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2,489 results for “Sars-CoV-2”
Data from: Compromise docking power evaluation of liganded crystal structures of Mpro SARS-CoV-2
<p>A set of 406 liganded SARS-CoV-2 M<sup>pro</sup> crystal structures originally downloaded from RCSB PBD database is provided. Ligand and protein files are processed and corrected for various types of structural errors and are provided in pdbqt and mol2 formats for immediate use in molecular docking programs AutoDock, AutoDock Vina, and PLANTS. Data are utilized in calculations of newly defined compromise docking power to monitor the performance of above-mentioned software. The provided dataset can also be used for benchmarking of other software and molecular docking protocols on liganded SARS-CoV-2 M<sup>pro</sup> systems.</p>
Input parameters and output trajectory files for manuscript "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CLPro: Insights from the Computational and Experimental Investigations"
<p>Input parameters used for molecular dynamics simulations on GROMACS 2022 software and the output trajectory files for calculating binding free energy, in the manuscript with the title: "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CL<sup>Pro</sup>: Insights from the Computational and Experimental Investigations"</p>
Mapping SARS-CoV-2 antigenic relationships and serological responses
<p>During the SARS-CoV-2 pandemic, multiple variants escaping pre-existing immunity emerged, causing concerns about continued protection. Here, we use antigenic cartography to analyze patterns of cross-reactivity among a panel of 21 variants and 15 groups of human sera obtained following primary infection with 10 different variants or after mRNA-1273 or mRNA-1273.351 vaccination. We find antigenic differences among pre-Omicron variants caused by substitutions at spike protein positions 417, 452, 484, and 501. Quantifying changes in response breadth over time and with additional vaccine doses, our results show the largest increase between 4 weeks and >3 months post-2nd dose. We find changes in immunodominance of different spike regions depending on the variant an individual was first exposed to, with implications for variant risk assessment and vaccine strain selection.</p>
Generative AI in the Advancement of Viral Therapeutics for Predicting and Targeting Immune-Evasive SARS-CoV-2 Mutations
<p>This dataset <strong>encompasses</strong> and describes the following features:</p> <ul> <li>Mutations in viruses like SARS-CoV-2 can make them escape vaccines and treatments.</li> <li>Accurately predicting these mutations is crucial for developing effective countermeasures.</li> <li>The study uses a type of AI called a Generative Adversarial Network (GAN) to analyze the virus's spike protein, which plays a key role in infection.</li> <li>The GAN generates protein sequences similar to natural ones, but which are also likely to evade immune responses.</li> <li>By analyzing these generated sequences, the researchers improve their AI model's ability to predict real-world escape mutations.</li> <li>This improved prediction could help design better vaccines and treatments, and prepare for future viral threats.</li> </ul>
UnCoVar: Benchmarking dataset for SARS-CoV-2 sequence processing software pipelines, Sanger sequences
Open the record for dataset details and reuse information.
Membrane mesh and tomogram with SARS-CoV-2 intact virions
<p>Membrane mesh and tomogram with SARS-CoV-2 intact virions. These data were originally published in <a href="http://dx.doi.org/10.1038/s41586-020-2665-2">Ke et al., Nature, 2020</a>. The tomograms were accessed from the <a href="https://cryoetdataportal.czscience.com/runs/467?prev=%2Fdatasets%2F10006%3Fprev%3D%252Fbrowse-data%252Fdatasets">Cryo-ET Data Portal</a>. Raw data is available from <a href="https://doi.org/10.6019/EMPIAR-10493">EMPIAR-10493</a>. The membrane segmentation was created using <a href="https://doi.org/10.1101/2024.01.05.574336">MemBrain-seg</a>.</p>
PanDDA analysis of fragment screen against the Nsp3 macrodomain of SARS-CoV-2 - P43 crystals at UCSF
