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3,148 results for “Persistent”
Population persistence, phenotypic divergence and metabolic adaptation in yarrow (Achillea millefolium L.) along a climate gradient, CA, 1920 to 2023
This dataset provides insights into the persistence and adaptation of yarrow (Achillea millefolium L.) populations over a 100-year period of climate change. The data include plant height measurements and climatic variables (temperature and precipitation) from historical and resurveyed sites spanning a broad environmental gradient (1–3,200 m a.s.l.), alongside metabolic profiles obtained from a common-garden experiment. The dataset captures phenotypic changes in plant growth, metabolic diversity, and site-specific climatic shifts between 1920 and 2020. These data support analyses of how temperature and precipitation interact to shape plant responses over time and allow for exploring patterns of local adaptation in phenotypic and metabolic traits. This comprehensive dataset is valuable for understanding the ecological and evolutionary mechanisms underlying population persistence and can inform conservation strategies under future climate scenarios.
Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection [Primary data]
<p>Primary data underlying journal article titled "<strong>Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection</strong>"</p>
A fading radius valley towards M-dwarfs, a persistent density valley across stellar types -- data
Open the record for dataset details and reuse information.
Probing center vortices and deconfinement in SU(2) lattice gauge theory with persistent homology — data release
<p>This release contains all data used to prepare the publication <a href="https://arxiv.org/abs/2207.13392">Probing center vortices and deconfinement in SU(2) lattice gauge theory with persistent homology</a>.</p> <p>Included are:</p> <ul> <li>The raw log output from the simulations and computed persistence images for the analysis in Section IV.B of the <a href="https://arxiv.org/abs/2207.13392">paper</a> in 'raw_data.zip'.</li> <li>The values of the action and Polyakov loop from the above logs, along with the persistence images restructured into netCDF4 format for convenience, in the files 'Nt=*_Ns=*_pis_actions_polyakovs.nc'.</li> <li>The values of the observable m_2 (as defined in the <a href="https://arxiv.org/abs/2207.13392">paper</a>) for configurations for the twisted boundary conditions analysis in netCDF4 format in 'Nt=4_Ns=12_16_20_m2.nc'.</li> <li>The example persistence diagrams used in the <a href="https://arxiv.org/abs/2207.13392">paper</a> in netCDF4 format in 'Nt=4_Ns=12_example_pds.nc'.</li> </ul>
Dataset to "Persistent Identifiers for File Formats: enabling preservation and re-use of research data"
<p>This fileset includes a "preprint" and the main dataset <em>fileformatRecognizer</em> (as .xlsx and .csv) to the paper "Persistent identifiers for file formats: enabling preservation and re-use of research data" submitted to iPRES 2019, but subsequently rejected after peer review. For the sake of transparency, permission to make available here the anonymous reviews motivating the rejection (<em>ReviewsPIDs4fileFormats.odt</em>) was asked, but was left without response. Some images (screendumps) and text result files from file identification tools tested are included. Further, a simple xquery command file (BaseX) for <em>fetch:content-type</em>()<em>, </em>used for getting MIME-types for files, is also provided.</p>
Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence
<p>RNA sequencing, SomaLogic proteomics and flow cytometry data were generated for two cohorts of end-stage kidney disease patients with COVID-19. The Wave 1 cohort consists of samples collected from patients during the first wave of COVID-19 in early 2020, while samples were collected for the Wave 2 cohort in the following year.</p> <p>This data deposition includes the RNA-seq counts, SomaScan proteomics, flow cytometry and clinical metadata associated with the study. For further information about the study and data, see the associated GitHub repository (https://github.com/jackgisby/covid-longitudinal-multi-omics) or our pre-print (https://doi.org/10.1101/2022.04.29.22274267). The repository also contains code to replicate our analysis of the data.</p> <p>The raw RNA-seq reads were processed using the nf-core RNA-seq v3.2 pipeline before htseq-count was used to generate a raw counts matrix, which is included in this deposition (<code>htseq_counts.csv</code>). Three files make up the proteomics data: <code>sample_technical_meta.csv</code>, <code>feature_meta.csv</code> and <code>soma_abundance.csv</code>. The first two files contain metadata columns for the samples and protein features, respectively. The final file includes the unprocessed protein abundance data. The files <code>general_panel.csv</code> and <code>t_cell_panel.csv</code> contain the flow cytometry data, split into the general and T-cell panels, respectively. Finally, clinical metadata is available for the two cohorts described in this study (<code>w1_metadata.csv</code>, <code>w2_metadata.csv</code>).