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54,099 results for “Expression”
Data for investigation of cell-type-specific expression and regulation in Setbp1 (p.S858R) atypical SGS male mice
<p><strong>processed.tar.gz, contains all files from the data directory associated with this projects and includes the following:</strong></p> <ul> <li><strong>setbp1_targets.csv</strong> : list of human SETP1 gene targets from ChIP-seq experiments converted to mouse and enriched for additional interactions from literature references (https://pubmed.ncbi.nlm.nih.gov/35685777/ and https://www.nature.com/articles/s41467-018-04462-8, </li> <li><strong>seurat_objects/</strong> <ul> <li><strong>kidney_integrated_celltypes.Rdata </strong>: filtered and preprocessed seurat object with cell type assignments for input to SoupX processing</li> <li><strong>kidney_integrated_celltypes_postSoup.Rdata</strong> : preprocessed and quality controlled seurat object with final cell type assignments after SoupX processing</li> <li><strong>setbp1_cerebralintcelltypes.Rdata</strong> : preprocessed and quality controlled seurat object with final cell type assignments</li> </ul> </li> <li><strong>decoupleR_CollecTRI/</strong> <ul> <li><strong>mouse_prior_tri.csv</strong> : accessed May 2023, it is unclear how versioning or accessions are used so we provided the Mouse CollecTRI (https://github.com/saezlab/CollecTRI) used in this study to calculate cell-type-specific TF activity</li> </ul> </li> <li><strong>motif_inputs/</strong> <ul> <li><strong>Mus_motif_all.txt </strong>: TF-motif PANDA input for mm10 reference genome enriched for</li> </ul> </li> <li><strong>expression_inputs/</strong> : expression matrices for all cell types for both tissues and conditions (n = 50) used to construct cell-type-specific PANDA networks</li> </ul> <p><strong>The below files are from the data/results directory of this associated project and include the following:</strong></p> <ul> <li><strong>alpaca.tar.gz</strong> : directory containing differential community analysis outputs from ALPACA</li> <li><strong>seurat.tar.gz </strong>: directory containing SoupX intermediates, as well as the</li> </ul>
Alliance of Genome Resources Gene Expression Data
<p>Tab separated formatted spreadsheets of gene expression annotations from the Alliance of Genome Resources. Gene expression data include temporal and/or spatial localization of transcripts and proteins in a wild-type background.</p> <p>File includes annotations for the following organisms:</p> <ul> <li>Caenorhabditis elegans (nematode; NCBI:txid 6239)</li> <li>Danio rerio (zebrafish;NCBI:txid 7955)</li> <li>Drosophila melanogaster (fruit fly; NCBI:txid 7227)</li> <li>Mus musculus (mouse; NCBI:txid10090)</li> <li>Rattus norvegicus (rat; NCBI:txid 10116)</li> <li>Saccharomyces cerevisiae (yeast; NCBI:txid 559292)</li> <li>Xenopus laevis (African clawed frog; NCBI:txid 8355)</li> <li>Xenopus tropicalis (Western clawed frog; NCBI:txid 8364)</li> </ul>
Visual opsin gene expression evolution in the adaptive radiation of cichlid fishes of Lake Tanganyika
<p>Tuning the visual sensory system to the ambient light is essential for survival in many animal species. This is often achieved through duplication, functional diversification, and/or differential expression of visual opsin genes. Here, we examined 753 new retinal transcriptomes from 112 species of cichlid fishes from Lake Tanganyika to unravel adaptive changes in gene expression at the macro-evolutionary and ecosystem level of one of the largest vertebrate adaptive radiations. We found that, across the radiation, all seven cone opsins – but not the rhodopsin – rank among the most differentially expressed genes in the retina, together with other vision-, circadian-rhythm-, and haemoglobin-related genes. We propose two new visual palettes characteristic of very shallow- and deep-water living species, respectively, and show that visual system adaptations along two major ecological axes, macro-habitat and diet, occur primarily via gene expression variation in a subset of cone opsin genes.</p>
Multi-view emotional expressions dataset
<p>Multi-view emotional expressions dataset (MEED) using 2D pose estimation.</p>
Results of the differential gene expression analysis in SIV infection in Chlorocebus sabaeus and Macaca mulatta
<p>Results of differential gene expression analysis in SIV infection in Chlorocebus sabaeus and Macaca mulatta.</p> <p>From the transcriptome data repository MACE (http://mace.ihes.fr)</p>
Transcriptome analysis of anuran breeding glands reveals a surprisingly high expression and diversity of NNMT-like genes
