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2,556 results for “RNAseq”
iPlacenta: hIPSC placenta-on-a-chip RNAseq data from 3D vs 2D, day 0 vs day 4 differentiation
<p>RNAseq data from hIPSC dervived trophoblasts seeded in 3D (OrganoPlate) or 2D surface at day 0 or day 4 differentiation. </p> <p>Description of file names found below</p> <table> <tbody> <tr> <td> <p><strong>SampleID/File name</strong></p> </td> <td> <p><strong>Condition- Differentiation day</strong></p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-1</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-2</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-3</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-4</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-5</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-6</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-7</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-8</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-9</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-10</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-11</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-12</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-13</p> </td> <td> <p>3D-Day4</p> </td> </tr> </tbody> </table>
Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states - raw count data set - RNAseq
<p>Raw count data of the RNAseq analysis of a project and manuscript under the title "Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states". The header of the table includes the subject identifiers except the first column "genes". The latter column includes all assessed gene identifiers.</p>
Introduction to bulk RNAseq analysis: supplementary material
<p><strong>Vampirium setup</strong></p><p>This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on <a href="https://github.com/hds-sandbox/bulk_RNAseq_course">Github</a>:</p><p>Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments</p><p>Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline.</p><p>Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology.</p><p>Slides.zip contains all the slides used in the workshop.</p><p>raw_reads.zip contains the raw reads from the bulk RNAseq experiment (<a href="https://doi.org/10.1016/j.celrep.2014.10.054">10.1016/j.celrep.2014.10.054</a>) used in this course.</p>
Complex basis of hybrid female sterility and Haldane's rule in Heliconius butterflies: Z-linkage and epistasis - RADseq and RNAseq reads, sterility phenotypes and pedigree
<p>RADseq and RNAseq reads (.fastq files), and sterility phenotypes and pedigree (.xlsx) using for QTL mapping of Heliconius pardalinus sterility crosses in Rosser, N., Edelman, N.B., Queste, L.M., Nelson, M., Seixas, F., Dasmahapatra, K.K. and Mallet, J., 2021. Complex basis of hybrid female sterility and Haldane’s rule in Heliconius butterflies: Z-linkage and epistasis, accepted for publication in Molecular Ecology. Queries to Neil Rosser (neil.rosser@york.ac.uk). </p> <p> </p>
RNASeq data of isolated murine glomeruli treated with Vitamin D3 and DMSO
<p>RNASeq data of isolated murine glomeruli (as described here <a href="https://dx.doi.org/10.1111%2Fbph.13667">10.1111/bph.13667</a>) treated with Vitamin D3 (100 nM) and DMSO (0.1%) for 6 days. </p>
Raw RNAseq count data for figure 4 - by Van de Beek et al., 2022
<p>This CSV file is generated by Van de Beek et al., (2022) and is part of "<strong><em>PRDM10</em></strong><strong> directs <em>FLCN</em> expression in a novel disorder overlapping with Birt-Hogg-Dubé syndrome and familial lipomatosis</strong>" to be published in Human Molecular Genetics.</p>
RNAseq analyses ANR MAORI project
<p>Results of DEseq2 analyses from RNAseq data</p> <p>Results of enrichment analyses using GO or KEGG</p> <p>Results of WGCNA data</p>
Single Cell RNAseq of Mouse Testis