</p> <p>The features in the clinical metadata include:</p> <table> <thead> <tr> <th>Column Name</th> <th>Data Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>sample_id</td> <td>Character</td> <td>Unique identifier for samples</td> </tr> <tr> <td>individual_id</td> <td>Character</td> <td>Unique identifier for individuals</td> </tr> <tr> <td>ethnicity</td> <td>Character</td> <td>The individual's ethnicity (asian, white, black or other)</td> </tr> <tr> <td>sex</td> <td>Character</td> <td>The individual's sex (M or F)</td> </tr> <tr> <td>calc_age</td> <td>Integer</td> <td>Age in years</td> </tr> <tr> <td>ihd</td> <td>Character</td> <td>Information on coronary heart disease</td> </tr> <tr> <td>previous_vte</td> <td>Character</td> <td>Whether individuals have had venous thromboembolism</td> </tr> <tr> <td>copd</td> <td>Character</td> <td>Whether individuals have chronic obstructive pulmonary disease</td> </tr> <tr> <td>diabetes</td> <td>Character</td> <td>Whether individuals have diabetes, and, if so, the type of diabetes</td> </tr> <tr> <td>smoking</td> <td>Character</td> <td>Smoking status</td> </tr> <tr> <td>cause_eskd</td> <td>Character</td> <td>Cause of ESKD</td> </tr> <tr> <td>WHO_severity</td> <td>Character</td> <td>The peak (WHO) severity for the patient over the disease course</td> </tr> <tr> <td>WHO_temp_severity</td> <td>Character</td> <td>The (WHO) severity at time of sampling</td> </tr> <tr> <td>fatal_disease</td> <td>Logical</td> <td>Whether the disease was fatal</td> </tr> <tr> <td>case_control</td> <td>Character</td> <td>Whether the individual was COVID-19 <code>POSITIVE</code> or <code>NEGATIVE</code> at time of sampling. Convalescent patients are denoted by the label <code>RECOVERY</code></td> </tr> <tr> <td>radiology_evidence_covid</td> <td>Character</td> <td>Evidence of COVID-19 from radiology</td> </tr> <tr> <td>time_from_first_symptoms</td> <td>Integer</td> <td>The number of days since the individual first experienced COVID symptoms at time of sampling</td> </tr> <tr> <td>time_from_first_positive_swab</td> <td>Integer</td> <td>The number of days since the individual's first positive swab was taken at time of sampling</td> </tr> </tbody> </table>
Data availability for "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles"
<p>Data availability for the paper titled "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles" by Peter A. Alpert et al. This repository contains all data tables and files necessary to reproduce plots. Also included are open source ".hdf5” files that contain all data for X-ray microscopy images and ".dat" files having the raw data for mie resonance scattering to derive size change and mass loss. Please see the "Readme.pdf" file for more information.</p>
Datasets to article "Selection history alters attentional filter settings persistently and beyond top-down control"
<p>Single-Subject Behavioral and ERP mean amplitude data for Experiments 1 to 3.</p>
Unraveling a synthetic rescue process involved in persisters formation
<p>A common strategy that bacteria utilize to increase their survival under stressful conditions in their natural environments, including antibiotic treatment, is the entry into quiescence, a state of reversible cell growth arrest that offers protection against many environmental insults. Understanding quiescence is an important fundamental question, with relevance in the medical and environmental fields. Little is known about the molecular and physiological determinants that orchestrate survival during this temporary arrest of proliferation, or those that allow a rapid transition back to the proliferating state when conditions again become favorable. In the wide host-range pathogen <em>Salmonella enterica</em> serovar Typhimurium (<em>S</em>. Typhimurium) and other Gram-negative bacteria, this temporary arrest of proliferation induces the expression of the alternative sigma subunit of RNA polymerase, sigma S/RpoS, which remodels global gene expression to reshape the cell physiology and ensure survival under starvation and various stress conditions (<em>i.e. </em>the general stress response).</p> <p>In <em>S</em>. Typhimurium, sigma S is required for stress resistance, biofilm formation and virulence. The incidence of human infections by non-typhoidal <em>Salmonella </em>such as <em>S.</em> Typhimurium increases. These serovars can infect farm animals, thus contaminating animal products, and can be transferred from animal carriers to the environment through fecal matter where they contaminate vegetables, fruits, nuts, and roots. Foodborne diseases caused by <em>Salmonella </em>represent a severe problem to the food supply as well as the public health. <em>S</em>. Typhimurium actively cycles through host and nonhost environments, where it is exposed to a wide variety of stresses, and where sigma S likely plays a crucial role in its persistence.