<p><strong>Abstract</strong></p> <p>In many amphibians, males have sexually dimorphic breeding glands, which can produce proteinaceous or volatile pheromones, used for intraspecific communication. In this study we analyse two types of glands in the Mexican treefrog species <em>Ptychohyla macrotympanum </em>(Hylidae) – large ventrolateral glands and small nuptial pads on their fingers – using histology, whole-transcriptome sequencing and phylogenetic analyses. We found strong differences in glandular tissue composition and gene expression patterns between the two breeding gland types. In both glands we only found low expression of protein pheromone candidates. Instead, in the ventrolateral glands, gene expression was strikingly dominated by nicotinamide N-methyltransferase (NNMT)-like genes. Diversity of these genes was remarkably high, with at least 68 distinct NNMT-like genes. Our phylogenetic comparative analysis of the diversity of NNMT-like genes across vertebrates indicates that the extreme diversity of this gene is largely a frog-specific phenomenon and can be traced to large numbers of relatively recent gene duplications occurring independently in many lineages. The strong dominance and astonishing diversity of NNMT-like genes found in anurans in general, and in their sexually dimorphic breeding glands specifically, suggests an important function of NNMT-like proteins for anuran reproduction, possibly being related to volatile pheromone production.In many amphibians, males have sexually dimorphic breeding glands, which can produce proteinaceous or volatile pheromones, used for intraspecific communication. In this study we analyse two types of glands in the Mexican treefrog species <em>Ptychohyla macrotympanum </em>(Hylidae) – large ventrolateral glands and small nuptial pads on their fingers – using histology, whole-transcriptome sequencing and phylogenetic analyses. We found strong differences in glandular tissue composition and gene expression patterns between the two breeding gland types. In both glands we only found low expression of protein pheromone candidates. Instead, in the ventrolateral glands, gene expression was strikingly dominated by nicotinamide N-methyltransferase (NNMT)-like genes. Diversity of these genes was remarkably high, with at least 68 distinct NNMT-like genes. Our phylogenetic comparative analysis of the diversity of NNMT-like genes across vertebrates indicates that the extreme diversity of this gene is largely a frog-specific phenomenon and can be traced to large numbers of relatively recent gene duplications occurring independently in many lineages. The strong dominance and astonishing diversity of NNMT-like genes found in anurans in general, and in their sexually dimorphic breeding glands specifically, suggests an important function of NNMT-like proteins for anuran reproduction, possibly being related to volatile pheromone production.</p> <p> </p> <p><strong>Supplementary datasets accompanying the paper:</strong></p> <p>- final RNAseq assemblies of the ventrolateral glands and the nuptial pads of <em>Ptychohyla macrotympanum</em><br> - fasta-file of all <em>Ptychohyla</em>-NNMT-like genes found in this study</p>
Synthetic gene expression data with underlying gene network
<p>This is the synthetic gene expression data along with the underlying gene network used in the simulation studies of Hu and Szymczak (2023) for evaluating network-guided random forest.</p> <p>In this dataset we consider the situation of 1000 genes and 1000 samples each for training and testing sets. Each file contains a list of 100 replications of the considered scenario which can be identified via the file name. In particular, we consider 6 different scenarios depending on the number of disease modules and how are the effects of disease genes distributed within the disease module. When there are disease genes, we also consider 3 different levels of effect sizes. The binary responses are then generated via a logistic regression model. More details on these scenarios and the data generation mechanism can be found in Hu and Szymczak (2023).</p> <p>The data is generated by the function <em>gen_data</em> in R package <em>networkRF</em> which can be accessed at https://github.com/imbs-hl/networkRF. To obtain the datasets with 3000 genes, which is the other part of the data used in the simulation studies of Hu and Szymczak (2023), simply modify the <em>num.var</em> argument of the function <em>gen_data.</em> More descriptions on the implementation and the format of the output can be found in the help page of the R package.</p>
Geology constrains biomineralization expression and functional trait distribution in the Mountainsnails (Oreohelix)
