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2,489 results for “Sars-CoV-2”
Variant analysis of SARS-CoV-2 genomes
<p>These are supplemental files accompanying a publication.</p>
Haruspex Analysis for SARS-CoV-2 surface_glycoprotein pdb entry 6m17 emdb 30039
Haruspex (version 1.0 190116) analysis for SARS-CoV-2 surface_glycoprotein , pdb entry 6m17 , emdb 30039. https://onlinelibrary.wiley.com/doi/10.1002/anie.202000421
Haruspex Analysis for SARS-CoV-2 surface_glycoprotein pdb entry 6vxx emdb 21452
<p>Haruspex (version 1.0 190116) analysis for SARS-CoV-2 surface_glycoprotein , pdb entry 6vxx , emdb 21452. https://onlinelibrary.wiley.com/doi/10.1002/anie.202000421</p>
Data from: Evolution and epidemic spread of SARS-CoV-2 in Brazil
Brazil currently has one of the fastest growing SARS-CoV-2 epidemics in the world. Owing to limited available data, assessments of the impact of non-pharmaceutical interventions (NPIs) on virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1–1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within-state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average travelled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil, and provide evidence that current interventions remain insufficient to keep virus transmission under control in the country.
Supplementary figure for: "UVA radiation could be a significant contributor to sunlight inactivation of SARS-CoV-2"
<p><strong>Supplementary Figure 1 for https://www.biorxiv.org/content/10.1101/2020.09.07.286666 </strong></p> <p><strong>Summary of sunlight inactivation mechanisms for viruses, based on <a href="https://paperpile.com/c/sh96XE/c05Xp+qSRra">[1,2]</a>. Solid yellow line: example of solar spectral irradiance reaching the Earth’s surface <a href="https://paperpile.com/c/sh96XE/KMpGD">[3]</a>. In principle, UVC light is most effective at damaging nucleic acid, leading to direct, endogenous inactivation; however, it is completely blocked by atmospheric ozone. Some UVB reaches the Earth’s surface, and may also damage nucleic acid. However, its effectiveness is lower than for UVC, and falls rapidly as wavelength increases (as shown by the white dashed line). Sunlight in the UVA range reaches the ground in larger amounts than for UVB, but does not interact directly with nucleic acid. However, UVA can be absorbed by natural or engineered sensitizers in the suspending medium, thereby creating photo-produced reactive intermediates that can damage viruses, leading to indirect, exogenous inactivation.</strong></p> <p><strong>* Corresponding author: pfegiz [at] ucsb [dot] edu</strong></p> <p><strong>1. <a href="http://paperpile.com/b/sh96XE/c05Xp">Nelson KL, Boehm AB, Davies-Colley RJ, et al. Sunlight-mediated inactivation of health-relevant microorganisms in water: a review of mechanisms and modeling approaches. Environ Sci Process Impacts. 2018; 20(8):1089–1122.</a></strong></p> <p><strong>2. <a href="http://paperpile.com/b/sh96XE/qSRra">Lytle CD, Sagripanti J-L. Predicted inactivation of viruses of relevance to biodefense by solar radiation. J Virol. 2005; 79(22):14244–14252.</a></strong></p> <p><strong>3. <a href="http://paperpile.com/b/sh96XE/KMpGD">Tropospheric Ultraviolet and Visible (TUV) Radiation Model [Internet]. [cited 2020 Sep 2]. Available from: </a><a href="https://www2.acom.ucar.edu/modeling/tropospheric-ultraviolet-and-visible-tuv-radiation-model">https://www2.acom.ucar.edu/modeling/tropospheric-ultraviolet-and-visible-tuv-radiation-model</a></strong></p> <p> </p> <p><strong>Funding statement:</strong></p> <p><strong>This work was supported by the University of California, Santa Barbara [Vice Chancellor for Research COVID-19 Seed Grant] and by the Army Research Office Multi University Research Initiative [W911NF-17-1-0306 to P.L.-F.].</strong></p>
Geostatistical Analysis of SARS-CoV-2 Positive Cases in the United States
<p>Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.</p> <p>This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8<sup>th</sup>, 2020):</p> <p><strong>Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” </strong>– results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24<sup>th</sup>, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.</p> <p><strong>Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" </strong>– the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24<sup>th</sup> through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.</p> <p><strong>Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" </strong>– this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.</p> <p>These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].</p> <p>The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.</p>