<p>This is the processed data from the publication: "Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains" (https://doi.org/10.1101/393769)</p> <p>The raw data is avaliable at GEO: GSE113293</p> <p>Associated software is at https://zenodo.org/badge/latestdoi/140632831</p> <p>SDA_objects.zip contains key tables required for many functions, download this to use the shiny app. Contents:</p> <ul> <li>cell_data: data.table containing metadata of the cells. 80 columns including cell id, SDA component cell scores, Tsne coordinates, pseudotime, experimental group etc.</li> <li>data: a sparse matrix of normalised read counts (20322 cells by 19262 genes)</li> <li>gene_annotations: data.table containing gene locations, enrichment vs other tissues from bulk data, infertility gene status</li> <li>GO_enrich: data.table containing gene ontology enrichments for each component, the genes enriched, p.values and enrichment values</li> <li>principal_curves: list of output of princurve() containing the pseudotime trajetories</li> <li>SDAresults: output of SDA run loaded using SDAtools::load_results()</li> </ul> <p>Other R objects include:</p> <ul> <li>QC_count_matrix: Sparse matrix of raw count values for QC cells and genes</li> <li>cell_imputation_AUCs: data.table of PRAC AUC values for unimputed (train), mean cell, SDA (predict), ICA, PCA, NNMF, and MAGIC</li> <li>HocomocoV11_motifProbs_matrix: Matrix of regulation probabilities from MotifFinder using fixed motifs from HocomocoV11 database</li> <li>motifFinder_denovo: list of results of MotifFinder denovo motifs from promoters regions of genes from each component</li> <li>motifFinder_denovo_fixed: list of results of MotifFinder using 125 denovo motifs with fixed motif on all genes.</li> <li>tomtom_matched_motifs: data.table of TOMTOM matches of denovo motifs, plus metadata</li> </ul>
Processed data of single cell RNAseq of 8 human cell lines
<p>Cell annotation and UMI count matrix for 8 human cell lines:</p> <ul> <li>HCT116</li> <li>IMR90</li> <li>A549</li> <li>Ramos</li> <li>H1437</li> <li>HEK293</li> <li>K562</li> <li>Jurkat</li> </ul> <p>The data set is part of the publication in https://rdcu.be/bYEsu</p> <p> </p>
Arabidobsis Super50 2020 RNAseq
<p>Abiotic stresses cause oxidative damage in plants. Here, we demonstrate that foliar application of an extract from the seaweed Ascophyllum nodosum, SuperFifty (SF), largely prevents paraquat (PQ)-induced oxidative stress in Arabidopsis thaliana. While PQ-stressed plants develop necrotic lesions, plants pre-treated with SF (i.e., primed plants) were unaffected by PQ. Transcriptome analysis revealed induction of reactive oxygen species (ROS) marker genes, genes involved in ROS-induced programmed cell death, and autophagy-related genes after PQ treatment. These changes did not occur in PQ-stressed plants primed with SF. In contrast, upregulation of several carbohydrate metabolism genes, growth, and hormone signaling as well as antioxidant-related genes were specific to SF-primed plants. Metabolomic analyses revealed accumulation of the stress-protective metabolite maltose and the tricarboxylic acid cycle intermediates fumarate and malate in SF-primed plants. Lipidome analysis indicated that those lipids associated with oxidative stress-induced cell death and chloroplast degradation, such as triacylglycerols (TAGs), declined upon SF priming. Our study demonstrated that SF confers tolerance to PQ-induced oxidative stress in A. thaliana, an effect achieved by modulating a range of processes at the transcriptomic, metabolic, and lipid levels.</p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (1/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>Cneg</td> <td>RNASEQ-AVF1</td> <td>RNASEQ-AVF1_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF1_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF2</td> <td>RNASEQ-AVF2_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF2_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF3</td> <td>RNASEQ-AVF3_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF3_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF4</td> <td>RNASEQ-AVF4_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF4_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF5</td> <td>RNASEQ-AVF5_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF5_S5_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA A</td> <td>RNASEQ-AVF6</td> <td>RNASEQ-AVF6_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF6_S6_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA B</td> <td>RNASEQ-AVF7</td> <td>RNASEQ-AVF7_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF7_S7_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA