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45 results for “SARS-CoV-2 pandemic”
Dataset for: Pre-pandemic artificial MERS analog of polyfunctional SARS-CoV-2 S1/S2 furin cleavage site domain is unique among spike proteins of genus Betacoronavirus
<table> <tbody> <tr> <th> </th> <td> <div> <h3><strong>Data File Descriptions and Methods</strong></h3> <ol> <li><strong>Data file 1 [betacov_matching_IPR042578.fasta]</strong>: Representative set of 2,465 betacoronavirus S protein overlapping homologous superfamily sequences retrieved in fasta format on 4 December 2022 from the InterPro repository at https://www.ebi.ac.uk/interpro/entry/InterPro/IPR042578/.<br><br></li> <li><strong>Data File 2 [betacov_matching_IPR042578_motif.fasta]</strong>: With Data File 1 as input, extracted 98,122 furin cleavage site (FCS) output motifs of 20 amino acids length, including overlapping and redundant sequences, produced with the FindFur algorithm with preset parameters as described by (Gu, 2020). FindFur as used was deposited on 15 December 2020 at the GitHub software repository at https://github.com/chwisteeng/FindFur.<br><br></li> <li><strong>Data File 3 [table_s1s2_hits_betacov_polyf.pdf]</strong>: Compiled summary table of sequence hits (PDF) of spike S1/S2 domains across genus <em>Betacoronavirus. </em>The compiled table of hits removed from Data File 2 sequences corresponding to spike protein fragments (incomplete length spike proteins as deposited at GenBank) and duplicates (redundant parts identically overlapping within the 20 amino acids motif windows), and then selected one sequence representative for multiple but identical sequences.<em> </em>Collection dates and geographical locations were retrieved from the NCBI Genbank protein database at https://www.ncbi.nlm.nih.gov/protein/. For SARS-CoV-2 spike variants, these data were also cross-validated with the SARS-CoV-2 lineage mutation tracker (Gangavarapu, 2023) available at https://outbreak.info which was based on extensive sequencing data from the global GISAID initiative (https://gisaid.org/). Solid lines (-) depict pat7 NLS, asterisks (*) O-glycosites, and circumflex (^) symbols FCS.<br><br></li> <li> <p><strong>Data File 4 [table_s1s2_hits_betacov_polyf.xlsx]</strong>: Compiled summary table of sequence hits (MS Excel) of spike S1/S2 domains across genus <em>Betacoronavirus. </em>The compiled table of hits removed from Data File 2 sequences corresponding to spike protein fragments (incomplete length spike proteins as deposited at GenBank) and duplicates (redundant parts identically overlapping within the 20 amino acids motif windows), and then selected one sequence representative for multiple but identical sequences.<em> </em>Collection dates and geographical locations were retrieved from the NCBI Genbank protein database at https://www.ncbi.nlm.nih.gov/protein/. For SARS-CoV-2 spike variants, these data were also cross-validated with the SARS-CoV-2 lineage mutation tracker (Gangavarapu, 2023) available at https://outbreak.info which was based on extensive sequencing data from the global GISAID initiative (https://gisaid.org/). Solid lines (-) depict pat7 NLS, asterisks (*) O-glycosites, and circumflex (^) symbols FCS.<br><br></p> </li> <li> <p><strong>Data File 5 [betacov_s1s2_nls_pat7_furin_psort.txt]: </strong>Nuclear localization signal (NLS) detection output for 5 representative betacoronavirus spike sequence domains, including the positive hits for pat7 in SARS-CoV-2 and for MERS-MA30 CoV. NLS predictions used the PSORT algorithm available as a webservice at https://wolfpsort.hgc.jp/ which is based on the work of Nakai and Horton (Nakai and Horton, 1999). Numbering refers to Data File 3 and Data File 4.<br><br></p> </li> <li> <p><strong>Data File 6 [betacov_s1s2_oglyc_netogly.txt]: </strong>Detection output for 5 representative betacoronavirus spike sequence domains tested for Thr/Ser O-glycosite residue pairs with the standard prediction software NetOGlyc4.0 (Steentoft et al., 2013) as available at https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/. Positive hits have scores above 0.5. Numbering refers to Data File 3 and Data File 4.