of this pilot study was gaining more insights into that.</p> <p><strong>Methods: </strong>Rats were conditioned with camphor smell and poly I:C injection, mimicking a viral infection. Following stimulus re-exposure, animals were sacrificed at different time points, and neural tissues along the HPA axis was analyzed with a rat genome array together with plasma protein using Luminex analysis.</p> <p><strong>Results:</strong> In the hypothalamus, we observed a strong upregulation of genes related to Wnt/β-catenin signaling (Otx2, Spp1, Fzd6, Zic1), monoaminergic transporter Slc18a2 and opioid-inhibitory G-protein Gpr88 as well as downregulation of dopaminergic receptors, vasoactive intestinal peptide Vip, and pro-melanin-concentrating hormone Pmch. In the pituitary, we recognized mostly upregulation of steroid synthesis in combination with GABAergic, cholinergic and opioid related neurotransmission, in adrenal glands, altered genes showed a pattern of activated metabolism plus upregulation of adrenoceptors Adrb3 and Adra1a. Data obtained from spleen showed a strong upregulation of immunomodulatory genes, chemo-/cytokines and glutamatergic/cholinergic neurotransmission related genes, as also confirmed by increased chemokine and ACTH levels in plasma.</p> <p><strong>Conclusions:</strong> Our data indicate that in addition to the classic HPA axis, there could be additional pathways as e.g. the cholinergic anti-inflammatory pathway (CAIP), connecting brain and immune system, modulating and finetuning communication between brain and immune system.</p>
Expression of immunoglobulin constant domain genes in neurons of the mouse central nervous system
<p>Data related to the publication "Expression of immunoglobulin constant domain genes in neurons of the mouse central nervous system". </p> <p>The fasta file (.fa) contains the sequence of neuronal FC-Ighm</p> <p>The .pdb files contain the model of the two different protein versions of Ighm</p> <p>The excel file contains the data of the quantification of co-expression and the ATG prediction results.</p>
Gene expression and splicing counts from the Yepez, Gusic et al study - non strand-specific
<p><strong>File description:</strong></p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p> </p> <p>The gene counts were originated using the GTF file from release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a>, and the split and non-split counts contain only the annotated junctions from the same release.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Number of samples: </strong>154<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34<br> <strong>Disease (ICD-10: N):</strong> E75: 1, E79: 13, E88: 118, G31: 9, K72: 3, NONE: 10<br> <strong>Strand specific: </strong>FALSE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment<br> <strong>Dataset contact: </strong>Vicente Yepez, yepez@in.tum.de; Julien Gagneur, gagneur@in.tum.de; Holger Prokisch, prokisch@helmholtz-muenchen.de</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p>
Data for "Excess labile carbon promotes the expression of virulence factors in coral reef bacterioplankton"
<p>This publication contains 27 coral reef bacterioplankton metagenome-assembled genomes (MAGs) from the following paper: </p> <p>Cárdenas, A., Neave, M. J., Haroon, M. F., Pogoreutz, C., Rädecker, N., Wild, C., Gärdes, A., & Voolstra, C. R. (2018). Excess labile carbon promotes the expression of virulence factors in coral reef bacterioplankton. <em>The ISME Journal</em>, <em>12</em>(1), 59–76. https://doi.org/10.1038/ismej.2017.142</p> <p> </p>
Genome-wide expression profile of AAV2-infected normal human fibroblasts
<p>Datasets containing the genome-wide expression profile of AAV2-infected normal human fibroblasts.</p> <p>Raw data: results--A1--over--M1-3.txt</p> <p>p<0.01, reads>40: A1_M1_p0.01_r40_fc not restricted.txt</p>
Figure 4 in Gene expression changes in response to combination stresses in Phaseolus vulgaris L. (Fabaceae)
Figure 4. Relative gene expression of some genes (OS, PR3, LOX, PR4 and PAL) by real-time PCR in Tetranychus
Figure 3 in Gene expression changes in response to combination stresses in Phaseolus vulgaris L. (Fabaceae)
Figure 3. Relative gene expression of some genes (OS, PR3, LOX, PR4 and PAL) by real-time PCR in Tetranychus
Figure 2 in Gene expression changes in response to combination stresses in Phaseolus vulgaris L. (Fabaceae)
