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122 results for “3d genome organization”
Loss of multi-level 3D genome organization during breast cancer progression - Processed LAD files
<p>This entry contains the processed LAD files produced as part of the following study:<br><strong>Loss of multi-level 3D genome organization during breast cancer progression</strong></p>
Loss of multi-level 3D genome organization during breast cancer progression - Third-party datasets
<p>This entry contains the following datasets:</p> <p>Datasets used by <a href="https://github.com/dixonlab/hic_breakfinder" target="_blank" rel="noopener">hic_breakfinder</a>:</p> <ul> <li>inter_expect_1Mb.hg38.txt</li> <li>intra_expect_100kb.hg38.txt</li> </ul> <p>FIles were originally downloaded from <a href="https://salkinstitute.box.com/s/m8oyv2ypf8o3kcdsybzcmrpg032xnrgx" target="_blank" rel="noopener">this</a> URL.</p> <p>Datasets used by <a href="https://github.com/parklab/HiNT" target="_blank" rel="noopener">HiNT</a>:</p> <ul> <li>backgroundMatrices_hg38.zip</li> <li>refData_hg38.zip</li> </ul> <p>Files were originally downloaded from the following URLs: <a href="http://compbio.med.harvard.edu/hint/refData/" target="_blank" rel="noopener">link1</a>, <a href="http://compbio.med.harvard.edu/hint/backgroundMatrices/" target="_blank" rel="noopener">link2</a>.</p> <p>The above datasets are used by the data analysis workflows hosted at <a href="https://github.com/paulsengroup/2022-mcf10a-cancer-progression" target="_blank" rel="noopener">paulsengroup/2022-mcf10a-cancer-progression.</a><br>The results produced by running the workflows from the above repository were used as part of the following study:<br><strong>Loss of multi-level 3D genome organization during breast cancer progression</strong></p>
Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin
<p>We prepared these datasets associated with the paper “Genome-scale imaging of the 3D organization and transcriptional activity of chromatin” published in Cell: <a href="https://doi.org/10.1016/j.cell.2020.07.032">https://doi.org/10.1016/j.cell.2020.07.032</a>.</p> <p>Please find detailed descriptions of individual data files in the README_August 2020.txt.</p> <p>We provide example codes to load and analyze these datasets in: <a href="https://github.com/ZhuangLab/Chromatin_Analysis_2020_cell">https://github.com/ZhuangLab/Chromatin_Analysis_2020_cell</a>.</p> <p>If you use these datasets, please cite our Cell paper.</p>
Loss of multi-level 3D genome organization during breast cancer progression - FISH dataset
<p>This entry contains the raw and processed FISH images produced by the following study:<br><strong>Loss of multi-level 3D genome organization during breast cancer progression</strong></p> <p>The raw images contained in file 2022-mcf10a-cancer-progression-fish-db.tar.gz were processed using fish data analysis workflow (<a href="https://github.com/paulsengroup/2022-mcf10a-cancer-progression/blob/main/run_fish.sh" target="_blank" rel="noopener">link</a>) hosted at <a href="https://github.com/paulsengroup/2022-mcf10a-cancer-progression" target="_blank" rel="noopener">paulsengroup/2022-mcf10a-cancer-progression</a>.<br>The resulting files have been archived in file 2022-mcf10a-cancer-progression-fish-processed-data.tar.gz.</p>
Data from: Comparative analysis of 2D and 3D distance measurements to study spatial genome organization
The spatial organization of genomes is non-random, cell-type specific, and has been linked to cellular function. The investigation of spatial organization has traditionally relied extensively on fluorescence microscopy. The validity of the imaging methods used to probe spatial genome organization often depends on the accuracy and precision of distance measurements. Imaging-based measurements may either use 2 dimensional datasets or 3D datasets which include the z-axis information in image stacks. Here we compare the suitability of 2D vs 3D distance measurements in the analysis of various features of spatial genome organization. We find in general good agreement between 2D and 3D analysis with higher convergence of measurements as the interrogated distance increases, especially in flat cells. Overall, 3D distance measurements are more accurate than 2D distances, but are also more susceptible to noise. In particular, z-stacks are prone to error due to imaging properties such as limited resolution along the z-axis and optical aberrations, and we also find significant deviations from unimodal distance distributions caused by low sampling frequency in z. These deviations are ameliorated by significantly higher sampling frequency in the z-direction. We conclude that 2D distances are preferred for comparative analyses between cells, but 3D distances are preferred when comparing to theoretical models in large samples of cells. In general and for practical purposes, 2D distance measurements are preferable for many applications of analysis of spatial genome organization.
Data from: Comparative analysis of 2D and 3D distance measurements to study spatial genome organization
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SATB2 organizes the 3D genome architecture of cognition in cortical neurons [ATAC-seq]
GEO Series GSE222607. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
3D genome organization coordinates key regulators of lineage specification in mammary epithelial cells [T8_Omni-C]
GEO Series GSE228954. Mus musculus. 6 samples. Type: Other.
Elementary 3D organization of active and silenced E. coli genome
GEO Series GSE272160. Escherichia coli. 4 samples. Type: Other.
CTCF chromatin residence time controls 3D genome organization, gene expression and DNA methylation in pluripotent cells
GEO Series GSE154009. Mus musculus. 38 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Suppression of liquid-liquid phase separation by 1,6-hexanediol partially compromises the 3D genome organization in living cells
GEO Series GSE138543. Homo sapiens. 22 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Comparing 3D genome organization in multiple species using Phylo-HMRF
GEO Series GSE128800. Pan troglodytes; Gorilla gorilla; Pan paniscus. 3 samples. Type: Other.
3D genome organization coordinates key regulators of lineage specification in mammary epithelial cells [RNASeq_MEC]
GEO Series GSE227748. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
SATB2 organizes the 3D genome architecture of cognition in cortical neurons [Hi-C]
GEO Series GSE222606. Mus musculus. 12 samples. Type: Other.
3D genome organization coordinates key regulators of lineage specification in mammary epithelial cells [MEC_Omni-C]
GEO Series GSE228930. Mus musculus. 6 samples. Type: Other.
Systematic analysis of circadian gene expression mediated by epigenetic modification and 3D genome organization in Rice [RNA-Seq]
GEO Series GSE213093. Oryza sativa Indica Group. 36 samples. Type: Expression profiling by high throughput sequencing.
SATB2 organizes the 3D genome architecture of cognition in cortical neurons [CUT&RUN]
GEO Series GSE222608. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Systematic analysis of circadian gene expression mediated by epigenetic modification and 3D genome organization in Rice
GEO Series GSE213227. Oryza sativa Indica Group. 89 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Linkages between 3D genome organization and transcriptome during muscle cell development
GEO Series GSE84279. Mus musculus. 6 samples. Type: Other.
Deciphering the role of 3D genome organization in breast cancer susceptibility (RNA-Seq)
GEO Series GSE184284. Rattus norvegicus. 13 samples. Type: Expression profiling by high throughput sequencing.
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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.