<br><br></p> </li> <li> <p><strong>Data File 7 [betacov_s1s2_nls_pat7_furin_blastp.txt]</strong>: Comprehensive sequence database searches using were performed using the NCBI protein BLAST (blastp) algorithm with webservice available at https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins. The following blastp search parameters and settings were used: Word size=2; Expect value=200000; Hitlist size=500; Gapcosts=9,1; Matrix=PAM30; Filter string=F; Genetic Code=1;Window Size=40; Threshold=11; Composition-based stats=0; Database Posted date=Jan 19, 2023 2:59 AM; Number of letters=17,117,563; Number of sequences=10,766; Entrez query: Includes: Betacoronavirus (taxid:694002); Excludes: SARS-CoV-2 (taxid:2697049). The six polyfunctional input query consensus motif sequences were TXXPR(K/H/R)XRSX and TXXPRX(K/H/R)RSX.</p> </li> </ol> <h3><strong>References</strong></h3> <p>Gu, C., 2020. FindFur: A Tool for Predicting Furin Cleavage Sites of Viral Envelope Substrates. Master’s Thesis, San Jose State University, CA, USA. doi: <a href="https://doi.org/10.31979/etd.4ahv-9jya">10.31979/etd.4ahv-9jya</a> </p> <p>Gangavarapu K, Latif AA, Mullen JL, Alkuzweny M, Hufbauer E, Tsueng G, Haag E, Zeller M, Aceves CM, Zaiets K, Cano M, Zhou X, Qian Z, Sattler R, Matteson NL, Levy JI, Lee RTC, Freitas L, Maurer-Stroh S; GISAID Core and Curation Team; Suchard MA, Wu C, Su AI, Andersen KG, Hughes LD. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Nat Methods. 2023. 20(4):512-522. doi: <a href="https://doi.org/10.1038/s41592-023-01769-3">10.1038/s41592-023-01769-3</a>.</p> <p>Nakai, K., Horton, P., 1999. PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem Sci 24, 34–36. doi: <a href="https://doi.org/10.1016/s0968-0004(98)01336-x">10.1016/s0968-0004(98)01336-x</a></p> <p>Steentoft, C., Vakhrushev, S.Y., Joshi, H.J., Kong, Y., Vester-Christensen, M.B., Schjoldager, K.T.-B.G., Lavrsen, K., Dabelsteen, S., Pedersen, N.B., Marcos-Silva, L., Gupta, R., Bennett, E.P., Mandel, U., Brunak, S., Wandall, H.H., Levery, S.B., Clausen, H., 2013. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J 32, 1478–1488. doi: <a href="https://doi.org/10.1038/emboj.2013.79">10.1038/emboj.2013.79</a></p> </div> </td> </tr> </tbody> </table>
Vector sequences in early WIV SRA sequencing data of SARS-CoV-2 inform on a potential large-scale security breach at the beginning of the COVID-19 pandemic
<p>DESCRIPTION</p> <p>Sequences identified as Influenza A virus, Spodoptera frugiperda rhabdovirus and Nipah henipavirus have been previously identified within the early HiSeq 1000 and HiSeq 3000 sequencing data of SARS-CoV-2, SRR11092059,SRR11092060,SRR11092061 and SRR11092062, and were being used to support the hypothesis that a "simultaneous outbreak of multiple zoonotic viruses" have happened in the Huanan Seafood market. https://doi.org/10.31219/osf.io/s4td6</p> <p>However, a closer examination of these sequences revealed that they were not sequences of actual wild viruses, but were in stead fragments left behind from PCR products and cloning vectors harboring both cDNA clones and infectious clones of such viruses, with evidence of viral sequences being joined directly to DNA sequences of vector and non-human origin within the same short reads.</p> <p>Here are the vector sequences and PCR product-like sequences recovered from the earliest WIV SRA sequencing data of Human SARS-CoV-2 from dataset SRR11092059,SRR11092060,SRR11092061,SRR11092062.</p> <p>Sequences associated with Vectors and PCR products from 3 distinct viral species have been obtained: The 3'-end of a Nipah Henipahvirus with fusion to a Hepatitis D virus Ribozyme, a T7 terminator and a Tetracycline resistance gene, The 5'-end of the same Nipah Henipahvirus with fusion to sequences found in diverse vectors, A complete vector genome encoding the HA gene of Influenza A virus subtype H7N9 under a CMV promoter and a bgH polyA terminator, and 221 Contiguous sequences corresponding to the Spodoptera frugiperda rhabdovirus reference genome fused to sequences that were homologous to multiple Plastid sequences and Notably Mitochondrial sequences of Rodents.