Figure 2. Relative gene expression of some genes (OS, PR3, LOX, PR4 and PAL) by real-time PCR in Tetranychus
Figure 1 in Gene expression changes in response to combination stresses in Phaseolus vulgaris L. (Fabaceae)
Figure 1. Relative gene expression of some genes (OS, PR3, LOX, PR4 and PAL) by real-time PCR in Tetranychus
Text-fig. 6. Cornaceae. Alangium (a–e), Mastixia (f–r). a–e: Alangium, DMNH EPI.47806. Scale bar = 1 cm. b, e: Reflected light, palladium coated. a, c, d: Micro-CT scan surface rendering. a: Locule cast, face view of slightly larger locule. b: Face view of slightly smaller locule. c: Lateral view of the endocarp, the slightly enlarged left carpel separated from the smaller carpel by a longitudinal septal groove; the faint pitting in the groove suggestive of the septal vasculature. d, e: Views of either end of the endocarp, illustrating the size difference between the two carpels and the pitting in the septal groove suggestive of the septal vasculature. f–k: Mastixia USNM PAL 772362. Scale bar = 1 cm. f, g, j, k: reflected light, palladium coated; h, i: micro-CT scan surface rendering. f: Lateral view of endocarp, inferred dorsal germination valve groove facing the viewer. Note irregular, rugose, longitudinal ridges. g: Lateral view of endocarp, inferred germination valve with median longitudinal groove to left. h: Lateral view of endocarp reoriented with the same longitudinal groove to the right. i: Lateral view, rotated to ventral surface. j: View of one end of the endocarp, germination valve groove up. k: Opposite end view, with prominent radial ridges and intervening grooves, germination valve groove up. l–r: Mastixia USNM PAL 772363. Scale bar = 1 cm. l: View of one face of endocarp, displaying a groove that may represent the surficial expression of the dorsal infold of a Mastixia-like germination valve. Surface badly eroded, reflected light, palladium coated. m: Opposite face of endocarp displaying extensive erosion and a central hole interpreted as feeding damage. n: Lateral view; m, n micro-CT scan surface renderings. o: A view of one end, displaying the prominent groove, reflected light, palladium coated. p: Opposite end to (o). q: View as in (o); p, q micro-CT scan surface renderings. r: Virtual transverse section showing curved locule (arrows). in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 6. Cornaceae. Alangium (a–e), Mastixia (f–r). a–e: Alangium, DMNH EPI.47806. Scale bar = 1 cm. b, e: Reflected light, palladium coated. a, c, d: Micro-CT scan surface rendering. a: Locule cast, face view of slightly larger locule. b: Face view of slightly smaller locule. c: Lateral view of the endocarp, the slightly enlarged left carpel separated from the smaller carpel by a longitudinal septal groove; the faint pitting in the groove suggestive of the septal vasculature. d, e: Views of either end of the endocarp, illustrating the size difference between the two carpels and the pitting in the septal groove suggestive of the septal vasculature. f–k: Mastixia USNM PAL 772362. Scale bar = 1 cm. f, g, j, k: reflected light, palladium coated; h, i: micro-CT scan surface rendering. f: Lateral view of endocarp, inferred dorsal germination valve groove facing the viewer. Note irregular, rugose, longitudinal ridges. g: Lateral view of endocarp, inferred germination valve with median longitudinal groove to left. h: Lateral view of endocarp reoriented with the same longitudinal groove to the right. i: Lateral view, rotated to ventral surface. j: View of one end of the endocarp, germination valve groove up. k: Opposite end view, with prominent radial ridges and intervening grooves, germination valve groove up. l–r: Mastixia USNM PAL 772363. Scale bar = 1 cm. l: View of one face of endocarp, displaying a groove that may represent the surficial expression of the dorsal infold of a Mastixia-like germination valve. Surface badly eroded, reflected light, palladium coated. m: Opposite face of endocarp displaying extensive erosion and a central hole interpreted as feeding damage. n: Lateral view; m, n micro-CT scan surface renderings. o: A view of one end, displaying the prominent groove, reflected light, palladium coated. p: Opposite end to (o). q: View as in (o); p, q micro-CT scan surface renderings. r: Virtual transverse section showing curved locule (arrows).