C</td> <td>RNASEQ-AVF8</td> <td>RNASEQ-AVF8_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF8_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (2/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>TDP-43 siRNA D</td> <td>RNASEQ-AVF9</td> <td>RNASEQ-AVF9_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF9_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF10</td> <td>RNASEQ-AVF10_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF10_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF11</td> <td>RNASEQ-AVF11_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF11_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF12</td> <td>RNASEQ-AVF12_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF12_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF13</td> <td>RNASEQ-AVF13_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF13_S5_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF14</td> <td>RNASEQ-AVF14_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF14_S6_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos B+C</td> <td>RNASEQ-AVF15</td> <td>RNASEQ-AVF15_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF15_S7_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos A+B+C</td> <td>RNASEQ-AVF16</td> <td>RNASEQ-AVF16_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF16_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
Pancreatic adecarcinoma fibroblast subtype using RNAseq
<p>Cancer-associated fibroblasts (CAFs) are orchestrators of the pancreatic ductal adenocarcinoma (PDAC) microenvironment. Previously we described four CAF subtypes with specific molecular and functional features. Here, we have refined our CAF subtype signatures using RNAseq and immunostaining with the goal to define bioinformatically the phenotypic stromal and tumor epithelial states associated with CAF diversity. We used primary CAF cultures grown from patient PDAC tumors, human datasets (in-house and public, including single-cell analyses), genetically engineered mouse PDAC tissues, and patient-derived xenografts (PDX) grown in mice. We found that CAF subtype RNAseq signatures correlated with immunostaining. Tumors rich in periostin-positive CAFs were significantly associated with shorter overall survival of patients. Periostin-positive CAFs were characterized by high proliferation and protein synthesis rates, low αSMA expression, and were found in peri-/pre-tumoral areas. They were associated with highly cellular tumors and with macrophage infiltrates. Podoplanin-positive CAFs were associated with immune-related signatures and recruitment of dendritic cells. Importantly, we showed that the combination of periostin-positive CAFs and podoplanin-positive CAFs was associated with specific tumor microenvironment features in terms of stromal abundance and immune cell infiltrates. Podoplanin-positive CAFs identified an iCAF-like subset whereas periostin-positive CAFs were not correlated with the published myCAF/iCAF classification.</p> <p>Taken together, these results suggest that a periostin-positive CAF is an early, activated CAF, associated with aggressive tumors, whereas a podoplanin-positive CAF is associated with an immune-related phenotype. These two subpopulations cooperate to define specific tumor microenvironment and patient prognosis, and are of putative interest for future therapeutic stratification of patients.</p> <p> </p> <p><strong>Material and methods</strong></p> <p>Total RNA was extracted from FFPE sections using a high pure FFPE RNA isolation kit (Roche®, Basel, Switzerland) following the manufacturer’s protocol. RNA yield and quality was determined using a NanoDrop™ One spectrophotometer and fragment size was analyzed using an RNA ScreenTape assay run on a 4200 Bioanalyzer (Agilent Technologies®, Santa Clara, CA, USA . DV200 values representing the percentage of RNA fragments above 200 nucleotides in length were estimated, and cases with DV200 more than 30% were included for library preparation.<br> Library preparation was performed using QuantSeq 3’ mRNA-Seq REV (Lexogen® , Vienna, Austria) with an input of 150 ng of total FFPE RNA. The pool was sequenced on a NovaSeq 6000 system flow cell SP (Illumina Inc., San Diego, CA) using a 75-cycle, paired-end protocol providing approximately 10 million reads per sample. Base call files were converted to fastq format using Bcl2Fastq (Illumina®, San Diego, CA). All RNA-seq reads were aligned to the human reference genome (GRCh37, hg19) using STAR (version 2.6.1a_08-27), quantified using FeatureCount and Upper-Quartile normalized.<br> </p>
