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2,562 results for “SARS CoV 2”
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
Open the record for dataset details and reuse information.
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'
Open the record for dataset details and reuse information.
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
Open the record for dataset details and reuse information.
Early and late consequences of damage to the stem cell compartment following SARS-CoV-2 infection
<p>This application has been reviewed in a previous cycle and we would like to thank reviews for very positive comment. However, to our surprise one reviewer stated that this proposal is not based on our original hypothesis. To address this misunderstanding the role of Nlrp3 inflammasome as a trigger of cytokine storms COVID19 was proposed in the literature for a first time by our team (Ratajczak MZ, Kucia M. SARS-CoV-2 infection and overactivation of Nlrp3 inflammasome as a trigger of cytokine "storm" and risk factor for damage of hematopoietic stem cells. Leukemia. 2020 Jul;34(7):1726-1729. doi: 10.1038/s41375-020-0887-9) and subsequently several groups confirmed this hypothesis and highly cited our initial report. COVID19 or SARS-CoV-2 virus is single-stranded RNA virus, whose infection can be asymptomatic or lead to damage of several vital organs and a fatal complication involving “cytokine storm”, which results in uncontrolled hyperactivation of the immune response by innate immunity cells. The major concern is that we still cannot foresee late complications of this infection including direct or indirect effects on stem cell compartment. SARS-CoV-2 may enter human cells after binding to the angiotensin-converting enzyme 2 (ACE2) receptor and utilizes its surface spike protein (SP) for attachment and entry into the target cells. It has been demonstrated that ACE2 receptor is highly expressed on hematopoietic stem cells (HSCs) and endothelial progenitors (EPCs) isolated from adult hematopoietic organs as well on developmental early precursors of these cells. Its expression increases with more primitive phenotype of cells and it may explain that our group noticed its high expression in addition to HSC and EPC on human very small CD133+CD34+Lin-CD45– cells, which can be specified as reported by us and others into functional HSCs and EPCs. COVID19 after binding to ACE2 may hyperactivate Nlrp3 inflammasome as we recently demonstrated in cells at different level of specification into hematopoietic and endothelial lineage. This may lead to pyroptotic death of the cells exposed to virus SP. Moreover, this could lead also as we postulated of an initiation of “cytokine storm” by innate immunity cells. Evidence accumulates that COVID19 infection despite a fact that it manifests primarily as a respiratory syndrome has significant impact on other organs including the hematopoietic system and endothelium leading to several complications. To support this a large percentage of infected patients, suffer from lymphopenia and thrombocytopenia as well as from damage of endothelium that promotes hypercoagulability. Nevertheless, there are still not very well-known mechanisms how virus affect human stem cells and damage them by productive or abortive infection. It is well known that the innate immune response and activation of the Nlrp3 inflammasome are important defense mechanisms during the first days of infection, until acquired immunity responds with the production of antibodies. However, as mentioned above hyperactivation of this intracellular protein complex in innate cells may induce a cytokine storm or may lead to their death of other cells in mechanism of pyroptosis. Virus may also damage cells by lysis or theoretically what we hypothesize may stay after entry into long living stem cells in a latent form and become activated when immune system becomes impaired. Our group postulated a possibility that damage of stem cells for hemato/endothelial lineage may occur mainly by hyperactivation of Nlrp3 inflammasome after binding of viral SP to ACE2 expressed on these cells. Similar role may play interaction of SP with Toll like receptor-4 (TLR4). Our group and group of Dr. Hal Broxmeyer has demonstrated that exposure of umbilical cord blood-derived HSCs to SP protein decreases viability and in vitro clonogenicity of these cells. We also observed similar effect on proliferation of human EPC. Based on this a central hypothesis of our proposal is that COVID19 infection may damage by SP-ACE2 or SP-TLR4 interaction stem cells from hematopoietic/endothelial lineage which contributes to early and late consequences of this infection.</p> <p>Project was supported by the Polish National Center OPUS grant UM-2021/41/BNZ6/01590</p> <p>RNAseq database </p> <p>Patient COVID - PRJNA1167900 - <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1167900">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1167900</a></p> <p>scRNA-seq of VSELs: PRJNA1126429 (<a title="https://www.ncbi.nlm.nih.gov/sra/PRJNA1126429" href="https://www.ncbi.nlm.nih.gov/sra/PRJNA1126429" target="_blank" rel="noopener">https://www.ncbi.nlm.nih.gov/sra/PRJNA1126429</a>)</p> <p>scRNA-seq of HSC: PRJNA1128409 (<a title="https://www.ncbi.nlm.nih.gov/sra/PRJNA1128409" href="https://www.ncbi.nlm.nih.gov/sra/PRJNA1128409" target="_blank" rel="noopener">https://www.ncbi.nlm.nih.gov/sra/PRJNA1128409</a>).</p>
Molecular docking and quantum-chemical characterization of inhibitory activity of procyanidins and flavonol glucosides from Graptopetalum paraguayense E. Walther against nonstructural proteins of SARS-Cov-2
<p>The dataset includes:</p> <ol> <li><span>Optimized geometries of all ligands, NSPs and their complexes in gas phase</span></li> <li><span>Total energies (a.u.) and interaction energies (a.u. and kcal mol-1) of the complexes, ligands and NSPs in gas phase and in argon</span></li> <li><span>Mass spectra (full ms) and product ion spectra (ms2) of standards and identified components from GP. </span></li> <li><span>Output files from quantum-chemical calculations.</span></li> <li><span>pdb files of NSPs</span></li> <li><span>mol files of all complexes</span></li> </ol>
