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2,489 results for “SARS-CoV-2”
SARS-CoV-2 genomics resources for Galaxy
<p>Reference and custom annotation data expected as input by Galaxy SARS-CoV-2 variation analysis workflows developed by covid19.galaxyproject.org</p>
Diet and SARS-Cov-2 Infection Risk: A Retrospective Observational Study
<p>Dataset, Analysis, Regression and Description.</p> <p>From mid-summer 2020 to January 2022, based on phase 2 to 3 of a self-reported questionnaire survey, we asked 15851 families across Iran about their diet and their COVID-19 disease. The results showed that some diets increased the risk of SARS-Cov-2 Apparent Infection Risk and some reduced it.</p> <p>The results show that the risk of reporting SARS-CoV-2 apparent infection in the second group was 12 times higher than the Third group. <strong>The two-tailed P value is less than 0.0001</strong>. Also, the risk of reporting SARS-CoV-2 apparent infection in the first group was 9 times higher than the Third group. <strong>The two-tailed P value is less than 0.0001</strong>. By conventional criteria, these differences are considered to be extremely statistically significant.</p>
PSSH2 - database of protein sequence-to-structure homologies (including Sars-CoV-2 structures)
<p><strong>Protein sequence and structure data</strong></p> <p>This data set contains data from Uniprot (in the files called protein_sequence, protein_synonyms, protein_names, organism_synonyms) and PDB (in the files called PDB and PDB_chain) as used by the <a href="https://github.com/ODonoghueLab/Aquaria">Aquaria web resource</a> at the time of download (2022-02-08).</p> <p> </p> <p><strong>The PSSH2 data set</strong><br> <br> PSSH2 is a database of protein sequence-to-structure homologies based on HHblits, an alignment method employing iterative comparisons of hidden Markov models (HMMs). To ensure the highest possible final alignment quality for matches in Aquaria using HHblits, we first calculate HMM profiles for each unique PDB sequence (PDB_full) and also for each unique Swiss-Prot sequence. We generated PSSH2 using HHblits to find similarities between HMMs from PDB and HMMs from UniProt sequences.</p> <p> </p> <p><strong>Calculating PSSH2</strong></p> <p>The Swissprot and PDB data was downloaded in November 2021.<br> Generating PSSH2: We used <a href="https://gwdu111.gwdg.de/~compbiol/uniclust/2021_03/UniRef30_2021_03.tar.gz">UniRef30_2021_03</a> (originally called UniRef30_2021_06) from HH-suite, a database of non-redundant UniProt sequence clusters in which the highest pairwise sequence identity between clusters was 30%. The HHblits code and the code for running the calculations was retrieved from git (https://github.com/soedinglab/hh-suite.git and https://github.com/aschafu/PSSH2.git respectively) at the respective time of calculation in the timeframe until December 2021. <br> </p> <p><strong>PDB based sequence-to-structure alignments</strong></p> <p>In addition to the PSSH2 data, new PDB structures were retrieved based on the primary accession of the proteins, by querying for all chains in all PDB entries with exact matches using the sequence cross references records given in PDB. Sequence-to-structure alignments were then created, again based on information provided in each PDB entry. These are contained in the PDBchain data.</p> <p>This data covers sequences and PDB structures in the timeframe until February 2022. </p> <p> </p> <p><strong>Evaluating PSSH2</strong></p> <p>The resulting alignment data was analysed using CATH domain assignments downloaded from /cath/releases/all-releases/v4_2_0/cath-classification-data/ to define correct hits and false hits: </p> <ul> <li>The set of query sequences is defined by the CATH non-redundant S40_overlap_60 dataset (ftp://orengoftp.biochem.ucl.ac.uk/cath/releases/all-releases/v4_2_0/non-redundant-data-sets/)</li> <li>The set of all expected hits are all pdb structures containing a domain with the same CATH code if contained in the set of processed sequences (-> all) or only if also contained in the set of non redundant sequences (-> nr40).</li> <li>The set of true positives is defined by sharing the same CATH code up to the level of homology ("CATH") or up to the level of topology ("CAT").</li> </ul> <p>The data was evaluated with respect to false discovery rate (FDR) and recall (true positive rate TPR) by cumulatively considering all hits with an E-value below the threshold ("C") or in bins with an E-value between the threshold and one tenth of the threshold ("B"). This evaluation was carried out for the data obtained in November 2021 (202111) as well as previous data from October 2020 (202010), February 2020 (202002) and September 2017 (201709). The results are collected in <a href="https://zenodo.org/api/files/445add84-fcf1-4dfe-b8a1-63dc55f378ee/PSSH%20CATH%20validation.csv?versionId=a5df7473-6efd-442b-b422-3944e9452003">PSSH CATH validation.csv</a>. </p> <p> </p> <p><strong>Known errors</strong></p> <p>Due to processing error, the profile of pdb structure 5fia A / B (sequence md5 052667679fc644184f40063c7602c9e1) is incomplete in the pdb_full hhblits database which led to further errors in generating sequence based alignments for sequences for 1vtm P (sequence md5 c844aff103449363cb8489c78c58ebf1) and 434t A / B (sequence md5 d67aa1c3a36492c719cb48b5e7ecc624).<br> <br> </p>