Mecp2 duplication iNeurons RNAseq data tables
<p><span>Genomic copy-number variations (CNVs) that can cause neurodevelopmental disorders often encompass many genes, which complicates our understanding of how individual genes within a CNV contribute to pathology. <em>MECP2</em> duplication syndrome (MDS or MRXSL in OMIM; OMIM#300260) is one such CNV disorder caused by duplications spanning methyl CpG-binding protein 2 (<em>MECP2</em>) and other genes on Xq28. Using an antisense oligonucleotide (ASO) to normalize <em>MECP2 </em>dosage is sufficient to rescue abnormal neurological phenotypes in mouse models overexpressing <em>MECP2 </em>alone, implicating the importance of increased <em>MECP2</em> dosage within CNVs of Xq28. However, because MDS CNVs span <em>MECP2</em> and additional genes, we generated human neurons from multiple MDS patient-derived induced pluripotent cells (iPSCs) to evaluate the benefit of using an ASO against <em>MECP2 </em>in a MDS human neuronal context,. Importantly, we identified a signature of genes that is partially rescued upon ASO treatment and pinpointed genes sensitive to MeCP2 function and in models of Rett syndrome, a neurological disorder caused by loss of MeCP2 function. Furthermore, the signature contained genes that are aberrantly induced in unaffected control human neurons upon MeCP2 depletion, revealing gene expression programs sensitive to MeCP2 levels in human neurons. This repository contains the raw raws, processed reads, and processed RNA-seq analyses (DESeq2, limmavoom) from this study.</span></p> <p><span>MDS_iNeurons_rawcounts_STAR.txt - raw counts from experiment</span></p> <p><span>MDS_iNeurons_sample_information.txt - information describing sample conditions (genotype, ASO treatment, dose, etc).</span></p> <p><span>SupplementalTable1.xlsx - DESeq2 and limmavoom results comparing MDS and unaffected controls; DESeq2 results comparing ASO treatments within a genotype</span></p> <p><span>SupplementalTable2.xlsx - gene lists of genes altered by changes in MECP2 dosage</span></p> <p><span>SupplementalTable3.xlsx - HOMER motif results of gene lists sensitive to MECP2 dosage </span></p>
Foxg1 regulation of translation: RNASeq data
<p>Osvaldo Artimagnella & Antonello Mallamaci. <br><em>Foxg1 regulation of translation: RNASeq data</em></p> <p>It includes raw:<br>(1) Total (totRNA)<br>(2) Translating Ribosome Affinity Purification (trapRNA)<br>(3) RNA ImmunoPrecipitation (ripRNA)<br>sequence data,<br>referred to by:<br>Osvaldo Artimagnella, Elena Sabina Maftei, Mauro Esposito, Remo Sanges, Antonello Mallamaci. "<em>Foxg1 regulates translation of neocortical neuronal genes, including the main NMDA receptor subunit gene, Grin1</em>". (submitted).</p>
Generation of transcriptional novelty by transposable element insertions in Arabidopsis, RNAseq Control Condition Sequencing Data
<p><strong>Arabidopsis stranded 150 bp paired end RNA sequencing data (Illumina) of plants that were grown under control conditions for the manuscript "Generation of transcriptional novelty by transposable element insertions in Arabidopsis"</strong></p> <p><strong><strong>Plant growth conditions</strong></strong></p> <p>Sequenced F4 seeds were sterilized for 10 minutes in 10% bleach, rinsed, and stratified at 4°C for four days in the dark before being sown on 0.5x Murashige & Skoog media (Du<em>schefa cat# M0222</em>) and transferred to growth chambers under long day conditions (16h of light at 24°C followed by 8h of darkness at 21°C; 20 seeds per plate, 6 replicate plates). Ten days after sowing, plants were subjected to 6°C for 24 hours and control plants were returned to normal long day growing conditions for 24 hours before harvesting (3 replicate plates per condition).</p> <p><strong><strong>RNA extraction and sequencing</strong></strong></p> <p>Seedlings were harvested and RNA extractions were done on pools of 5 plants. RNA extractions were performed for 3 biological replicate samples for each line in each condition (n=96) using the Macherey-Nagel NucleoSpin RNA kit (cat# 740955.50). Samples were sent to Novogene for Illumina 150bp paired-end sequencing using a stranded poly-A library.