</p> <p>As sequences corresponding to a rescued infectious clone of a BSL-4 organism (Nipah Henipahvirus) were found in sample sequences that supposedy represents patient samples that were obtained from Hospital ICU and sequenced in a pathogen diagnosis laboratory (which is separate from the Virology Research laboratory which is implied by the context of an Infectious Clone of such an organism, evident by the 3'-HDV ribozyme and T7 terminator fused directly to the 3'-terminus of the Nipah Henipahvirus reads), The discovery of artifact-containing sequences of at least 3 different pathogen species that are phylogenetically and methodologically distinct from each other in samples that were supposedly submitted by a laboratory that is Separate from the virological research laboratories that could have hosted such clone sequences imply extensive crosstalk and cross-contamination between the various laboratories within the Wuhan Institute of Virology, which includes at least one BSL-4 laboratory with evidence of containment breach of a BSL-4 organism and it's subsequent introduction into RNA-seq samples that were processed by a laboratory of distinct and separate purposes than the basic virological research evidenced by the Infectious Clone of the Hipah Henipahvirus.</p> <p>Such a discovery therefore likely imply a major security breach happening within the Wuhan institute of Virology at the time when the first sequences of SARS-CoV-2 was sampled and sequenced, which have important implications on the origins of the SARS-CoV-2 virus itself.</p> <p>METHODS</p> <p>The metagenomic sequencing datasets, SRR11092059,SRR11092060,SRR11092061 and SRR11092062 were first analyzed using the NCBI phylogenetic analysis tool, which identified viral sequences that is not related to SARS-CoV-2 itself. These include Influenza A virus (IAV, subtype H7N9), Spodoptera frugiperda rhabdovirus and Nipah Henipahvirus.</p> <p>The datasets were then subjected to BLAST search using MEGABLAST against the reference sequences of such viruses to verify the existence of the viral sequences and determine the exact sybtype of such viruses and the closest sequences on GenBank that corresponds to the reads. There seuqences are MH926031.1 for the Spodoptera frugiperda rhabdovirus, KY199425.1 for the Influenza A virus and AY988601.1 for the Nipah Henipahvirus.</p> <p>A second round BLAST analysis with these identified sequences were then performed, which unexpectedly revealed numerous reads corresponding to Cloning vectors and non-human Mitochondrial and Plastid sequences being fused directly to the sequences of the identified viral species. Reads were then downloaded and subjected to assembly using the CAP3 sequence assembly program and the EGASSEMBLER tool. Contig sequences were then queried against the NCBI nr/nt database which unanimously identified the original sample sequences as viral sequences inserted into cloning vectors.</p> <p>The complete sequence of the Influenza A virus Haemagluttinin (HA) gene clone was obtained from SRR11092061,SRR11092062 using multiple rounds of BLAST search and sequence assembly expansion on the existing vector-virus junction contigs, and a partial sequence corresponding the 3'-end of Nipah Henipahvirus AY988601.1 fused to a 3'-HDV ribozyme, T7 terminator and a Tet resistance gene was obtained from SRR11092059. In addition, 221 Contig sequences corresponding to the Rhabdovirus MH926031.1 fused to Chloroplast sequence MN524635.1 and Rodent Mitochondrial sequence MT241668.1 have been recovered from SRR11092061.</p> <p>We then performed a BLAST search using the identified vector sequences on SRR11092059,SRR11092060,SRR11092061 and SRR11092062, which confirms the existence of these two vetor sequences in all 4 datasets.</p>
The CHASING COVID Cohort Study: A national, community-based prospective cohort study of SARS-CoV-2 pandemic outcomes in the USA
<p>The Communities, Households and SARS-CoV-2 Epidemiology (CHASING) COVID Cohort Study is a community-based prospective cohort study launched during the upswing of the USA COVID-19 epidemic. The objectives of the cohort study are to: (1) estimate and evaluate determinants of the incidence of SARS-CoV-2 infection, disease and deaths; (2) assess the impact of the pandemic on psychosocial and economic outcomes and (3) assess the uptake of pandemic mitigation strategies. 6740 people are enrolled in the cohort, including participants from all 50 US states, the District of Columbia, Puerto Rico and Guam. Participants are contacted regularly to complete study assessments, including interviews and dried blood spot specimen collection for serologic testing.</p> <p>Datasets are provided in CSV and sas7bdat (with formatting script) file formats.</p>
