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2,489 results for “Sars-CoV-2”
SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study
<p><strong>Background</strong>: Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions.</p> <p><strong>Methods</strong>: We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss.</p> <p><strong>Findings</strong>: The use of high efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% CrI: 73.1% - 85.7%) and 87% (CrI: 80.0% - 92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation.</p> <p><strong>Conclusions</strong>: Our analysis shows that a COVID-19 outbreak in a hospital's non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.</p>
Predicting reservoir hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic
<p><span>The SARS-CoV-2 pandemic has raised the concern for reservoir hosts of the virus since the early-stage outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting the viral genomic features, to calculate the infection likelihoods and further predict the probable hosts of novel viruses. Overcoming the limitation of sequence similarity-based methods, DeepHoF was applied to the analysis of SARS-CoV-2 in the 2020 pandemic. Using the isolates sequenced in the earliest stage of COVID-19, DeepHoF identified minks, bats, dogs and cats can be highly susceptible to SARS-CoV-2, while minks might be one of the most noteworthy reservoir hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the infection likelihood on human or the host range. With a large-scale genome analysis based on DeepHoF's computation for the later world-wide pandemic, it should not be slighted for the probably bidirectional transmission of SARS-CoV-2 between humans and minks.</span></p>
Targeted Intracellular Degradation of SARS-CoV-2 via Computationally-Optimized Peptide Fusions
<p>The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has elicited a global health crisis of catastrophic proportions. With only a few vaccines approved for early or limited use, there is a critical need for effective antiviral strategies. In this study, we report a unique antiviral platform, through computational design of ACE2-derived peptides which both target the viral spike protein receptor binding domain (RBD) and recruit E3 ubiquitin ligases for subsequent intracellular degradation of SARS-CoV-2 in the proteasome. Our engineered peptide fusions demonstrate robust RBD degradation capabilities in human cells and are capable of inhibiting infection-competent viral production, thus prompting their further experimental characterization and therapeutic development. </p>
SIRAH-CoV2 initiative: SARS-CoV-2 helicase (PDB id:6ZSL)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Helicase protein (PDB id:6ZSL). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Parameters for Zinc ions are those reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6ZSL_SIRAHcg_rawdata_0-4us.tar, 6ZSL_SIRAHcg_rawdata_4-8us.tar, and 6ZSL_SIRAHcg_rawdata_8-10us.tar contain all the raw information required to visualize (using VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6ZSL_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6ZSL_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6ZSL_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6ZSL_SIRAHcg_prot.prmtop 6ZSL_SIRAHcg_prot.ncrst 6ZSL_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
Production and purification protocols for Ectodomain of SARS-CoV-2 Spike (S) protein
<p>Production and purification protocols for Ectodomain of SARS-CoV-2 Spike (S) protein </p>
Electron microscopy of SARS-CoV-2 particles - Dataset 12
<p>The dataset contains transmission electron microscopy image stacks (tomograms) of ultrathin sections through extracellular SARS-CoV-2 particles in Vero cell cultures. The dataset contains 11 image stacks of 1900 x 1900 pixel dimensions, which were recorded at 0.57 nm pixel size (12 bit). Image stacks were size calibrated and stored in 8 bit TIF format. Visualization can be done using ImageJ or Fiji. A PDF document describes the methods used for generation of the image files. The dataset was generated as dataset 12 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
Electron microscopy of SARS-CoV-2 particles - Dataset 10
<p>The dataset contains 66 transmission electron microscopy images of ultrathin (110 nm) plastic sections through extracellular SARS-CoV-2 particles in Vero cell cultures. Images were recorded with 1376 x 1032 pixel dimensions at 0.64 nm pixel size (12 bit) and stored in 16 bit TIF format. For visualization of the images, use an image viewer capable of reading 16 bit images (e.g. IrfanView). Image files are size calibrated and can be opened with the correct size calibration using ImageJ or Fiji using the Bioformats importer. The image files are accompanied by a PDF document which describes the methods which were used for generation of the images. The dataset was produced as dataset 10 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