SIRAH-CoV2 initiative: Membrane embedded SARS-CoV-2 ORF3a (PDB id:6XDC)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of the SARS-CoV2 ORF3a dimeric transmembrane protein (PDB id: 6XDC, Bioassembly 1) embedded in a membrane patch containing POPE, POPC, and POPS phospholipids in a 2:1:1 proportion. 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.9b00435">Barrera 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>. </p> <p>The files contain all the raw information required to visualize (on 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. Additionally, </p> <p>The files 6xdc_SIRAHcg_rawdata_0-2us.tar, 6xdc_SIRAHcg_rawdata_2-4us.tar, 6xdc_SIRAHcg_rawdata_4-6us.tar, 6xdc_SIRAHcg_rawdata_6-8us.tar, and 6xdc_SIRAHcg_rawdata_8-10us.tar contain all the raw information required to visualize (on 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 6XDC_SIRAHcg_10us_prot-memb_skip10ns.tar contains only the protein and phospholipids´ coordinates, with one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6xdc_SIRAHcg_10us_prot-memb_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6xdc_SIRAHcg_prot-memb.prmtop 6xdc_SIRAHcg_prot-memb.ncrst 6xdc_SIRAHcg_10us_prot-memb_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 Exequiel Barrera (ebarrera@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SARS-CoV-2 PCR training
<p>***english version***</p> <p>These movies show the nucleic acid extraction process from swab samples followed by the PCR diagnostic for SARS-CoV-2, using commercially available kits. They were originally produced in the frame of a remote training given by the Robert Koch Institute in April 2020. Additional training materials are available here: <a href="https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training">https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training</a>.</p> <p> </p> <p>***version en française***</p> <p>Ces films montrent le processus d'extraction d'acides nucléiques à partir d'échantillons écouvillons suivi du diagnostic PCR pour le SRAS-CoV-2, à l'aide de kits disponibles dans le commerce. Ils ont été initialement produits dans le cadre d'une formation dispensée par l'Institut Robert Koch en avril 2020. Des matériel de formation supplémentaires sont disponibles ici: <a href="https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training">https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training</a>.</p> <p> </p> <p>***version en español***</p> <p>Estas películas muestran el proceso de extracción de ácido nucleico de hisopos seguido del diagnóstico por PCR para SARS-CoV-2, utilizando los kits disponibles en el mercado. Estas películas fueron producidas originalmente en el marco de una capacitación a distancia, impartida por Instituto Robert Koch en abril de 2020. Usted puede consultar el material de capacitación adicional aquí: <a href="https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training">https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training</a>.</p> <p> </p> <p>***русская версия***</p> <p>Эти фильмы показывают процесс экстракции нуклеиновой кислоты из образцов мазка с последующей диагностикой ПЦР на SARS-CoV-2 с использованием имеющихся в продаже наборов. Изначально они были созданы в рамках дистанционного обучения, проведенного Институтом Роберта Коха в апреле 2020 года. Дополнительные учебные материалы доступны здесь: <a href="https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training">https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training</a> <<a href="https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training">https://gitlab.com/zig-covid-training/sars-cov-2_pcr_training</a>> . </p> <p>We acknowledge Anna Shin (Анна Шин) and Nur Tukhanova (Нур Туханова) for translation of the Russian subtitles.</p> <p> </p>
Metaproteomics analysis of SARS-CoV-2-infected patient samples reveals presence of potential co-infecting microorganisms
<p>Supplemental data for SARS-CoV-2 patient sample metaproteomics analysis</p>
Computational epitope map of SARS-CoV-2 spike protein
<p>Dataset accompanying the publication "Map of SARS-CoV-2 spike epitopes not shielded byglycans" published in XYZ.</p> <p> </p> <p>The dataset contains:</p> <p>1. raw epitope screening scores (README file attached in the archive)</p> <p>2. structure and GROMACS topology and input files for two systems:</p> <p> - 4x SARS-CoV-2 spike protein, glycosylated<br> - 4x SARS-CoV-2 spike protein, non-glycosylated</p> <p> </p>
Data from: Acute necrotizing encephalopathy with SARS-CoV-2 RNA confirmed in Cerebrospinal fluid
Here we report a case of Covid-19-related acute necrotizing encephalopathy (ANE) where SARS-CoV-2 RNA was found in cerebrospinal spinal fluid (CSF) first 19 days after symptom onset after negative findings earlier. Even though monocytes and protein levels in CSF were only marginally increased, and our patient never experienced a hyperinflammatory state, she deteriorated in neurological function and became comatose. Magnetic resonance imaging of the brain showed pathological signal symmetrically in central thalami, subinsular regions, medial temporal lobes and brain stem. Extremely high concentrations of the neuronal injury markers neurofilament light (NfL) and tau, as well as an astrocytic activation marker glial fibrillary acidic protein (GFAp), were measured in CSF in parallel to in-depth proteomics analysis. The patient received intravenous immunoglobulins (IVIG) and plasma exchange (PLEX). Her neurological status improved and she was extubated four weeks after symptom onset. This case report highlights the neurotropism of SARS-CoV-2 in selected patients and emphasizes the importance of repeated lumbar punctures and CSF analyses in patients with suspected Covid-19 and neurological symptoms.