Pandemic-related Attitudes, Stressors and Work Outcomes among Medical Assistants during the SARS-CoV-2 ("Coronavirus") Pandemic in Germany: a cross-sectional Study
<p>File type: SPSS file (.sav)</p> <p>Study type: Cross-sectional study</p> <p>Population: Medical assistants in Germany</p> <p>Study period: April 7th-April 14th, 2020</p> <p>Number of participants: 2150</p> <p>Research question: Investigation of pandemic-related attitudes, stressors and work outcomes among medical assistants during the SARS-CoV-2 (“Coronavirus”) pandemic</p> <p>Missing values: None (due to online survey) </p> <p>Original variables: v_982, v_1, v_2, v_3, v_5, v_6, v_7, v_13, v_14, v_21, v_22, v_23, v_24, v_26, v_27, v_28, v_29, v_31, v_32, v_33, v_40, v_41, v_42, v_43, v_46, v_47, v_48 v_49, v_52, v_57, Beruf_MFA</p> <p>All other variables were calculated from the original variables either by rescaling or dichotomization. </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>
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>
Quantification of SARS-CoV-2 RNA in Wastewater Treatment Plants Mirrors the Pandemic Trend in Hong Kong
<p>The dataset included the SARS-CoV-2 and PMMoV virus concentration of WWTPs from December 24, 2020 to June 30, 2021 in Hong Kong, China.</p>
Etiology of upper respiratory tract infection in outpatients before and during the SARS-CoV-2 pandemic
<p>Illumina MiSeq Viral reads after quality passing and detection in zipped FASTQ format. Files are named by Patient's codes and whether DNA or RNA workflow is used for sample preparation.</p>
Evolutionary trends in the compositional structure of the SARS-CoV-2 genome during the pandemic
<p>By computing a measure of compositional genome structure in random datasets of coronavirus genomes, we observed a long-term decreasing trend in this measure, accompanied by an increasing evolutionary rate.</p> <p> </p>
Pandemic, epidemic, endemic: B cell repertoire analysis reveals unique anti-viral responses to SARS-CoV-2, Ebola and Respiratory Syncytial Virus
<p>VDJ gene usage, and associated amino acid sequences and properties, from healthy controls as well as patients with COVID-19, RSV or Ebola and Yellow fever vaccine recipients.</p>
Nature and well-being: The association of nature engagement and well-being during the SARS-CoV-2 pandemic
<p><span>1. </span><span>Numerous studies have shown the positive association between nature engagement and well-being. During the early phases of the SARS-CoV-2 pandemic, nature engagement changed dramatically as mental health and well-being declined across the globe.</span></p> <p><span>2. </span><span>This study examines how psychological connection to nature and engagement with nature in various forms is associated with well-being during the SARS-CoV-2 pandemic. Specifically, we examine which types of nature engagement (i.e., with nearby nature, through nature excursions, and media-based) are more strongly associated with well-being based on measures of loneliness, rumination, pandemic emotional impact, and mental health.</span></p> <p><span>3. </span><span>We employed a cross-sectional online survey of adults (N=3,282) residing in the United States, 25% of whom report seldom spending time in nature.</span></p> <p><span>4. </span><span>Our findings revealed that the psychological construct of connection to nature was associated with less loneliness and greater mental health. Overall, nature engagement was a consistent predictor of well-being, but different types of activities predicted varying outcomes on our four dependent variables. Greater engagement with nearby nature during the pandemic was associated with less rumination, less pandemic emotional impact, and better mental health while nature excursions (e.g., camping, backpacking) and media-based nature engagement were associated with greater loneliness, more emotional impact from the pandemic, and worse mental health. Additionally, nature engagement via media was associated with greater rumination.</span></p> <p><span>5. </span><span>Our findings suggest that promoting opportunities to increase engagement with and access to nearby nature is associated with better human well-being, especially during challenging events, and should be part of a </span>multi-pronged approach for coping with the next public health crisis<span>.</span></p>