<p><strong>Aim</strong>: Geographic variation in metabolic resources necessary for functional trait expression can set limits on species distributions. For species that need to produce and maintain biomineralized traits for survival, spatial variation in mineral macronutrients may constrain species' distributions by limiting the expression of biomineralized traits. Here, we examine whether threatened, heavily biomineralized <em>Oreohelix</em> land snails are restricted to CaCO<sub>3</sub> rock regions, if they incorporate greater amounts of CaCO<sub>3</sub> rock carbon in their shell than less biomineralized smooth forms, and if ornamentation increases shell strength.</p> <p><strong>Location</strong>: Western United States</p> <p><strong>Methods</strong>: We used random forest (RF) classification models at multiple spatial resolutions to evaluate the contribution of topographic, vegetation, climate, and geologic variables in predicting the presence of heavily biomineralized shell ornaments. We then measured and compared shell biometric variables, <sup>14</sup>C/<sup>12</sup>C ratios, and peak force for fracture for ornamented and smooth forms from calcareous and non-calcareous substrates.</p> <p><strong>Results</strong>: Distance to CaCO<sub>3</sub> rock was the most important variable in all trait distribution models and was highly associated with local ornamentation classification and forecasted distribution. Pairwise comparisons of <sup>14</sup>C/<sup>12</sup>C ratios in closely occurring ornamented vs. smooth population pairs revealed ornamented forms incorporate greater CaCO<sub>3</sub> rock carbon than smooth forms. Ornamented types measured in this study were generally heavier and required greater peak force for fracture than smooth snails, except when compared to smooth forms sampled from CaCO<sub>3</sub> rock.</p> <p><strong>Main</strong> <strong>conclusions</strong>: Biomineralization expression, species distribution, and trait function appear to be constrained by mineral supply in a highly diverse group of land snails. This trait-environment relationship suggests similar CaCO<sub>3</sub> macronutrient constraints may modulate biomineralization expression and restrict species distribution in other terrestrial molluscs and has a direct impact on the management of <em>Oreohelix</em> species.</p>
isoMIGA: Expression and Splicing QTL Summary Statistics in Human Microglia
<p>https://github.com/RajLabMSSM/isoMiGA </p> <p>Full associations and top association summary statistics for QTLs mapped in a multi-ethnic meta-analysis of human microglia, as part of the isoform-centric Microglia Genomic Atlas (isoMiGA) project.</p> <p>Sample size = 555 samples from 391 unique donors</p> <p>6 cohorts (Raj MFG, Raj STG, Raj, SVZ, Raj, THA, Roussos, Gaffney) meta-analysed using the linear mixed model random-effects meta-analysis software mmQTL (PMID: 35058635).</p> <p>Each file has the following naming convention:</p> <p>{REFERENCE}_{PHENOTYPE}_{ASSOC}.tsv.gz</p> <p><strong>References</strong></p> <p>The following two transcript references were used for mapping phenotypes:</p> <p>1. GENCODE - GENCODE v38 comprehensive transcripts (https://www.gencodegenes.org/human/release_38.html)</p> <p>2. Union - a union of all GENCODE v38 transcripts with an additional 35,879 novel transcripts identified from long-read RNA-seq in 30 human microglia samples. GTF to be uploaded in Zenodo.</p> <p><strong>Phenotypes</strong></p> <p>The following phenotypes were tested for genetic association:</p> <ul> <li>expression: total gene expression of each gene following voom normalization of read counts.</li> <li>transcript: transcript usage - each transcript expression normalized by TPM divided by the total TPM for that gene</li> <li>leafcutter: junction usage using the Leafcutter framework to count and cluster intron-splicing junction reads. Each phenotype is the relative usage of the intron against the total count of the introns within that cluster.</li> <li>SUPPA_A3: alternate 3' splice site usage, as identified by SUPPA2 from transcript TPMs</li> <li>SUPPA_A5: alternate 5' splice site usage</li> <li>SUPPA_AF: alternate first exon usage</li> <li>SUPPA_AL: alternate last exon usage</li> <li>SUPPA_RI: intron retention usage</li> <li>SUPPA_SE: exon skipping usage</li> </ul> <p>All phenotype matrices were scaled and centred and then quantile normalized.</p> <p><strong>Associations</strong></p> <p>Top associations (top_assoc.tsv.gz) list the SNP-feature pair with the lowest adjusted P-value (qval) for that feature.</p> <p>Full associations (full_assoc.tsv.gz) list all tested SNP-feature pairs.