<p>This deposition contains the X-ray diffraction data used for the PanDDA analysis of the fragment screen against the NSP3 macrodomain of SARS-CoV-2 described in Schuller et al. 2021 (DOI: 10.1126/sciadv.abf8711).</p> <p>A description of the files can be found in the "README" text file. </p> <p>The data in this deposition is from the fragment screen performed at UCSF using P43 crystals. The data from the fragment screen performed at UCSF using C2 crystals can be found here - https://zenodo.org/record/4716363 - in the zipped directory named "ucsf_nsp3_mac1_C2.zip". </p>
Dataset (I.) related to publication: PIP4K2C inhibition reverses autophagic flux arrest induced by SARS-CoV-2
<p>MD simulation data (PIKfyve simulations) related to publication: </p> <p>Karim, M., Mishra, M., Lo, CW. <em>et al.</em> PIP4K2C inhibition reverses autophagic flux impairment induced by SARS-CoV-2. <em>Nat Commun</em> <strong>16</strong>, 6397 (2025). https://doi.org/10.1038/s41467-025-61759-1</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of PIKfyve in complex with RMC-113 [18 replicas; each 4 us] (-out.cms files and trajectories).</li> </ul> <p> </p>
Tracking SARS-CoV-2 variants in wastewater in San Pedro de la Paz, Chile
<p>Various studies have shown the presence of SARS-CoV-2 RNA in the feces of patients<br>with COVID-19, both symptomatic and asymptomatic. This allowed determining the<br>viral load in wastewater samples from Wastewater Treatment Plants (WWTPs),<br>carrying out wastewater-based surveillance (WBS) of the virus in the community, as a<br>complement to person-to-person testing. The appearance of SARS-CoV-2 variants,<br>which can increase transmissibility and/or immune evasion, creates an imperative<br>need to implement specific and permanent surveillance methods to control the COVID19 pandemic. For variant detection, we performed a real-time RT-qPCR assay with a<br>commercial kit to detect five virus variants (Alpha, Beta, Gamma, Lambda, and Delta)<br>in the municipality of San Pedro de la Paz, Chile, from January to November 2021.<br>Detection of variants in wastewater was consistent with available clinical data and<br>provided additional information for community surveillance, identifying lambda and<br>delta variants as the most frequently detected during the second and third wave of<br>infections in the population of this area. Furthermore, in some cases we detected<br>specific variants in wastewater before local authorities confirmed the first clinical cases.<br>The study demonstrates that WBS is a tool that allows a rapid and cost-effective<br>detection of specific mutations associated with SARS-CoV-2 variants using RT-qPCR.<br>However, Illumina amplicon sequencing confirms that there are more optimal methods<br>to sequence this type of matrices. This method can be used to complement clinical<br>data during outbreaks and is especially useful when clinical care is insufficient or<br>collapsed and/or cost is very high, as is the case in many countries.</p>
Seroprevalence survey on infection with the SARS-CoV-2 virus after the second wave in Kinshasa, Democratic Republic of the Congo. 2021
<p>Results of population-based age stratified seroepidemiological investigation in the Democratic Republic of the Congo</p>
AMTraC-19 (v7.7d) Dataset: Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia
<p>A preprint paper describing scenarios which generated this dataset can be accessed here: https://arxiv.org/abs/2107.06617. Please cite this work when using the dataset:<br> S. L. Chang, C. Zachreson, O. M. Cliff, M. Prokopenko, Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia, arXiv: 2107.06617, 2021.</p> <p>Abstract. An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., "social distancing"). Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model successfully predicted that, under a progressive vaccination rollout, if 40-50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021. We also quantified an expected burden on the healthcare system and potential fatalities across Australia.</p> <p>The AMTraC-19 source code (v7.7d) is released on Zenodo: https://zenodo.org/record/5778218</p>
Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California
<p>This repository contains the pertinent datasets used in the manuscript, <em>Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California</em>.</p>
ACE2-IgG1 fusions with improved in vitro and in vivo activity against SARS-CoV-2
<p>These are raw data for figures in ACE2-IgG1 fusions with improved in vitro and in vivo activity against SARS-CoV-2, in iScience: PMID: 34957381</p>