Evaluation of oxidative stress markers in Rwanda during the SARS-CoV-2 pandemic: a cross-sectional study
<p>SARS-CoV-2 is mainly described as endothelial dysfunction, and due to the bidirectional link between oxidative stress and endothelial dysfunction, we initiated a program directed to the evaluation of the oxidative status of the population of Rwanda by measuring spectrophotometrically their plasma Reactive Oxygen Metabolites (dROMs) and Plasma Antioxidant Potential (PAT). The reference population was chosen to reflect the absence of actual or past SARS-CoV-2 infections as well as other clinically established infective status and reference intervals for d-ROM and PAT were identified.</p> <p>The average d-ROM was 378.6 UCARR with a standard deviation of 105.2. The average PAT value was 2853.6, with a standard deviation of 635.7 UCOR. On the basis of the published values for the Caucasian and East Asian populations, the average value of d-ROM obtained in Rwanda was significantly higher than expected. Conversely, the average PAT value was at the upper limit according to the averaged values for healthy Caucasian populations. The results of this study, the first so far reported on a sub-Saharan population, can effectively be used as a baseline value for clinical management of inflammatory conditions, for the stratification of at-risk individuals and to inform recommendations for effective use of public health resources.</p>
Serological Response to SARS-Cov-2 Virus in Personnel of the Institut Bergonié in the Context of the COVID-19 Pandemic
ClinicalTrials.gov study NCT04426006. IPD Sharing: NO. Countries: 1. Publications: 1.
Nature and well-being: The association of nature engagement and well-being during the SARS-CoV-2 pandemic
Open the record for dataset details and reuse information.
Evaluation of oxidative stress markers in Rwanda during the SARS-CoV-2 pandemic: a cross-sectional study
Open the record for dataset details and reuse information.
Predicting reservoir hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic
Open the record for dataset details and reuse information.
Data from: Tradeoffs in moving citizen-based anuran call surveys online during the SARS-CoV-2 pandemic: lessons from rural Appalachia, USA
<p>Citizen science approaches provide adaptable methodologies for enhancing the natural history knowledge of understudied taxa and engaging underserved populations with biodiversity. However, transitions to remote, virtual training and participant recruitment in response to public health crises like the SARS-CoV-2 pandemic have the potential to disrupt citizen science projects. We present a comparison of outputs from a citizen science initiative built around call surveys for the Mountain Chorus Frog (<i>Pseudacris brachyphona</i>), an understudied anuran, in Appalachian Virginia, USA prior to and during the SARS-CoV-2 pandemic. A transition to virtual training in this initiative did not lead to a decrease in scientific output and led to unexpected natural history insight about our focal taxon; however, a reliance on virtual instruction did decrease overall participation by local residents, particularly for rural K-12 students. We discuss the tradeoffs exhibited by the adaptation of our initiative to a virtual format and provide recommendations for other citizen science initiatives facing similar restrictions in the face of current and future public health crises.</p>
Study to Determine the Immunization Status Among Nephrological Health Care Personnel Against SARS-CoV-2 in a Single Center Over the Course of 12 Months During the Worldwide COVID-19 Pandemic.
ClinicalTrials.gov study NCT04347694. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
CPAP Observance During the COVID-19 (SARS-CoV-2) Pandemic
ClinicalTrials.gov study NCT04775966. IPD Sharing: NO. Countries: 1. Publications: 1.
STEP-COVID: A Program for Pregnant Women During the SARS-CoV-2 Pandemic
ClinicalTrials.gov study NCT05419167. IPD Sharing: NO. Countries: 1. Publications: 1.
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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)
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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.