</p> <p><strong>Data dictionary</strong></p> <p>The columns of the two association files only differ by the presence of the qval column in the top associations.</p> <p>feature: the phenotype being tested</p> <p>variant_id: the genetic variant being tested</p> <p>chr: chromosome</p> <p>pos: position (hg38)</p> <p>ref: reference allele</p> <p>alt: alternate allele</p> <p>Allele: the effect allele that the beta is relative to</p> <p>beta_tissue_0: Gaffney cohort beta</p> <p>sd_tissue_0: Gaffney cohort standard error</p> <p>z_tissue_0: Gafney cohort Z-score</p> <p>beta_tissue_1: Roussos cohort beta</p> <p>sd_tissue_1: Roussos cohort standard error</p> <p>z_tissue_1: Roussos cohort Z-core</p> <p>beta_tissue_2: Raj MFG cohort beta</p> <p>sd_tissue_2: Raj MFG cohort standard error</p> <p>z_tissue_2: Raj MFG cohort Z-score</p> <p>beta_tissue_3: Raj STG cohort beta</p> <p>sd_tissue_3: Raj STG cohort standard error</p> <p>z_tissue_3: Raj STG cohort Z-score</p> <p>beta_tissue_4: Raj SVZ cohort beta</p> <p>sd_tissue_4: Raj SVZ cohort standard error</p> <p>z_tissue_4: Raj SVZ cohort Z-score</p> <p>beta_tissue_5: Raj THA cohort beta</p> <p>sd_tissue_5: Raj THA cohort standard error</p> <p>z_tissue_5: Raj THA cohort Z-score</p> <p>fixed_beta: Fixed effect meta-analysis estimate of the beta</p> <p>fixed_sd: Fixed effect meta-analysis standard error of the beta</p> <p>fixed_z: Fixed effect meta-analysis Z-score</p> <p>Random_Z: Random effect meta-analysis Z-score</p> <p>Fixed_P: Fixed effect meta-analysis P-value</p> <p>Random_P: Random effect meta-analysis P-value</p> <p>Fixed_bonf: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni)</p> <p>Random_bonf: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni)</p> <p>Fixed_FDR: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR)</p> <p>Random_FDR: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR)</p> <p>qval: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) and for the number of features tested in the dataset (Storey's q value)</p> <p> </p> <p><br> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Differentially-expressed genes in blood in response to lipopolysaccharide in three rodent species
<p>Infection tolerance in rodents was examined by injecting single-dose lipopolysaccharide (LPS) to induce inflammation in <span><em>Peromyscus</em> <em>leucopus</em></span><span> (LL stock), the white-footed deermouse also reservoir for Lyme disease and </span><span><em>Mus</em> <em>musculus</em></span><span> (outbred CD-1 breed), the house mouse, and </span><span><em>Rattus</em> <em>norvegicus</em></span><span>, the brown rat (Fischer strain). Reaction to LPS was analyzed in the blood of challenged rodents and compared to control animals. As natural reservoirs of zoonoses deermice show significant anti-inflammatory response as described in "An Infection-Tolerant Mammalian Reservoir for Several Zoonotic Agents Broadly Counters the Inflammatory Effects of Endotoxin" (</span><a href="https://doi.org/10.1128/mBio.00588-21)" rel="noopener"><span>https://doi.org/10.1128/mBio.00588-21)</span></a><span>. The project and the description of the samples are described under the following NCBI BioProjects: PRJNA975149 (</span><a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA975149" rel="noopener"><span>https://www.ncbi.nlm.nih.gov/bioproject/PRJNA975149)</span></a><span> for mouse and deermouse and PRJNA973677 (</span><a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA973677)" rel="noopener"><span>https://www.ncbi.nlm.nih.gov/bioproject/PRJNA973677)</span></a><span>. This project is a follow-up project focusing on the transcriptomic analysis of the whole blood bulk RNA-seq and further analysis of differentially expressed genes (DEG) between the treatment arm and controls. Complete fold change and false discovery rate for all three rodent species used for the current Dryad set are previously published (<a href="https://doi.org/10.7280/D1470Z">https://doi.org/10.7280/D1470Z</a>). Here we report that deermice tolerance to infection is partly due to lower expression of interferon-gamma in comparison to mice and rats. <br></span></p>
Inter-generational nuclear crosstalk links the control of gene expression to programmed genome rearrangements during the Paramecium sexual cycle