GRAND-SLAM analysis of SARS-CoV-2 data from Finkel et al., Nature 2021 (https://www.nature.com/articles/s41586-021-03610-3)
<p>This is the processed SLAM-seq data from Finkel et al., Nature 2021 (https://www.nature.com/articles/s41586-021-03610-3). 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 sars.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) and the SARS-CoV-2 genome (NC_045512). Then run:</p> <pre><code class="language-bash">gedi -e Slam -trim5p 15 -reads sars.cit -genomic h.ens90 SARS-CoV2 -prefix grandslam_t15/sars -plot -progress </code></pre> <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> <ol> <li><a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">Prepare</a> the human genome (ensembl v90), the SARS-CoV-2 genome (NC_045512), the human rRNA sequence (U13369.1), and the Mycoplasma hominis sequence</li> <li>Prepare the joint STAR index for the human and virus genome by calling gedi -e GenomicUtils -p -m star -g h.ens90 SARS-CoV2</li> <li>Modify the starindex entry in the json file to match your file system</li> <li>Run: gedi -e Pipeline -r parallel -j sars.json rnaseq_mapping.sh report.sh grandslam.sh</li> </ol> <p>Software versions:</p> <ul> <li>gedi toolkit 1.0.4</li> <li>GRAND-SLAM 2.0.7</li> <li>cutadapt 3.4</li> <li>Bowtie 2 version 2.3.0</li> <li>STAR version 2.5.3a</li> </ul>
Imputed Multiple Sequence Alignment used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'
<p>Multiple Sequence Alignment of imputed SARS-CoV-2 sequences used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'</p>
Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection
<p><strong>scRNA-seq data</strong></p> <p>Data were processed using cellranger v 4.0.0 with the refdata-gex-GRCh38-2020-A reference.</p> <p><em>h5files.zip</em>: contains all h5-Files of raw feature-barcode counts (e.g. 20094_0001_A_B_raw_feature_bc_matrix.new.h5 )</p> <p><em>raw_feature_bc_matrices.zip</em>: contains the <em>same data</em> as h5files.zip, but also in mtx-format.</p> <p>covid_object_ncomms<em>.RDS</em>: contains the Seurat file with which all analyses were conducted.</p> <p><em>samples2condition.df</em>: text file containing sample to condition information</p> <p><strong>Bulk RNA-seq</strong></p> <p><em>covid_bulk.zip</em> contains the count matrices extracted from the zUMIs runs for the bulk cohort.</p> <p><em>nasal_swabs.zip</em> contains the count matrices extracted from the zUMIs run for the nasal swab cohort.</p> <p>The extracted count matrices were then used with the bulk analysis scripts provided with the source code.</p> <p><strong>Source Code</strong></p> <p>All <strong>source code</strong> for the publication is available from: <a href="https://github.com/mjoppich/covidSC">https://github.com/mjoppich/covidSC</a> or from tagged releases: <a href="https://github.com/mjoppich/covidSC/releases/tag/ncomms">https://github.com/mjoppich/covidSC/releases/tag/ncomms</a></p> <p>When using any of these data, please cite:<br> <br> Pekayvaz et al., Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection, Nature Communications 2022</p>
Raw diffraction data for structure of SARS-CoV-2 main protease with Z31792168 (PDB: 7QT5)
<p>Raw diffraction data for SARS-CoV-2 main protease in complex with Z31792168 collected as part of an room-temperature crystallographic ligand screening experiments on beamline i24 at Diamond Light Source.</p>
Raw diffraction data for structure of SARS-CoV-2 main protease with Z4439011520 (PDB: 7QT7)
<p>Raw diffraction data for SARS-CoV-2 main protease in complex with Z4439011520 collected as part of an room-temperature crystallographic ligand screening experiments on beamline i24 at Diamond Light Source.</p>
Raw diffraction data for structure of SARS-CoV-2 main protease with Z1367324110 (PDB: 7QT6)
<p>Raw diffraction data for SARS-CoV-2 main protease in complex with Z1367324110 (SMILES:CN1CCCC=2C=CC(=CC12)S(=O)(=O)N) collected as part of an room-temperature crystallographic ligand screening experiments on beamline i24 at Diamond Light Source.</p>
Raw diffraction data for structure of SARS-CoV-2 main protease with Z4439011584 (PDB: 7QT9)
<p>Raw diffraction data for SARS-CoV-2 main protease in complex with Z4439011584 collected as part of an room-temperature crystallographic ligand screening experiments on beamline i24 at Diamond Light Source.</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.