</p> <p>One important aspect of persistence is the phenotypic differentiation of quiescent populations into sub-population(s) of "persisters" that survive in the presence of lethal concentrations of antibiotics. This phenomenon is worsening the worldwide antibiotic crisis, by causing therapy failure and chronic infections and potentially favoring the development of antibiotic resistance. Understanding mechanisms governing bacterial persisters is thus an important topic and a key issue for drug developments. However, despites many studies, the physiological and molecular mechanisms controlling the formation of persisters are poorly understood and controversial. <strong>In the present study we explore the recently discovered and unexpected functional interaction between sigma S and succinate dehydrogenase (Sdh), in the formation of persisters. </strong></p> <p>Persisters are phenotypic variants within a population that survive in the presence of lethal concentrations of antibiotics. When a bacterial population is diluted into fresh medium containing bactericidal antibiotics, a biphasic killing is observed. The bulk of the population, consisting of sensitive cells, dies rapidly and the surviving persisters are killed much more slowly, or do not die during the time course of the experiment. Their regrowth after antibiotic removal yields a new population that has the same sensibility to the antibiotics, as did the parental one. Persisters formation is critically dependent on the growth phase. Despite the identification of a number of genes and pathways involved in persisters formation (toxin-antitoxin modules, SOS and stringent responses, efflux systems, metabolic functions and global regulators), the underlying molecular mechanisms are still poorly understood and controversial. In particular, persisters formation in <em>Escherichia coli</em> K-12 has been reported to be increased, decreased or not affected by a <em>rpoS</em> deletion. A better understanding of physiological parameters favoring persisters formation is critical to develop antipersisters strategies.</p> <p>Succinate dehydrogenase (Sdh), a membrane bound complex that connects the TCA cycle and respiratory chain, is one major target down regulated by sigma S. Negative regulation by sigma S likely targets housekeeping genes that might be deleterious when fully expressed in quiescent cells. Understanding why full expression of those genes has a fitness cost might provide insights into survival mechanisms and weaknesses of quiescent cells, with potential application for antibacterial strategies. To tackle this issue, we used Sdh as a model system. <strong>Our study led us to unravel a synthetic rescue process of a </strong><em><strong>sdh</strong></em><strong> mutation in the formation of persisters. </strong></p> <p>Stationary-phase <em>Salmonella </em>form persisters with a higher frequency than actively growing bacteria, after transfer to fresh medium in the presence of lethal concentrations of ampicillin and ciprofloxacin, but not significant effect of the <em>rpoS</em> deletion on this phenomenon was observed. Surprisingly however, the <em>rpoS</em> deletion suppressed the defect in persister formation of a <em>sdh</em> deletion mutant. Similar results were obtained with independent <em>sdh</em> and <em>sdhrpoS</em> constructs and the mutations did not significantly affect the minimum inhibitory concentration (MIC) of <em>Salmonella</em> for ampicillin and ciprofloxacin. <strong>It is very likely that the </strong><em><strong>rpoS</strong></em><strong> deletion compensates for a metabolic perturbation provoked by the </strong><em><strong>sdh</strong></em><strong> deletion, and key for persister formation. </strong>Since a <em>sdh</em> mutation also decreases persister formation by <em>E. coli </em>and <em>Staphylococcus aureus</em>, the underlying physiological effect might be common to Gram-negative and Gram-positive bacteria. Understanding the molecular and physiological bases of this phenomenon should provide insights into key features driving persisters formation and revival. </p> <p>For references , see the <strong>STUDY</strong> pdf file. </p> <p>The persister assay is described in the <strong>PROTOCOL</strong> pdf file.</p> <p>Strains used are described in the <strong>STRAINS AND PRIMERS</strong> .xlsx file.</p> <p>Results are summarized in the <strong>STUDY</strong> pdf file </p> <p>For detailed data, see the <strong>M1 to M101 persisters</strong> .xlsx files. </p> <p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong></p> <p>See also:</p> <p>NOREL Francoise, MONTEIL Veronique, DOUCHE Thibaut, & MATONDO Mariette. (2023). Global effects of deletion of the <em>sdh</em> genes, encoding succinate dehydrogenase, and of cobalt on protein abundance in stationary phase <em>Salmonella enterica</em> serovar Typhimurium. [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8279681">https://doi.org/10.5281/zenodo.8279681</a>. </p> <p>Phégnon, L., Uttenweiler-Joseph, S., & Létisse, F. (2024). <span>Key physiological and metabolic characteristics for the differentiation of quiescent Salmonella's cells into persisters [Data set]. </span>Zenodo. <a href="https://doi.org/10.5281/zenodo.10885905" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10885905</a></p> <p> </p>