<p>Multinucleate cells are found in many eukaryotes, but how multiple nuclei coordinate their functions is still poorly understood. In the cytoplasm of the ciliate <em>Paramecium tetraurelia</em>, two micronuclei (MIC) serving sexual reproduction coexist with a somatic macronucleus (MAC) dedicated to gene expression. During sexual processes, the MAC is progressively destroyed while still ensuring transcription and new MACs develop from copies of the zygotic MIC. Several gene clusters are successively induced and switched off before vegetative growth resumes. Concomitantly, programmed genome rearrangements (PGR) remove transposons and their relics from the new MACs. Development of the new MACs is controlled by the old MAC, since the latter expresses genes involved in PGR, including the <em>PGM</em> gene encoding the essential PiggyMac endonuclease that cleaves the ends of eliminated sequences. Using RNA deep sequencing and transcriptome analysis, we show that impairing PGR up-deregulates key PGR genes, together with ~600 other genes possibly also involved in PGR. Among these genes, 42% are no longer induced when no new MACs are formed, including 180 genes that are co-expressed with <em>PGM </em>under all tested conditions. We propose that bi-directional crosstalk between the two coexisting generations of MACs links gene expression to the progression of MAC development.</p>
Genotype of expression quantitative loci (eQTL) analyses for bovine blood and liver
<p>To identify expression quantitative loci (eQTL) operating in bovine blood and liver, 238 animals were genotyped uisng Illumina BovineHD genotyping arrary. For this file minor allele is used as ref as the default in plink.</p>
The Continuative Expressions Database
<p>This is the raw dataset for the paper "Towards a typology of continuative expressions", accepted for publication in Linguistic Typology at the Crossroads. The database is also available at <a href="https://anapanifica.github.io/continuative/">https://anapanifica.github.io/continuative/</a>.</p>
Spectral decomposition coefficients for spatiotemporal gene expression pattern identification in Bacillus subtilis swarm development
<p>Spectral decomposition coefficients, spatiotemporal gene expression pattern identification and multidimensional scaling coordinates of spatiotemporal transcriptomics data and image analysis results used to create Figure 2 in the scientific article "Simultaneous spatiotemporal transcriptomics and microscopy of <em>Bacillus subtilis</em> swarm development reveal cooperation across generations" by the following authors: Hannah Jeckel*, Kazuki Nosho*, Konstantin Neuhaus, Alasdair D. Hastewell, Dominic J. Skinner, Dibya Saha, Niklas Netter, Nicole Paczia, Jörn Dunkel, Knut Drescher. The symbol "*" indicates an equal contribution.</p> <p>This data consists of two excel sheets, one for the transcriptomics data and one for image analysis results (physical properties).</p> <p>Genes were ranked according to a spatiotemporal information score defined in the publication described above. For each gene, its name and ID (derived from locus tag) are given as identifiers. For information of reference genome used for mapping and convention on how gene names are chosen, see <a href="https://drescherlab.org/data/swarm-transcriptome/">https://drescherlab.org/data/swarm-transcriptome/</a>. Other columns in the sheet represent the spatiotemporal information score, assigned spatiotemporal pattern number, decomposition coefficient, multidimensional scaling coordinates and gene function.</p> <p>Physical properties are measured from short microscopy videos and defined in the publication mentioned above. This excel sheet contains property name, spatiotemporal information score, spectral decomposition coefficients and multidimensional scaling coordinates.</p>
Codeletion of 1p and 19q determines distinct gene methylation and expression profiles in IDH-mutated oligodendroglial tumors _ Dataset
<p>Overall design: Genome-wide DNA methylation profiling of oligodendroglial tumors (OTs) and five non tumoral brain tissue (NTBT) samples. The Illumina Infinium Human DNA methylation 450k Beadchip was used to obtain DNA methylation profiles across approximately 450,000 CpGs in tumoral samples. Samples included 46 OTs and 5 NTBT.</p> <p>Bisulphite converted DNA from the 51 samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip</p> <p>Extracted molecule: genomic DNA</p> <p>Platform: Illumina HumanMethylation450 BeadChip (HumanMethylation450_15017482)</p> <p>Label protocol: Standard Illumina Protocol</p> <p>Hybridization protocol: bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation 450K Beadchip using standard Illumina protocol</p> <p>Scan protocol: Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting </p> <p>Data processing: BeadStudio software v3.2</p> <p>Data format: IDAT files</p>
RNA-seq based analysis of gene expression in thyroids of wild-type and Keap1 knockdown mice after exposure to excess iodide.