Electron microscopy of SARS-CoV-2 particles - Dataset 09
<p>The dataset contains 101 transmission electron microscopy images of ultrathin (85 nm) plastic sections through extracellular SARS-CoV-2 particles in Vero cell cultures. Images were recorded with 1376 x 1032 pixel dimensions at 0.64 nm pixel size (12 bit) and stored in 16 bit TIF format. For visualization of the images, use an image viewer capable of reading 16 bit images (e.g. IrfanView). Image files are size calibrated and can be opened with the correct size calibration using ImageJ or Fiji using the Bioformats importer. The image files are accompanied by a PDF document which describes the methods which were used for generation of the images. The dataset was produced as dataset 09 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
Electron microscopy of SARS-CoV-2 particles - Dataset 08
<p>The dataset contains 85 transmission electron microscopy images of ultrathin (65 nm) plastic sections through extracellular SARS-CoV-2 particles in Vero cell cultures. Images were recorded with 1376 x 1032 pixel dimension at 0.64 nm pixel size (12 bit) and stored in 16 bit TIF format. For visualization of the images, use an image viewer capable of reading 16 bit images (e.g. IrfanView). Image files are size calibrated and can be opened with the correct size calibration using ImageJ or Fiji using the Bioformats importer. The image files are accompanied by a PDF document which describes the methods which were used for generation of the images. The dataset was produced as dataset 08 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
Snapshots, frequency contact maps analysis, Poisson Boltzmann calculations, and data scripts for characterization of structural and energetic differences between conformations of the SARS-CoV-2 spike protein
<p><strong>Molecular dynamics simulation</strong> trajectories, which have been performed using the Amber ff14SB force field running with the Amber18 package at the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Simulation methodology and further details are described in [1] and [2]. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu).</p> <p>The <strong>Poisson Boltzmann </strong>energy calculations have been achieved by using the input_files.tar.xz found here and solving the Poisson Boltzmann equation with pygbe. A more detailed example and tutorial can be found at [4]. For further details contact Horacio V Guzman.</p> <p><strong>The dataset contains </strong></p> <ul> <li><strong>A total of 30 snapshots of the three trajectories (10 snapshots each system = two per replica x 5 replicas/system):</strong></li> </ul> <ol> <li>SARS-CoV-2002 spike protein with three RBD in the down positions: "COV2-DDD/PDB/" .</li> <li>SARS-CoV-2002 spike protein with one RBD in the up and two RBD in the down positions: "COV2-UDD/PDB/".</li> <li>SARS-CoV-2002 spike protein with two RBD in the up and one RBD in the down positions: "COV2-DUU/PDB/".</li> </ol> <ul> <li><strong>Input files for Poisson-Boltzmann analysis</strong>:</li> </ul> <ol> <li>PoissonBoltzmann/input_files.tar.xz</li> </ol> <ul> <li><strong>Data for the frequency contact map and processing scripts</strong>:</li> </ul> <ol> <li>cov2-ddd.pdb, cov2-udd.pdb, cov2-duu.pdb reference PDB files.</li> <li>Contact maps [3] at "COV2-DDD/CONTACT_MAP/", "COV2-UDD/CONTACT_MAP/", "COV2-DUU/CONTACT_MAP/".</li> <li>frequency.lua: get frequency of contacts from a set of contacts map files.</li> <li>diff_frequency.lua: get differential frequency of contacts from a set of frequency files.</li> <li>Frequency of contacts listed in frequency.data files at "COV2-DDD/", "COV2-UDD/" and "COV2-DUU/" directories.</li> </ol> <p>Read the "INFO" files for further informations.</p> <p>This dataset and the code is part of a collaboration between:</p> <ul> <li>The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)</li> <li>Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).</li> <li>Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).</li> <li>School of engineering in bioinformatics, University of Talca, Talca, Chile.</li> </ul> <p>[1] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. (2020). All-atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme (Version 0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3817447</p> <p>[2] Chad W. Hopkins, Scott Le Grand, Ross C. Walker, and Adrian E. Roitberg. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. Journal of Chemical Theory and Computation 2015 11 (4), 1864-1874. http://doi.org/10.1021/ct5010406</p> <p>[3] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. Quantitative determination of mechanical stability in the novel coronavirus spike protein. Nanoscale, 2020,12, 16409-16413. <a href="https://doi.org/10.1039/D0NR03969A">https://doi.org/10.1039/D0NR03969A</a></p> <p>[4] https://github.com/pyF4all</p>