Dataset for "Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR"
<p>This dataset constitutes the pre-processed data contained in the publication 'Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR' Analytical Chemistry (2022) 94(14):5566-5574 (DOI: 10.1021/acs.analchem.1c05134).</p>
Supplementary Tables for "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort"
<p>Supplementary Tables from "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort".</p>
Processed and annotated yeast gene expression data from yeast2 and ygs98 platforms
<p>This dataset contains the following files:</p> <ul> <li><em>yeast2_processed_rds.tar.gz -</em> processed gene expression matrices from the yeast2 platform. The data is stored in binary R format (.rds).</li> <li><em>ygs98_processed_rds.tar.gz </em>- processed gene expression matrices from the yeast2 platform. The data is stored in binary R format (.rds).</li> <li><em>yeast2-curated-annotations.txt</em> - metadata for the yeast2 platform.</li> <li><em>ygs98-curated-annotations.txt</em> - metadata for the ygs98 platform.</li> </ul> <p> </p>
Depletion of cap-binding protein eIF4E dysregulates amino acid metabolic gene expression
<p><span>Protein synthesis is </span><span>metabolically costly </span><span>and</span><span> must be tightly coordinated with </span><span>changing </span><span>cellular needs and nutrient availability. T</span><span>he cap-binding protein eIF4E</span><span> makes </span><span>the earliest contact between mRNAs and the translation machinery</span><span>, offering a key regulatory nexus</span><span>. </span><span>W</span><span>e acute</span><span>ly</span><span> deplet</span><span>ed</span> <span>this essential protein </span><span>and </span><span>found </span><span>s</span><span>urprisingly modest effects on cell growth and </span><span>recovery of </span><span>protein synthesis.</span><span> Paradoxically, </span><span>impaired protein biosynthesis upregulated </span><span>genes involved in catabolism of aromatic amino acids</span><span>simultaneously with the </span><span>induction of the </span><span>amino acid</span><span> biosynthetic regulon</span> <span>driven</span> <span>by </span><span>the integrated stress response factor</span> <span>GCN4</span><span>. </span><span>W</span><span>e</span><span> further</span><span> identified translation</span><span>al </span><span>control</span><span> of </span><span>PCL5</span><span>, </span><span>a negative regulator of Gcn4, that provides a consistent protein-to-mRNA ratio under varied translation environments. </span><span>This</span> <span>regulation </span><span>depende</span><span>d in part</span><span> on a uniquely long poly-(A) tract in the </span><span>PCL5</span><span> 5´ UTR and poly-(A) binding protein. Collectively, these results highlight</span> <span>how eIF4E connects</span> <span>protein synthesis </span><span>to</span> <span>metabolic gene regulation</span><span>,</span><span>uncover</span><span>ing</span><span> new mechanisms control</span><span>ling</span> <span>translation</span><span> during environmental challenges.</span></p>
GEO gene expression dataset recompute for selected tumor samples
<div> <p>We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.</p> <p>All uploaded files are UTF-8, <code>.csv</code>-formatted matrices. The <code>*_expected_count.csv.gz</code> files are unlogged, raw expression counts as reported by <code>rsem-quantify-expression</code> (see details below). The associated <code>*_metadata.csv.gz</code> files contain metadata pertinent to each column of the corresponding expression matrix.<br>Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).</p> <p>Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.</p> <p>Each recompute has at least the <code>gene_id</code> column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.<br>Each associated metadata has at least the following columns:</p> <ul> <li><code>geo_accession</code>: The GEO sample ID of the sample.</li> <li><code>ena_sample</code>: The ENA sample ID of the sample.</li> <li><code>ena_run</code>: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.</li> </ul> <p>The remaining columns are derived from GEO metadata files and other ENA-provided data. Please refer to the <code>x.FASTQ</code> package for more information.</p> <h3>Pipeline Details</h3> <p>The alignment and quantification was made with the <code>x.FASTQ</code> tool available <a href="https://github.com/TCP-Lab/x.FASTQ">on Github</a> installed locally on an Arch Linux machine on commit <code>3a93dd77a70df59c74f7b15216c26f12cd918e81</code> running the Linux <code>6.7.8-zen1-1-zen</code> kernel with a <code>11th Gen Intel i7-1185G7 (8)</code> CPU and a <code>Intel TigerLake-LP GT2 [Iris Xe Graphics]</code> GPU. Please note that no sample filtering or omissions were done based on sample quality or sequencing depth. However, sensible trimming (e.g. low-quality bases and common adapters) was performed on all the samples.</p> <p>Reference genome was downloaded from Ensembl, version <code>hg38</code>. STAR was used to create the index genome with overhang set to <code>149</code>.</p> </div>