Images of loop-mediated isothermal amplification (LAMP) for SARS-CoV-2 testing and optimized 'Cap-iLAMP'
<p>Here we present images of loop-mediated isothermal amplification (LAMP) and optimized Cap-iLAMP (capture and improved loop-mediated isothermal amplification). Cap-iLAMP combines a hybridization capture-based RNA extraction of gargle lavage samples with an improved colorimetric RT-LAMP assay and smartphone-based color scoring. Cap-iLAMP is compatible with point-of-care testing and enables the detection of SARS-CoV-2 positive samples in less than one hour. The sensitivity is 97% and the specificity is 99%.</p>
Comparative evaluation of ten lateral flow immunoassays to detect SARS-CoV-2 antibodies
<p><b>Background: </b>Rapid mobilisation from industry and academia following the outbreak of the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), led to the development and availability of SARS-CoV-2 lateral flow immunoassays (LFAs). High-quality LFAs are urgently needed at the point of care to add to currently available diagnostic tools. In this study, we provide evaluation data for ten LFAs suitable for use at the point of care.</p> <p><b>Methods:</b> COVID-19 positive patients (N=45), confirmed by reverse transcription – quantitative polymerase chain reaction (RT-qPCR), were recruited through the International Severe Acute Respiratory and Emerging Infection Consortium - Coronavirus Clinical Characterisation Consortium (ISARIC4C) study. Sera collected from patients with influenza A (N=20), tuberculosis (N=5), individuals with previous flavivirus exposure (N=21), and healthy sera (N=4), collected pre-pandemic, were used as negative controls. Ten LFAs manufactured or distributed by ASBT Holdings Ltd, Cellex, Fortress Diagnostics, Nantong Egens Biotechnology, Mologic, NG Biotech, Nal von Minden, and Suzhou Herui BioMed Co. were evaluated.</p> <p><b>Results: </b>Compared to RT-qPCR, sensitivity of LFAs ranged from 87.0-95.7%. Specificity against pre-pandemic controls ranged between 92.0-100%. Compared to IgG ELISA, sensitivity and specificity ranged between 90.5-100% and 93.2-100%, respectively. Percentage agreement between LFAs and IgG ELISA ranged from 89.6-92.7%. Inter-test agreement between LFAs and IgG ELISA ranged between kappa=0.792-0.854.</p> <p><b>Conclusions: </b>LFAs may serve as a useful tool for rapid confirmation of ongoing or previous infection in conjunction with clinical suspicion of COVID-19 in patients attending hospital. Impartial validation prior to commercial sale provides users with data that can inform best use settings.</p>
Global (2M) SARS-CoV-2 genomes dataset, from Viridian, processed with MAPLE0.6.11
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Crystal Structures of SARS-CoV-2 main protease with screening fragments and COVID Moonshot compounds from the XChem facility at Diamond Light Source
<p>Bulk repositiory of structures of SARS-CoV-2 main protease in complex with fragment molecules from inital XChem screen and designed COVID Moonshot inhibtor compounds. Each structure has a PDB ID, coordinate file, structure factor file, ligand restraint (cif) and PANDDA event maps (as appropriate).</p><p>2023-10-26 - updated to include <strong>all </strong>initial fragment screening hits alongside follow up compounds</p>
Supplementary Data for 'Machine learning detection of SARS-CoV-2 high-risk variants'
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Global Transcriptomic Analysis of Placenta from Women with Gestational SARS-CoV-2 Infection during the 3rd Trimester of Pregnancy
<p>Supplementary data for <strong>Global Transcriptomic Analysis of Placenta from Women with Gestational SARS-CoV-2 Infection during the 3rd Trimester </strong><br><strong>of Pregnancy</strong></p>
Molecular dynamics trajectories related to manuscript: SARS-CoV-2 nsp3 and nsp4 are minimal constituents of a pore spanning replication organelle
<p>This dataset contains two folders, each with sequentially numbers coordinates files for all-atom molecular dynamics trajectories related to the manuscript "SARS-CoV-2 nsp3 and nsp4 are minimal constituents of a pore spanning replication organelle" by Zimmermann et al. Each folder contains PDB and PSF files specifying the components of each system depicted in Fig. S10 of that manuscript as well as 200 sequentially named DCD files, each containing 5 nanoseconds of a 1 microsecond trajectory.</p>
Luciferase readout: Raw neutralization results for neutralization assays from pseudoparticles containing the SARS-CoV-2 receptor binding domain from a cryptic lineage
<div> <div> <div> <p>Deep sequencing of wastewater to detect SARS-CoV-2 has been used during the COVID- 19 pandemic to monitor viral variants as they appear and circulate in communities. SARS- CoV-2 lineages of an unknown source that have not been detected in clinical samples, referred to as cryptic lineages, are sometimes repeatedly detected from specific locations. We have continued to detect one such lineage previously seen in a Missouri site. This cryptic lineage has continued to evolve, indicating continued selective pressure similar to that observed in Omicron lineages.</p> </div> </div> </div> <p>This file contains the raw neutralization data using pseudoparticles containing a SARS-CoV-2 Spike protein with the RBD from the cryptic lineage detected in Missouri wastewater.</p>
STAMINA project 883441 related raw sequencing data of RTPCR positive SARS-CoV-2 amplicons
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