<p>C57BL/6J Keap1flox/flox mice were developed by Prof. Masayuki Yamamoto (DOI: 10.1016/j.bbrc.2005.10.185). These mice express lower levels of Keap1 because of the loxP site insertions (DOI: 10.1128/MCB.01591-09) and are designated as Keap1KD. 3-4 months old WT and Keap1KD mice fed a standard diet (KLIBA NAFAG 3242, Switzerland) containing 1.5 mg/kg sodium iodine were given normal tap water with or without 0.05% sodium iodide (Sigma, St Louis, MO, USA) for 7 days. Hence, the following groups of mice were included:</p> <p>WT mice on control water (n=8)-designated as wt in the file</p> <p>WT mice exposed to excess iodide (WT-IOD, n=6)- designated as wti in the file</p> <p>Keap1KD mice on control water (n=8)- designated as kp in the file</p> <p> and Keap1KD mice exposed to excess iodide (Keap1KD-IOD, n=6)- designated as kpI in the file. </p> <p> Mice were maintained in the animal facility of the Department of Physiology at the University of Lausanne in temperature-, light-, and humidity-controlled rooms with a 12-hour light/dark cycle. All animal procedures were in accordance with Swiss legislature and the study was approved by the Canton of Vaud SCAV.</p> <p>RNA from individual mouse thyroids was prepared as previously described (DOI: 10.1007/978-1-4939-3756-1_25) and was submitted to Alithea Genomics (Switzerland). The bulk RNA barcoding and sequencing (BRB-seq) libraries were generated and sequenced as described previously (DOI: 10.1186/s13059-019-1671-x) to a depth of approximately 1.2 million raw reads per sample.</p> <p>This research was funded by the Swiss National Science Foundation Research Grants 310030_212558, IZCOZ0_205415 and IZCOZ0_177070</p> <p></p> <p></p>
Image stacks for full-body transcription factor expression atlas with completely resolved cell identities in C. elegans
<p>Each image stack presented as zip file. Once decompressed, each folder contain '.ano' linker file, straightening C. elegans L1 images file, the segmentation mask image file and the cell annotation file. The image files are stored in Peng Hanchuan RAW/TIFF format, and the cell annotation file is stored in simple comma separated values format. To visualize the image stack data, drag the '.ano' linker file to VANO interface. </p> <p>vano_win32_1.741.zip contains VANO for worm visualization.</p>
Data from: Characterisation of GFAP-Expressing Glial Cells in the Dorsal Root Ganglion after Spared Nerve Injury
<p>This data pertain to the paper titled "Characterisation of GFAP-Expressing Glial Cells in the Dorsal Root Ganglion after Spared Nerve Injury " by Elena A. Konnova, Alexandru-Florian Deftu, Paul Chu Sin Chung, Marie Pertin, Guylène Kirschmann, Isabelle Decosterd and Marc R. Suter. The name of the data files correspond to the for each figure in the study. The data file in .csv format are organized so that they can easily be opened in R or other analysis language. To understand them and how they are labelled, it is advised to open the figure next to them and find the appropriate panel.</p><p>Here are included:</p><ul><li>the representative images of immunohistochemistry for GFAP, IBA1, Ki67, NeuN, ATF3, FABP7, GS, Cx43, Kir4.1, MBP, L1CAM in the dorsal root ganglia (DRG) of hGFAP-CFP mice after spared nerve injury (SNI).</li><li>the western blot images of DRG samples and FACS sorted CFP+ cells from the DRG of hGFAP-CFP mice after SNI</li><li>the voltage clamp data of GFAP+ cells from DRG of hGFAP-CFP mice after SNI</li></ul><p> </p>
The raw microarray data and the differential expression analysis results from "Manipulating the growth environment through co-culture to enhance stress tolerance and viability of probiotic strains in the gastrointestinal tract".
<p>The signal data for each spot were subsequently quantified by using Feature Extraction software (Agilent Technologies).M1.txt to M5.txt: monoculture; C1.txt to C5.txt: co-culture; P1.txt to P5.txt: pH-controlled monoculture. The differential expression analysis results were obtained by using limma.</p>
Phase 1 Study to Evaluate the Effect of DS-8201a on the QT/QTc Interval and Pharmacokinetics in HER2-Expressing Breast Cancer
ClinicalTrials.gov study NCT03366428. IPD Sharing: YES. Countries: 1. Publications: 1.
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.