Attitudes and Stressors related to the SARS-CoV-2 Pandemic among Emergency Medical Services Workers in Germany: A cross-sectional Study
<p>This dataset stems from a cross-sectional study conducted in April and Mai 2020 among n=1537 emergency medical services workers (EMS) from entire Germany during the first peak of the SARS-CoV-2 pandemic. The study questionnaire was distributed online with help of the German Association of Emergency Medical Service on their social media channels. The collected data provides insights into major stressors among EMS workers at the first peak of the pandemic in Germany and allows for analysis of possible determinants of major stressors via logistic regression analysis. No funding was obtained for this study.</p> <p> </p> <p><strong>Research question:</strong></p> <p>Investigation of pandemic-related attitudes, stressors and work outcomes among emergency medical services workers during the SARS-CoV-2 pandemic</p> <p><strong>Study population: </strong></p> <p>Emergency medical services workers in Germany</p> <p><strong>Study type: </strong></p> <p>Cross-sectional study (two independent cross-sectional waves)</p> <p><strong>File type: </strong></p> <p>SPSS file (.sav)</p> <p><strong>Study periods: </strong></p> <p>First wave: April 9th-16th 2020<br> Second wave: Mai 14th-21st 2020</p> <p><strong>Number of participants: </strong></p> <p>1537</p> <p><strong>Missing values: </strong></p> <p>None (due to online survey) </p> <p><strong>Original variables: </strong></p> <p>v_982, v_1, v_2, v_31, v_4, v_5, v_7, dupl1_v_13, dupl1_v-14, v_57, v_13, v_37, v_38, v_39, v_40, v_41, v_42, v_43, v_44, v_45, v_46, v_47, v_48, v_49, v_50, dupl1_v_40, dupl1_v_41, dupl1_v_42, dupl1_v_43, v_55, Beruf_Rettungsdienst, Welle</p> <p>All other variables were calculated from the original variables either by rescaling or dichotomization. </p> <p>Dichotomization of attitudes, stressors and work outcomes: <br> Answer options "Strongly disagree" and "Disagree" were labelled as "no"<br> Answer options "Agree" and "Strongly Agree" were labelled as "yes"</p> <p>Dichotomization of self-rated health:<br> Answer options "Very bad", "Bad" and "Moderate" were labelled as "Bad".<br> Answer options "Good" and "Very good" were labelled as "Good".</p>
Supplementary Material and Code of Evidence for immunity to SARS-CoV-2 from epidemiological data series
<p>This Supplementary Material file, Code and Data correspond to the manuscript:</p> <p>Evidence for immunity to SARS-CoV-2 from epidemiological data series<br> Pablo Yubero, Alvar A. Lavin, Juan F Poyatos<br> medRxiv 2020.07.22.20160028; doi: https://doi.org/10.1101/2020.07.22.20160028</p>
"A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. dataset
<p>Raw, original data and fits data set for "A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. in EXCEL and GraphPad Prism file formats and FORTRAN code.</p>
An Introduction to a Bayesian Analysis of the Laboratory Origin of SARS-CoV-2
<p>An Introduction to a Bayesian Analysis of the Laboratory Origin of SARS-CoV-2</p>
Supplementary Table 1 - GISAID Accession Numbers of Samples analysed in the first and second waves of SARS-CoV-2 cases in Irish hospitals
<p>The Supplementary Table 1 contains the GISAID accession numbers of samples sequenced in the context of the AIID biobank in the Republic of Ireland during the first and second wave of SARS-CoV-2 cases in hospitals of Dublin.</p>
Dataset of 3D-complexes of SARS-CoV-2:Human proteins
<p><strong>The dataset of human: SARS-CoV-2 protein complexes used for the study.</strong></p>
Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations
<p>Dataset and analysis for:</p> <p>Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations.<br>Arnab Mukherjee, Sharmistha Mishra, Vijaya Kumar Murty, Swetaprovo Chaudhuri<br> </p> <p>For any questions please contact the first author at: arnab.mukherjee@mail.utoronto.ca</p> <p><strong>Contents:</strong></p> <ol> <li><strong>school_active_cases_ON.zip:</strong> Contains datasets for number of COVID-19 infections reported by public schools in Ontario on ten different dates. The data files have been created based on the raw data in the file named 'covidtesting.csv' that has also been shared.</li> <li><strong>school_active_cases_pdf.m:</strong> Matlab code to obtain PDF of secondary infections in schools for a particular date based on the datasets in 'school_active_cases_ON.zip'. To obtain PDF for different dates, the appropriate dataset needs to be loaded. Created in MATLAB R2021b.