</p> <p><strong>RNAseq sample descriptions of the plants grown under control conditions</strong></p> <p>wt_control: wild-type plants.</p> <p>wtHS_control: wild-type plants that have been submitted to heat stress in a previous generation.</p> <p>wtAZ_control: wild-type plants that have been submitted to epigenetic drug treatments (alpha-amanitin and zebularine) in a previous generation.</p> <p>htLine#: plants carrying additional <em>ONSEN</em> transposable element insertions.</p> <p>Files description: Forward and reverse strand RNA seq data are combined in one file. The numbering at the end ("_1") denominates the biological replicate number.</p>
single-cell RNAseq data (data set 1) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset1) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CRC samples downloaded from the GEO website (<strong>GSE81861). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
RNAseq data: Analysis of circRNA expression in human neuronal differentiation
<p>This dataset contains sequencing read count data related to samples from differentiating human neuroepithelial stem cells (NES) collected at days zero (NES), five (D5) and 28 (D28) of differentiation. Details on how samples were collected and how data was generated and processed are described below.</p> <p> </p> <p><em>Sample preparation</em></p> <p>NES were seeded on tissue culture flasks coated with 20 μg/ml poly-L-ornithine (Sigma-Aldrich P3655), and 1 μg/ml laminin (Sigma-Aldrich L2020). Cells were grown in DMEM/F12+GlutaMAX medium (ThermoFisher 31331093) supplemented with 0.05X B27 (ThermoFisher 17504044), 1X N2 (ThermoFisher 17502001), 10 ng/ml bFGF (fisher scientific CTP0261), 10 ng/ml EGF (PeproTech AF-100-15) and 10 U/ml penicillin/streptomycin (ThermoFisher 15140122). Medium was exchanged 50% daily and cells maintained in 5% CO2 at 37ºC, passaging once 100% confluent and seeding at a density of 5x104 cells/cm2. Neural differentiation was induced by growth factor withdrawal the day after plating with media B27 concentration increased to 0.5X. Media was exchanged 50% every second day up until D15, after which media was supplemented with 0.4 ug/ml laminin and exchanged 50% every three days. </p> <p> </p> <p><em>RNA extraction and sequencing</em></p> <p>Cells were lysed in TRIzol reagent (ThermoFischer 15596026) before separating with chloroform and mixing the aqueous phase with isopropanol as per manufacturer directions. RNA was then isolated from the isopropanol/chloroform solution using the ReliaPrep RNA Cell Miniprep kit (Promega Z6010). Libraries were prepared with Illumina Truseq Stranded total RNA RiboZero GOLD kit and sequenced on the NovaSeq6000 platform with a 2x151 setup using NovaSeqXp workflow in S4 mode flowcell.</p> <p> </p> <p><em>Data generation</em></p> <p>Raw reads were processed using cutadapt v3.2 to trim adaptor sequences and low-quality base pairs and discard short reads (options: -m 20 -e 0.1 -q 20 -O 1). The GRCh37 genome assembly was used for all alignment, annotation, and downstream analysis steps. Trimmed read weres alignment to the GRCh37 genome assembly using TopHat v2.0.9 tophat_fusion (with Bowtie v1.1.2 and Samtools v0.1.19) with –fusion-min-dist 200. BAM files have been anonymised by removal of potentially identifiable genetic variant information using BAMboozle v0.5.0 (Ziegenhain & Sandberg, 2021) with default settings. This BAM files and corresponding index (.bai) files are provided here with naming convention "<em>label.</em>bam" Information on sample labels and corresponding conditions is provided in the file 'metadata.txt'.</p> <p><br> </p>
RNAseq analysis of gene counts and expression levels in diabetic foot ulcers
Open the record for dataset details and reuse information.
RNASeq profiling of Foxg1-GOF neocortical neurons.v2
<p>The dataset uploaded in this entry includes data referred to by the manuscript "<em>Foxg1</em> upregulation enhances neocortical projection neuron activity", by Wendalina Tigani, Moira Pinzan Rossi et al. (submitted).</p> <p> </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.