Data and analysis for: "Persistent Spatial Clustering and Predictors of Pediatric La Crosse Virus Neuroinvasive Disease Risk in Eastern Tennessee and Western North Carolina, 2003–2020"
<p>This is the initial release of the data and code corresponding to the manuscript submitted to PLoS Neglected Tropical Diseases. <strong>Please refer to the README.md file</strong> for a description of the contents of this repository and how to use them. The README file can be opened with a text editor, or viewed directly in the GitHub repository. The data and code are provided within a project directory with a reproducible R package library for ease and accuracy of reproducibility. </p> <p><strong>Ethics Approval</strong></p> <p>This study was approved by the University of Tennessee, Knoxville Institutional Review Board (UTK IRB-22-07079-XP) and the Tennessee Department of Health Institutional Review Board (TDH IRB 2021-0314). Data provided here is de-identified and aggregated (both temporally and spatially) to protect the privacy of individuals included in the study, in concordance with IRB and Data Use Agreements.</p>
Dataset for Earth Sciences at Freie Universität Berlin: Open Access, Licenses and Persistent Identifiers Monitoring
<p>In <em>Version 4</em>, <strong>publishers </strong>and <strong>journals</strong> names has been extended.</p> <p>In <em>Version 3</em>, new entries have been added for both <strong>journal </strong>and <strong>non-journal article outputs</strong>, specifically including data from the year <strong>2023</strong>. Minor adjustments were also made to URLs and open access (OA) statuses.</p> <p><em>Note</em>: Data for journal and non-journal article outputs from the year 2023 were unavailable at the time of preparing the <strong>short paper</strong> presenting the results, findable under <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">10.5281/zenodo.14170751</a> [1]).</p> <p><br>Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.</p> <p>Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under <a href="https://quest-open-earthsciences.charite.de/">https://quest-open-earthsciences.charite.de/</a>.</p> <p>The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2023. These datasets consist of (i) <strong>"journal_articles_v3.csv"</strong> and (ii) <strong>"non_journal_articles_outputs_v3.csv"</strong>, the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).</p> <p>Data for the dashboard was obtained from the FU university bibliography (<a href="https://frub-berlin.primo.exlibrisgroup.com/">https://frub-berlin.primo.exlibrisgroup.com/</a>), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was <strong>enriched with manually researched information</strong>. Data enrichment was different for journal articles and for non-journal-article publications. For <strong><em>journal articles</em></strong>, <em>copyright/open licence</em> information was added, and <em>open access category</em> information was checked and added or corrected. For <strong><em>non-journal-article outputs</em></strong>, missing <em>PIDs</em> were added and <em>open access category</em> information was checked and added or corrected. </p> <p>The "<em>data_dictionary_earth_sciences_v3.csv"</em> table documents all variables of each data file containing here.</p> <p>Both for the dashboard, and in our following publications, we categorized <strong>"bronze"</strong> OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (<a href="https://quest-dashboard.charite.de/#tabStart">https://quest-dashboard.charite.de/#tabStart</a>) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under <a href="https://doi.org/10.5281/zenodo.1099821" target="_blank" rel="noopener">10.5281/zenodo.1099821</a>9 [2]</p> <p> </p> <p>[1] Duine, M., Iarkaeva, A., & Hübner, A. (2024, November 15). Initiating discipline-specific Open Science Monitoring with the Open Science Dashboard for Earth Sciences. 28th International Conference on Science, Technology and Innovation Indicators (STI2024), Berlin, Germany. <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14170751</a><br>[2] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. <a href="https://doi.org/10.5281/zenodo.10998219">https://doi.org/10.5281/zenodo.10998219</a></p>
data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation
<p>This is supporting data and software code for the following preprint in bioRxiv</p> <p><strong>Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation</strong></p> <p>https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1</p>
musiXplora: Persistent Datasets on Zenodo
