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Dataset results
137 results for “slam”
Genome-wide in vivo dynamics of cohesin-mediated loop extrusion and its role in transcription activation [SLAM-seq]
GEO Series GSE277898. Homo sapiens. 84 samples. Type: Other.
Effect of performing SLAM-seq chemistry in methanol fixed cells versus standard tube processing on quantification bias in nucleotide conversion RNA-seq data
GEO Series GSE253370. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
GRAND-SLAM analysis of NXF1 knockout cells from Zuckerman et al., Mol Cell 2020 (https://doi.org/10.1016/j.molcel.2020.05.013)
<p>This is the processed SLAM-seq data from Zuckerman et al., Mol Cell 2020 (https://doi.org/10.1016/j.molcel.2020.05.013). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file nxf1.tsv.gz is the GRAND-SLAM output table.</p> <p> </p> <p>To generate the GRAND-SLAM output yourself, first <a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">prepare</a> the human (ensembl v90) genome. Then run:</p> <pre><code class="language-bash">gedi -e Slam -trim5p 15 -reads nxf1.cit -genomic h.ens90 -prefix grandslam_t15/nxf1 -plot -progress </code></pre> <p>To generate the cit file you have to modify the paths in all scripts/* to match the paths on your file system, and then run star.bash.</p> <p>You can also start from scratch (i.e., the json file):</p> <ol> <li><a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">Prepare</a> the human genome (ensembl v90), the human rRNA sequence (U13369.1), and the Mycoplasma hominis sequence</li> <li>Run: gedi -e Pipeline -r parallel -j nxf1.json rnaseq_mapping.sh report.sh grandslam.sh</li> </ol> <p>Software versions:</p> <ul> <li>gedi toolkit 1.0.2</li> <li>GRAND-SLAM 2.0.5g</li> <li>STAR version 2.5.3a</li> </ul>
A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles
<p>Datasets collected from the experiments described in the paper R. Milijas, L. Markovic, A. Ivanovic, F. Petric and S. Bogdan, "A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles," <em>2021 International Conference on Unmanned Aircraft Systems (ICUAS)</em>, 2021, pp. 1148-1154, doi: 10.1109/ICUAS51884.2021.9476802.</p> <p>The datasets consist of ROS bags which hold the UAV and LiDAR data, and of zip files which hold only the lidar data in binary format for non-ROS users.</p>
GRAND-SLAM analysis of acute BANP depletion from Grand et al., Nature 2021 (https://doi.org/10.1038/s41586-021-03689-8)
<p>This is the processed SLAM-seq data from the SLAM-seq data set published in Grand et al., Nature 2021 (https://doi.org/10.1038/s41586-021-03689-8). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file BANP.tsv.gz is the GRAND-SLAM output table.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the mouse (ensembl v90). Then run:</p> <p>gedi -e Slam -trim5p 15 -reads BANP.cit -genomic m.ens90 -prefix grandslam_t15/BANP -plot -D -modelall</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <p> Prepare the mouse genome (ensembl v90) and the murine rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j BANP.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>Software versions:</p> <p> gedi toolkit 1.0.4<br> GRAND-SLAM 2.0.7<br> STAR version 2.5.3a</p>
GRAND-SLAM analysis of simulated and real SLAM-seq datasets for grandRescue
<p>These are several processed datasets for the purpose of investigating 4sU-induced toxicity, mapping impairment and the potential of grandRescue to alleviate these effects.</p> <p>Published dataset: Narain dataset (Narain, et al.): <a href="https://doi.org/10.1016/j.molcel.2021.06.016">https://doi.org/10.1016/j.molcel.2021.06.016</a></p> <p> </p> <p>The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The *.tsv.gz files are the GRAND-SLAM output tables.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the mouse & human genomes. Then run the following command with the respective cit-files, prefixes (*.cit) and genomes (e.g. h.ens90 or m.ens102):</p> <p>gedi -e Slam -trim5p 15 -reads *.cit -genomic m.ens102 -prefix grandslam_t15/* -plot -D -modelall</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <p> Prepare the human/mouse genome and their rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j *.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>Software versions:</p> <p> gedi toolkit 1.0.5<br> GRAND-SLAM 2.0.7<br> STAR version 2.7.10b</p>
The Tolerability of Saracatinib in Subjects With Lymphangioleiomyomatosis (LAM) (SLAM-1)
ClinicalTrials.gov study NCT02116712. IPD Sharing: NO. Countries: 1. Publications: 0.
Time-lapse SLAM-seq for nuclear RNA
GEO Series GSE154657. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
YBX1 is selectively required for maintaining myeloid leukemia cell survival by regulating mRNA stability in an m6A-dependent manner [SLAM-seq]
GEO Series GSE159152. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Effect of TUC-seq protocol versus SLAM-seq on quantification bias in nucleotide conversion RNA-seq data
GEO Series GSE253169. Mus musculus. 10 samples. Type: Other.
Trisomy of a ‘Down syndrome critical region’ globally amplifies transcription via HMGN1 overexpression [SLAM-Seq]
GEO Series GSE121070. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
SLAM-ITseq identifies that Nrf2 induces liver regeneration through the pentose phosphate pathway [Larval SLAM-Itseq]
GEO Series GSE230831. Danio rerio. 27 samples. Type: Expression profiling by high throughput sequencing.
N6-methyladenosine and the NEXT complex direct Xist RNA turnover and X inactivation dynamics [SLAM-Seq]
GEO Series GSE279260. Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing.
SLAM-seq for K562 endogenous mRNA decay
GEO Series GSE126522. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
SLAM-seq defines direct gene-regulatory functions of the BRD4-MYC axis [SLAM-seq]
GEO Series GSE100708. Homo sapiens. 104 samples. Type: Expression profiling by high throughput sequencing.
Prolonged Kaposi's Sarcoma-associated Herpesvirus vIL-6 Exposure Enhances Inflammatory Responses by Epigenetic Reprogramming (SLAM-seq)
GEO Series GSE233119. Homo sapiens. 33 samples. Type: Expression profiling by high throughput sequencing.
Nacent transcription profiling of cell-cycle synchronised U2OS cells using SLAM-seq
GEO Series GSE196277. Homo sapiens. 15 samples. Type: Other.
Inhibition of topoisomerase 1 and BRD4-mediated pause release selectively kills tumours by inducing readthrough transcription [SLAM-seq]
GEO Series GSE211469. Homo sapiens. 12 samples. Type: Other.
Slam-seq reveals that miR-430 regulates zygotic mRNA during zebrafish embryogenesis [titration_data]
GEO Series GSE247934. Danio rerio. 15 samples. Type: Expression profiling by high throughput sequencing; Other.
mRNA export factors store nascent transcripts within nuclear speckles as an adaptive response to transient global inhibition of transcription [SLAM-seq]
GEO Series GSE284174. Homo sapiens. 18 samples. Type: Expression 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
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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.