SCoV2-VAR: A light-weighted, customizable, and open-source database of 12 million SARS-CoV-2 genomes
<pre>Explosive accumulation of SARS-CoV-2 variants is posing a challenge to monitoring virus mutation and other data dealing, particularly based on centralized databases. The present study aimed to establish a light-weighted, customizable, and open-source database for SARS-CoV-2 genomes and annotations, without any access limit. The database, named SCoV2-VAR, was constructed, based on the variations (VAR) of the full-length SARS-CoV-2 (SCoV2) data uploaded on websites. All sequence samples were subject to quality control, single nucleotide polymorphism (SNP) annotation, format conversion, and final compression before appending to SCoV2-VAR. The final version of SCoV2-VAR (up to Feb 2024) contained more than 12 million SARS-CoV-2 records, with full genome and annotations. SCoV2-VAR was extremely light-weighted, with a storage size of 937 Mb for all 12 million sequences, post a 1: 596 compression. SCoV2-VAR is capable of timely updating, quickly querying, and customizable outputting SARS-CoV-2 sequences and their annotations. Additionally, the present study provided an overview of all 12 million SARS-CoV-2 samples, for both sequences and annotations.<br> <br><br></pre>
Robust detection of SARS-CoV-2 exposure in population using T-cell repertoire profiling
<p>The dataset contains processed T-cell receptor repertoire sequencing data from >1200 individuals of different sex and age. Note that only samples with good sequencing coverage are published (>10^5 reads per file). </p> <p>The main aim of our study is to find TCR sequence biomarkers and develop a bioinformatic pipeline that allows building an accurate and robust classifier that distinguishes COVID-19-convalescent donors from unexposed individuals. We performed immunosequencing of the rearranged TCR α and β regions for PBMCs. For the cohort described in this study (Cohort-I) we sequence both chains of the TCR heterodimer as both of these chains are required to properly predict antigen recognition26. We ran conventional T-cell repertoire data analysis and pre-processed data to remove low-coverage samples. </p> <p>Of samples in Cohort-I which passed read count threshold, 383/377 TCR α/β samples were from healthy donors (SARS-CoV-2 PCR test negative or obtained prior to pandemic) and 890/848 were from COVID-19-positive patients. The majority of samples were accompanied by information on HLA class I and II alleles. Samples were prepared and sequenced in nine batches.</p> <p>The metadata for both TCR alpha and beta repertoires contains the following information:</p> <div> <ul> <li>sequencing_date - date when seguencing was performed</li> <li>batch_name - one of the 9 unique batch identifiers</li> <li>sample_id, patient_id - information on sample identifier and donor identifier</li> <li>COVID_status, COVID_IgG, COVID_IgM, COVID_PCR - information on COVID-19 status</li> <li>HLA-A.1, HLA-A.2, HLA-B.1, HLA-B.2, HLA-C.1, HLA-C.2 - MHC class I alleles</li> <li>HLA-DPB1.1, HLA-DPB1.2, HLA-DQB1.1, HLA-DQB1.2, HLA-DRB1.1, HLA-DRB1.2 - MHC class II alleles</li> <li>file_name - name of the corresponding file in <em>fmba_clonotype_usage_tables.zip </em>archive</li> </ul> </div> <p>Each file in <em>fmba_clonotype_usage_tables.zip </em>archive stores the information on either TCR alpha or beta repertoire. Each line in a file corresponds to the unique clonotype and each clonotype is accompanied with the following information:</p> <ul> <li>count - number of reads where the clonotype was detected</li> <li>freq - count of reads with the clonotype divided by thw whole number of reads in a sample</li> <li>cdr3nt, cdr3aa - nucleotide and amino acid sequences of TCR's CDR3 sequence</li> <li>v, d, j - the V/D/J segment name which was used for the clonotype's rearrangement</li> <li>VEnd, DStart, DEnd, JStart - information on VDJ junction positions </li> </ul> <p>We proceed with selecting a set of CDR3 sequences that can serve as biomarkers and form a feature list for COVID-19 status classifier. We also validate the resulting set of clonotypes in several ways. Co-occurence of specific TCR α and β clonotypes can serve as an independent validation for biomarkers and their co-association with some specific pathogen. Additional information on donor HLAs is provided to filter the set of biomarkers based on HLA restriction: association with donor HLA serves as an additional evidence for TCR specificity to a specific set of antigens presented in a given donor and allows detecting the fingerprint of past and present infection. Furthermore, clonotypes with similar sequences can be aggregated into 'metaclonotype' biomarkers based on clonotype graph analysis.</p> <p>Finally, we train various COVID-19 status classifiers on selected batches from Cohort-I data using different algorithms and incorporating different feature sets. Verification of the robustness of our results was performed using independent batches of the Cohort-I and data from Cohort-II published previously.</p>
RNA extraction alternative method for SARS-CoV-2 molecular diagnosis
<p>The devastating outbreak of COVID-19 has posed serious challenges for the diagnostics laboratories, often facing global shortage of reagents and equipment. This study aimed at evaluating an additional RNA extraction method respect to those already recommended by WHO and CDC. A new protocol for RNA extraction from nasopharyngeal swab was set up, adapting the Qiagen RNeasy 96 plate for cell lines, and validated on a set of 96 clinical samples analyzed in parallel by a recommended method. The internal control and target genes analysis showed a good agreement between the two extraction methods, indicating that the two approaches can be considered equivalent for the SARS-CoV-2 diagnostics. The addition of this extraction method can help in increasing the throughput for SARS-CoV-2 molecular test, even in a low automation setting.</p> <p>The data set published in Zenodo is the full data analysed in the paper.</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.