SARS-CoV-2 RNA levels in Scotland's wastewater
<p>Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference data set produced by this national programme, from May 2020 to February 2022.</p> <p>Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 122 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs.</p> <p>These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK.</p> <p>We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. Check the README for details and the project <a href="https://biordm.github.io/COVID-Wastewater-Scotland/">COVID-WW Website</a></p>
Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2
<p>Single-cell extracted imaging features created in project "Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2". The dataset includes train (with annotations) and test features used in the manuscript. Four SARS-CoV-2 antigens (S, N, R, M) were imaged separately with serum samples presenting IgG, IgA and IgM antibodies.</p>
Zellige example dataset: primary culture of human bronchial cells infected by SARS-CoV-2
<p>A human bronchial epithelium was infected by SARS-CoV-2. The specimen was imaged four days post-infection. The z-stack image encompasses the very irregular epithelium surface. It was acquired with a point scanning microscope (Zeiss LSM700) equipped with Zeiss Plan-Apochromat 63x lens (NA=1.4). Pixel size 0.110µm, z step 0.4µm. This dataset contains both the ground-truth height map and the height map generated with Zellige. The Zellige parameters used are: <span class="math-tex">\(T_{A}=23, T_{otsu}=16, S_{min}=5, \sigma_{xy}=4, \sigma_{z}=1, T_{OSE1}=0.9, R_{1}=5, C_{1}=0.9, T_{OSE2}=0.1, R_{2}=5, C_{2}=0.8.\)</span>.</p> <p>Nota: to compare the ground truth height map with the Zellige height map, one first needs to substrat 1 to all values of the Zellige height map.</p> <p>See the related paper:<br> <a href="https://hal-pasteur.archives-ouvertes.fr/pasteur-03319522">https://hal-pasteur.archives-ouvertes.fr/pasteur-03319522</a></p> <p>See the accompanying paper: Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. Trébeau <em>et al.</em> 2022: <a href="https://doi.org/10.1101/2022.04.05.485876">https://doi.org/10.1101/2022.04.05.485876</a></p> <p> </p>
data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation
<p>This is supporting data and software code for the following preprint in bioRxiv</p> <p><strong>Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation</strong></p> <p>https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1</p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
Identifying and profiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with Machaon - Pre-computed features for replication
<p>Machaon's computed features that were used in the structural comparisons with Spike protein.</p> <p>DATA_PDBS_vir_whole_1-3.zip files are parts of a single folder.</p> <p> </p>
Host removal database: Homo sapiens, Sars-Cov-2, PhiX174
<p>💾 <strong>cleanup-db</strong></p> <p>Kraken2 database, built upon a viral sequence masked human reference from:</p> <ul> <li>Handley, Scott A. (2020). <strong>Virus+ Sequence Masked Human Reference Genome (hg19)</strong> (1.0) [Data set]. Zenodo. [<a href="https://zenodo.org/record/4116107">10.5281/zenodo.4116107</a>]</li> </ul> <p>but separating chromosomes as artificial taxa to allow for QC, and includes Sars-Cov-2 and PhiX 174</p> <p>💾 <strong>gutcheck-db</strong></p> <p>A very small DB containg some common gut bacteria and Human and Murine mitochondrial genome:</p> <ul> <li><em>Akkermansia muciniphila</em></li> <li><em>Bacteroides fragilis</em></li> <li><em>Bifidobacterium longum</em></li> <li><em>Blautia obeum strain</em></li> <li><em>Escherichia coli</em></li> <li><em>Enterococcus faecium</em></li> <li><em>Prevotella copri</em></li> </ul> <p> </p> <p>See: <a href="https://github.com/telatin/cleanup">https://github.com/telatin/cleanup</a></p>
Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
<p>Coswara is a dataset containing diverse set of respiratory sounds and rich meta-data from COVID-19 positive and Non-COVID subjects.</p>
SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses
