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273 results for “cerebral cortex”
The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons
<p>This project contains the annotated transcriptomes in GTF format used in the manuscript "The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons" DOI: <a href="https://doi.org/10.1093/molbev/msae123">https://doi.org/10.1093/molbev/msae123</a></p>
Data associated with "Modulation of intercolumnar synchronization by endogenous electric fields in cerebral cortex"
<p>These is data associated with the article "Modulation of intercolumnar synchronization by endogenous electric fields in cerebral cortex" in Science Advances 2021, by the same authors.</p>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
PANDA cell-type-specific networks for S858R and WT mouse cerebral cortex
<p>The below files are from the data/results directory of this associated project and include the following:</p> <ul> <li><strong>PANDA networks </strong>: cell-type-specific TF-gene regulatory networks constructed using multi-omic inputs (snRNA-seq, TF-motif, and PPI) for all cell types in S858R and WT mouse cerebral cortex tissues (n = 16). All the files included for PANDA networks indicate the condition (heterozygous or control), tissue (cerebral cortex) followed by _PANDA.Rdata. (example: astrocytes_heterozygouscortexexpression_PANDA.Rdata)</li> </ul>
Dataset and Software code _ Localized and global representation of prior value, sensory evidence, and choice in male mouse cerebral cortex
Open the record for dataset details and reuse information.
Data from Schmitt et al. 2020: A causal role of area hMST for self-motion perception in humans. Cerebral Cortex Communications
<p><strong>Dataset associated with the following publication:</strong></p> <p>Constanze Schmitt, Bianca R Baltaretu, J Douglas Crawford, Frank Bremmer, A Causal Role of Area hMST for Self-Motion Perception in Humans, <em>Cerebral Cortex Communications</em>, Volume 1, Issue 1, 2020, tgaa042, <a href="https://doi.org/10.1093/texcom/tgaa042">https://doi.org/10.1093/texcom/tgaa042</a> Published 2020 July 20.</p> <p><strong>Description of dataset:</strong></p> <p>In our study we presented an optic flow stimulus simulating forward self-motion across a ground plane in one of three directions (30° to the left, straight ahead, 30° to the right) to eight human participants. In 57% of all trials TMS pulses were applied either to right hemisphere hMST or a control area while the optic flow stimulus was presented. Participants indicated the perceived heading of the self-motion stimulus.</p> <p>The dataset contains the responses (perceived headings) of all eight participants (s1 to s8). The responses are reported here in degrees with 0 degrees representing a movement straight ahead, negative values describing movements forward to the left and positive values describing movements forward to the right.</p> <p> </p>
Dyspnea and Cerebral Cortex Activation Measured by fNIRS During Spontaneous Breathing Trial
ClinicalTrials.gov study NCT06211738. IPD Sharing: YES. Countries: 1. Publications: 0.
Maternal Exercise during Pregnancy Increases BDNF Levels and Cell Numbers in the Hippocampal Formation but Not in the Cerebral Cortex of Adult Rat Offspring
Open the record for dataset details and reuse information.
DNase-seq from right cerebral cortex (ENCSR293BBH)
GEO Series GSE215637. Mus musculus. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
TF ChIP-seq from left cerebral cortex (ENCSR644VYX)
GEO Series GSE231067. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
DNase-seq from left cerebral cortex (ENCSR098GIZ)
GEO Series GSE215559. Mus musculus. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Gene expression profile at single cell level of the cerebral cortex at different sleep need
GEO Series GSE214337. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
microRNA-seq from left cerebral cortex (ENCSR560FAG)
GEO Series GSE219390. Mus musculus. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
snRNA-seq from left cerebral cortex (ENCSR421YXX)
GEO Series GSE286707. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
snRNA-seq from left cerebral cortex (ENCSR467GGT)
GEO Series GSE286725. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
snRNA-seq from left cerebral cortex (ENCSR624WDN)
GEO Series GSE286764. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
total RNA-seq from left cerebral cortex (ENCSR946YUY)
GEO Series GSE188154. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
DNase-seq from left cerebral cortex (ENCSR200TAZ)
GEO Series GSE215468. Mus musculus. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
DNase-seq from right cerebral cortex (ENCSR551ZTT)
GEO Series GSE215682. Mus musculus. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
DNase-seq from right cerebral cortex (ENCSR017TAB)
GEO Series GSE215475. Mus musculus. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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.