</li> <li><strong>U_jet2.m:</strong><em> </em>User-defined Matlab function that is required to run the code 'gZ_code.m'. The function simulates the evolution of a simple jet/puff. Created in MATLAB R2021b.</li> <li><strong>gZ_code.m:</strong> Matlab code to obtain the analytical PDF of secondary infections due to long-range transmission, near-field transmission, or both. Created in MATLAB R2021b.</li> <li><strong>covidtesting.zip: </strong>Contains the data file 'covidtesting.csv' that reports the breakdown of COVID-19 infections in different public schools in Ontario on a daily basis. Data obtained from 'https://data.ontario.ca/dataset/summary-of-cases-in-schools/resource/dc5c8788-792f-4f91-a400-036cdf28cfe8'. Contains information licensed under the Open Government License – Ontario.</li> <li><strong>schoolrecentcovid2021_2022.zip:</strong> Contains the data file 'schoolrecentcovid2021_2022.csv<strong>' </strong>that reports the status of COVID-19 cases in Ontario, obtained from 'https://data.ontario.ca/en/dataset/status-of-covid-19-cases-in-ontario/resource/ed270bb8-340b-41f9-a7c6-e8ef587e6d11'. Contains information licensed under the Open Government License – Ontario.</li> </ol> <p> </p> <p> </p> <p> </p>
The nucleotides absent in genes of SARS-CoV-2 non-canonical subgenomic RNAs generate new Programmed -1 Ribosomal Frameshifting
<p>The data correspond to the article entitled: "dNTPs and adjuvant reagent solutions in 3’ RACE improve the characterization of noncanonical RNA SARS-CoV-2 genomes"</p> <p>R1. RACE 3’ Primer Blast Alignment. Contains BLAST alignments against the GenBank database using the consensus nucleotide sequence from the 3’ end of the SARS-CoV-2 genome and the polylinker. In addition, an illustration of the restriction enzyme pattern of the 3' RACE primer RV30AkCOVID19 and its synthesis by MALDI-TOF is included. The red box indicates the nucleotide sequence of the polylinker and the yellow box represents the 3' RACE primer along with the result of primer synthesis and purification.</p> <p>Graphic representation of the procedure for SARS-CoV-2 genome cDNA synthesis and design of the 3’ RACE RV30AkCOVID19 primer. The rectangle with vertical lines and the dots represents the 3’ RACE RV30AkCOVID19 primer and the polylinker, respectively, in the region complementary to the 3’ UTR end. The arrow represents the reverse transcriptase during complementary strand synthesis. The scissors represent RNases used in purification. The black spheres and magnets indicate the purification process using magnetism.</p> <p>R2. Reads and assembles SARS-CoV-2 genomes.</p> <p>The folder "1) Reads - Ion torrent" contains the reads obtained from sequencing via Ion Torrent technology and the reagents used in this study.</p> <p>The folder named "2) FastQC" contains the results of Ion Torrent sequencing. In the file name, the number indicates the sample, and the letters "RNA" indicate the sequencing according to the IonTorrent protocol. The cDNA synthesis procedures for this study correspond to the following nomenclature: dNTPs-R = dNTPs SARS-CoV-2 solution, DES-R = denaturation reagent, and COM PRO = commercial procedure.</p> <p>The folders named "3) IRMA" and "4) Bowtie2" contain the assemblies of the genomes.</p> <p>Regions and/or codons with loss of genomes 07dN120320 and 27sT122620.</p> <p>Mutations and amino acid substitutions of the SARS-CoV-2 genomes.</p> <p>In addition, an Excel document with the nucleotide ratios of each characterized genome is included from SARS-CoV-2.</p> <p>R3. BLAST alignment of assembled SARS-CoV-2 genomes. Contains two folders named "BLAST - IRMA" and "BLAST - Bowtie2," which contain plain text documents with the results of the BLAST alignment for the genomes obtained with each of the assemblies.</p> <p>R4. Pangolin v1.16 and Nextclade v2.9.1 lineages for SARS-CoV-2 genomes. Contains the folders "Pangolin and Nextclade (Bowtie2)" and "Pangolin and Nextclade (IRMA)." Each folder shows the data obtained with the Pangolin v1.16 and Nextclade v2.9.1 software for the classification of the genomes reported in this study, which were assembled with the IRMA and Bowtie2 software.</p> <p>R5. Reference genome alignment and assembled genomes. Contains the folders "1) IRMA genomes," "2) Bowtie2 genomes," and "3) Genomes 07dN120320 and 27St122620." The files show the sequences and alignments of the examined genomes (the file name indicates the analyzed genome) relative to the SARS-CoV-2 reference genome both in FASTA and Clustal W formats.</p> <p>R6. Programmed −1 Ribosomal Frameshifting Structure. The folder "1) Gibbs free energy 2D" contains a plain text document indicating the secondary structures of the open reading frame stimulation element in dot-bracket format. The folder "2) modeling Data Modeling 3D" contains the information for generating the structure of folder 1 in 3D.