<p>These are the<strong> processed</strong> BCR repertoire bulk sequencing data described in <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (Fig 3b-d; Extended Data Fig 3; Extended Data Table 6). The corresponding <strong>raw</strong> sequencing reads are available on SRA under <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">BioProject PRJNA731610</a>.</p> <p><strong>Summary</strong>: Bulk-sorted total plasmablasts from PBMCs and germinal centre B cells at 4 weeks after primary immunization from 3 vaccinees who had no prior history of infection with SARS-CoV-2. </p> <p><strong>Code: </strong>Code along with Docker container for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2021">found on GitHub</a>.</p> <p><strong>Metadata file</strong>: WU368_turner_et_al_nature_2021_meta.tsv</p> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>mAb = monoclonal antibody</li> </ul> <p><strong>BCR data file</strong>: WU368_turner_et_al_nature_2021_bcr.tsv.gz</p> <p>In addition to the processed bulk sequences, also included are the heavy chains of 37 mAbs that had been validated to be spike-binding and that were used together with the bulk sequences for clonal lineage inference. The mAbs are annotated as "mab" in the "seq_type" column.</p> <p><strong>BCR data column descriptions</strong></p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s are used, as opposed to IMGT-defined "junctions". Non-standard columns are noted below.</p> <ul> <li>v_call_genotyped: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a></li> <li>germline_[vdj]_call: clonal consensus germline sequence reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` using Change-O</a></li> <li>isotype: IGH[ADEGM]</li> <li>cdr3: CDR3 nucleotide sequence</li> <li>cdr3_length: CDR3 nucleotide sequence length</li> <li>cdr3_aa: CDR3 amino acid sequence</li> <li>collapse_count: number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a></li> <li>donor: vaccinee</li> <li>sample: sample ID (arbitrary)</li> <li>timepoint: time point at which sample was collected</li> <li>tissue: tissue from which sample was collected</li> <li>sorting: FACS sorting</li> <li>seq_type: sequence type (mAb or bulk)</li> <li>nuc_RS_19_312: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a></li> <li>nuc_denom_19_312: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".")</li> <li>nuc_RS_freq_19_312: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312)</li> </ul>
Molecular Dynamics Simulation of SARS-CoV-2 Spike Protein
<p>Trajectory data corresponding to the manuscript, tentatively titled "Distant Residues Modulate the Conformational Opening in SARS-CoV-2 Spike Protein"</p> <p>Authors: Dhiman Ray, Ly Le, Ioan Andricioaei</p> <p>Affiliation: University of California Irvine, USA</p> <p>Description: Multiple unbiased simulations of 40 ns were performed for the SARS-CoV-2 spike protein. Frames are saved at 50 ps interval. The initial structures were generated from umbrella sampling simulation starting from PDB ID: 6VSB and 6VXX. The index at the end of filename stands for the umbrella sampling window from which the trajectory was initiated. The indices are not continuous as not all the umbrella sampling windows were used to start trajectories. Additionally 3 trajectories, each of length 80 ns, are included for the closed, partially open and fully open state. The topology is provided as a PDB file ("spike_dry.pdb").</p> <p>The trajectories are for the spike head only structure obtained from the CHARMM-GUI Covid-19 archive. No solvent or ions are included in the trajectory or the topology.</p> <p>Update: Additional trajectories and PDB files for D614G mutant added. Each trajectory is 40 ns long. The PDB files are named 6VXX_mutant_dry.pdb and 6VSB_mutant_dry.pdb for the closed and partially open state.</p> <p>Pre-print available: https://doi.org/10.1101/2020.12.07.415596</p>
MD simulations of SARS-CoV-2 Spike Protein under static electric fields
<p>This dataset contains trajectories corresponding to all-atom MD simulations of segments of the SARS-CoV-2 Spike Protein, and in-silico mutations, under the influence of moderate external electric fields. The final structures of some of the simulations were used to perform in-silico docking with ACE2 receptor to evaluate the effect of comformational changes (docking was perform with PyDOCK).</p> <p>The file trajectories_6vsb_dt1ns.zip contains trajectories of simulations that were performed on a segment of the Protein Data Bank ID 6VSB comprising RBD, SD1 and SD2. The file trajectories_6m0j_dt1ns.zip correspond to the RBD in Protein Data Bank ID 6M0J. The file trajectories_in-silico_mutations_dt1ns.zip correspond to simulations performed on in-silico generated mutations following the mutations corresponding to WHO Variants of Concern UK, South Africa and Brazil. In all cases, simulations were performed at different electric field intensities ranging between 10<sup>4</sup> V/m and 10<sup>7</sup> V/m, with an extra short simulation under very high intensity (10<sup>9</sup> V/m). The file docked_structures_6m0j.zip contains the 100 best scored docked structures for each case as the output of PyDOCK.</p> <p>Trajectories are stored in GROMACS compressed trajectory file format (.xtc), downsampled to a 1ns timestep. Individual trajectories length are between 300 nanoseconds and 1 microsecond. In-silico docked structures are in PDB format. See linked preprint for more details.</p>