R code for differential gene expression and enrichment analyses
<p>The information about the magnitude of differences in thermal plasticity both between and within populations, as well as identification of the underlying molecular mechanisms are key to understanding the evolution of thermal plasticity. In particular, genes underlying variation in the physiological response to temperature can provide raw material for selection acting on plastic traits. Using RNAseq, we investigate the transcriptional response to temperature in males and females from bulb mite populations selected for the increased frequency of one of two discrete male morphs (fighter- and scrambler-selected populations) that differ in relative fitness depending on temperature. We show that different mechanisms underlie the divergence in thermal response between fighter- and scrambler-selected populations at decreased vs. increased temperatures. Temperature decrease to 18°C was associated with higher transcriptomic plasticity of males with more elaborate armaments, as indicated by a significant selection-by-temperature interaction effect on the expression of 40 genes, 38 of which were upregulated in fighter-selected populations in response to temperature decrease. In response to 28°C, no selection-by-temperature interaction in gene expression was detected. Hence, differences in phenotypic response to temperature increase likely depended on genes associated with their distinct morph-specific thermal tolerance. Selection on males also drove gene expression patterns in females. These patterns could be associated with temperature-dependent fitness differences between females from fighter- vs. scrambler-selected populations reported in previous studies. Our study shows that selection for divergent male sexually selected morphologies and behaviors has the potential to drive divergence in metabolic pathways underlying plastic response to temperature in both sexes.</p>
Mate choice in the brain: Species differ in how male traits 'turn on' gene expression in female brains
<p>Mate choice plays a fundamental role in speciation, yet we know little about the molecular mechanisms that underpin this crucial decision-making process. Stickleback fish differentially adapted to limnetic and benthic habitats are reproductively isolated and females of each species use different male traits to evaluate prospective partners and reject heterospecific males. Here, we integrate behavioral data from a mate choice experiment with gene expression profiles from the brains of females actively deciding whether to mate. We find substantial gene expression variation between limnetic and benthic females, regardless of behavioral context, suggesting general divergence in constitutive gene expression patterns, corresponding to their genetic differentiation. Intriguingly, female gene co-expression modules covary with male display traits but in opposing directions for sympatric populations of the two species, suggesting male displays elicit a dynamic genomic response that reflects known differences in female preferences. Furthermore, we confirm the role of numerous candidate genes previously implicated in female mate choice in other species, suggesting that evolutionary tinkering with these conserved molecular processes underlies divergent mate preferences and sexual isolation. Taken together, our study adds important new insights to our understanding of the molecular processes underlying female decision-making critical for generating sexual isolation and speciation.</p>
Source Data for main text and supplemental figures for manuscript "Community assessment of methods to deconvolve cellular composition from bulk gene expression"
<p>Source Data for main text and supplemental figures for manuscript "Community assessment of methods to deconvolve cellular composition from bulk gene expression"</p>
Fig. 15 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse
Fig. 15. Trychosis breviterebratus sp. nov. (A-F, KPM-NK 75758) ― A, lateral habitus; B, dorsal habitus; C, frons, frontal view; D, head and mesosoma, lateral view; E, propodeum, dorsal view; F, right wings.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.