</p> <p>R7. SARS-CoV-2 Database.</p> <p>1) GISAID_sequences.zip contains a Zip file that contains a folder named GISAID, which in turn contains plain text documents with the genomes of each variant indicated in the filename of each document.</p> <p>2) The depuration of sequences_GISAID contains two subfolders. The first subfolder, named "1) SARS-CoV-2 complete genome" contains plain text documents with the genomes downloaded from GISAID without undetermined nucleotides. The file name of each document corresponds to the analyzed variant. The subfolder "2) SARS-CoV-2 eliminate genome" contains the sequences eliminated from subfolder 1 because they differed from the majority of the analyzed sequences.</p> <p>3) SARS-CoV-2 consensus variants. Contains plain text documents with consensus sequences for each variant, with frequency thresholds of 20 and 100 indicated in the file name of each document.</p> <p>4) SARS-CoV-2 alignment consensus variants. Contains two subfolders, with the number indicating the alignment frequency threshold. The "Alignment 20_" subfolder contains four documents named "with Ns," which correspond to fasta and Clustal formats with undetermined nucleotides, whereas the files named "without" do not have undetermined nucleotides. The "100_" folder has the same file pattern as the previous folder.</p> <p>5) SARS-CoV-2 codons alignment consensus variants and nc-sgRNA. Contains a document with the alignment of the genomes characterized in this study with the reference genome of SARS-CoV-2. A subfolder named “SARS-CoV-2 codons nc-sgRNA” shows each of the nc-sgRNA obtained in this study with the reference genome, and the file name corresponds to the nc-sgRNAs. The subfolder “SARS-CoV-2 Geneious Prime” contains 4 documents. Each document includes the graphical representation of the alignment of the nc-sgRNA obtained with each treatment for the synthesis of SARS-CoV-2 cDNA with respect to the reference genome. The following three documents indicated with the numbers 25, 50, and 100 correspond to the percentage of identity with respect to the number of annotations relative to the reference genome, which is indicated in the title of each document.</p> <p>6) Variant Alignment – Ns. Contains eight documents corresponding to the fasta and clustal formats with SARS-CoV-2 genomes obtained in this study from the reference genome and from genomes containing undetermined nucleotides of the Gamma, Lambda, Mu and Omicron variants.</p> <p>R8. Phylogeny SARS-CoV-2. Contains two subfolders with the results of the phylogenetic analyses conducted via the maximum likelihood method of the genomes characterized in this study compared to the variants. The subfolder named "Phylogeny with Ns" indicates the analysis of genomes containing undetermined nucleotides, whereas "Phylogeny without Ns" corresponds to the analysis of complete genomes.</p>
MD simulations from "#GotGlycans: Role of N343 Glycosylation on the SARS-CoV-2 S RBD Structure and Co-Receptor Binding Across Variants of Concern
<p>This folder contains all the MD simulations (saved in frames of 1 ns in PDB format) analysed and discussed in the paper titled "#GotGlycans: Role of N343 Glycosylation on the SARS-CoV-2 S RBD Structure and Co-Receptor Binding Across Variants of Concern" DOI https://doi.org/10.1101/2023.12.05.570076. The naming reflects the specific variant and the presence ('g' or 'gly') or absence ('ng' or 'nogly') of glycosylation at N343 and N331 sites in the SARS-CoV-2 S RBD. Gaussian accelerated MD simulations are indicated with 'gamd', all others represent conventional (deteriministic) sampling. For all details please refer to the original manuscript.</p>
Sars-Cov-2 and Mers sequences from human host with no unknown characters
<p>The datasets are organized as follows: first column, number of bases in a given sequence; second, third, fourth and fifth columns, number of bases of type A, C, G and T, respectively, in the same sequence. </p> <p><strong>1) Sars-Cov-2 dataset. </strong>This dataset contains number of bases for the complete genome sequences from a human host, with none unknown characters.<span> </span>In the NCBI database, there are about 950.000 sequences with these characteristics.</p> <p><strong>2) Restricted Sars-Cov-2 dataset:</strong> This dataset contains number of bases for the complete sequences from a human host, with no unknown characters, with 29903 bases, that is of the same length as the reference sequence NC045512.2. We obtained, from the NCBI database, about 5600 sequences with such features.</p> <p><strong>3) Mers dataset:</strong> This dataset contains number of bases for the complete sequences of about 200 complete genome sequences from a human host, with no unknown characters.</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.