Banana Per Capita Consumption and SARS-CoV-2 Mortality Rates
<p>Plant Lectins are natural Antiviral agents and Banana is rich in them. Bananas are also a rich source of magnesium. Magnesium has a positive and effective role in increasing the body's immunity and stimulating the production of antibodies in the body.</p> <p>R2=0.99</p>
A vaccine-induced public antibody protects against SARS-CoV-2 and emerging variants
<p>These are the<strong> processed</strong> BCR repertoire bulk sequencing data described in <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a>. The <strong>raw</strong> sequence data are available on SRA under BioProjects <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>. </p> <p><strong>Summary</strong>: Bulk-sorted total plasmablasts and IgDlo enriched B cells from PBMCs and germinal centre B cells from lymph nodes from various timepoints after primary immunization from 22 BNT162b2 vaccinees who had no prior history of infection with SARS-CoV-2. </p> <p><strong>Metadata file</strong>: WU368_schmitz_et_al_immunity_2021_meta.tsv</p> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>PB = plasmablast</li> <li>GC = germinal center</li> <li>mAb = monoclonal antibody</li> </ul> <p><strong>BCR data file</strong>: WU368_schmitz_et_al_immunity_2021_bcr.tsv.gz</p> <p>In addition to the processed bulk sequences, also included are the heavy chains of 37 mAbs (including 2C08) first reported in <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> that had been validated to be spike-binding. The mAbs are annotated as "mab" in the "seq_type" column.</p> <p><strong>Sequence data column description</strong></p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s are used, as opposed to IMGT-defined "junctions". Non-standard columns are noted below.</p> <ul> <li>v_call_genotyped: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a></li> <li>isotype: IGH[ADEGM]</li> <li>cdr3: CDR3 nucleotide sequence</li> <li>cdr3_length: CDR3 nucleotide sequence length</li> <li>cdr3_aa: CDR3 amino acid sequence</li> <li>donor: vaccinee ID</li> <li>sample: sample ID (arbitrary)</li> <li>timepoint: time point at which sample was collected</li> <li>tissue: tissue from which sample was collected</li> <li>sorting: FACS sorting</li> <li>seq_type: sequence type (mAb or bulk)</li> </ul>
Inflammatory Responses in the Placenta upon SARS-CoV-2 Infection Late in Pregnancy - IHC data
<p>SARS-CoV-2 infection during pregnancy does not affect the large majority of neonates but presents an increased risk for adverse pregnancy outcome. The effects of SARS-CoV-2 of its recently identified variants on placental function are not well understood. In this study, we investigated the impact of late gestational SARS-CoV-2 infection on the placenta.</p> <p>This dataset of is comprised of 897 images of classic immunohistochemistry for 3 markers in placenta from COVID-19 patients and controls.</p> <p><strong>A full description of the tissues, markers, and donors is available in the metadata.csv file.</strong></p>
Coarse-grained molecular dynamics simulations of SARS-CoV-2 envelope protein E in the pentameric form
<p>The trajectories of coarse-grained (CG) molecular dynamics (MD) simulations of<br> 1) unmodified (FeigLab_NMR; FeigLab_PentamerNoPTM_POPC_Martini3b: 5 μs; 5 μs); <br> 2) palmitoylated (FeigLab_PentamerCYSP43; PentamerCYSP44_POPC_Martini3b: 5 μs; 5 μs); <br> SARS-CoV-2 E protein pentamer in a POPC bilayer.</p> <p>The trajectory of CG MD of system containing 2 pentamers in the membrane buckled in a single direction (BuckledMembrane_FeigLab_2xPentamerNoPTM_POPC_Martini3b: 1 μs).</p> <p>FeigLab_Pentamer: https://github.com/feiglab/sars-cov-2-proteins/blob/master/Membrane/E_protein.pdb<br> FeigLab_NMR_Pentamer is assembled based on the transmembrane domain determined by NMR (PDB ID: 7K3G) and FeigLab model for the rest.</p>
Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students
<p>The deposited files contain a database related to the study of the fear of COVID-19 among Polish students and a code book. It is connected with the article titled <em>Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students</em></p>
Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho
<p>These are pseudo-anonymised data from the MISTRAL study: "Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho". The data dictionary explains the data available in the dataset. Between December 2020 and September 2021, 2131 individuals with either COVID symptoms or contact with a COVID-positive case were included from two hospitals in Lesotho and had a valid PCR results.</p>
ScienceDex guides
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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.