<p>This Dictionary provides a structured overview of musiXplora data and their latest and concept DOIs, which always leads to the latest version. It can be accessed directly as a JSON and is being updated regularly, as soon as any musiXplora data on Zenodo was updated. Consider reading the <a href="https://doi.org/10.5281/zenodo.11582199" target="_blank" rel="noopener">Documentation</a> for Retrieval Examples, and how to access the latest version of this dictionary as well without the latest DOI.</p> <p>The structure of this dictionary is simple: <strong>musiXplora-ID: JSON-Data</strong></p> <p> </p> <p>For further questions or requests, please refer to: redaktion@musixplora.de</p> <p>Version of Dictionary: 0.0.1 (11 June, 2024)</p>
Dataset and Source Code for the Paper: A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms
<p>Here are the data set and source code related to the paper: "A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms"</p> <p>1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actors and https://malpedia.caad.fkie.fraunhofer.de/actors</p> <p>3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>4- countries.zip : contains country name list.</p> <p>5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/</p> <p>6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families</p> <p>7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher</p> <p>8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip</p> <p>9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip.</p> <p>10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.</p> <p>11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:</p> <p>docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use__neo4j__config=true --env=NEO4J_AUTH=none neo4j:5.13.0</p> <h4><strong>web interface: http://localhost:7474</strong></h4> <h4><strong>username: neo4j</strong></h4> <h4><strong>password: neo4j</strong></h4> <p> </p>
Projection of potential future tree cover persistence for 2029 based on the global model
<p>Tree cover persistence projection results for 2029 based on the global model under a business-as-usual scenario.</p>
Global treecover persistence for 2014
<p>Global tree cover persistence for the period 2001–2014 with loss (defined as any change below the 10% threshold of tree cover) subtracted from 1 km tree cover extent map for 2000.</p>
Projection of potential future tree cover persistence for 2029 based on the six regional models
<p>Tree cover persistence projection results for 2029 based on the six regional models under a business-as-usual scenario.</p>
Dataset for Advanced Persistent Threat (APT) Attacks on Power Substation Networks via GOOSE Protocol Exploitation
<p>This dataset captures network traffic from a simulated Advanced Persistent Threat (APT) campaign targeting a power substation's communication network. The attacker maintains a prolonged presence within the network, conducting low-profile scans using Nmap to stealthily discover the network configuration. The focus is on the communication between the Remote Terminal Unit (RTU), the Programmable Logic Controller (PLC), and the Bay Protection Unit, all of which utilize the Generic Object Oriented Substation Event (GOOSE) protocol for critical operations.</p>
SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses
<p>These are the<strong> processed</strong> BCR repertoire bulk sequencing data described in <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (Fig 3b-d; Extended Data Fig 3; Extended Data Table 6). The corresponding <strong>raw</strong> sequencing reads are available on SRA under <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">BioProject PRJNA731610</a>.</p> <p><strong>Summary</strong>: Bulk-sorted total plasmablasts from PBMCs and germinal centre B cells at 4 weeks after primary immunization from 3 vaccinees who had no prior history of infection with SARS-CoV-2. </p> <p><strong>Code: </strong>Code along with Docker container for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2021">found on GitHub</a>.</p> <p><strong>Metadata file</strong>: WU368_turner_et_al_nature_2021_meta.tsv</p> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>mAb = monoclonal antibody</li> </ul> <p><strong>BCR data file</strong>: WU368_turner_et_al_nature_2021_bcr.tsv.gz</p> <p>In addition to the processed bulk sequences, also included are the heavy chains of 37 mAbs that had been validated to be spike-binding and that were used together with the bulk sequences for clonal lineage inference. The mAbs are annotated as "mab" in the "seq_type" column.</p> <p><strong>BCR data column descriptions</strong></p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s are used, as opposed to IMGT-defined "junctions". Non-standard columns are noted below.</p> <ul> <li>v_call_genotyped: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a></li> <li>germline_[vdj]_call: clonal consensus germline sequence reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` using Change-O</a></li> <li>isotype: IGH[ADEGM]</li> <li>cdr3: CDR3 nucleotide sequence</li> <li>cdr3_length: CDR3 nucleotide sequence length</li> <li>cdr3_aa: CDR3 amino acid sequence</li> <li>collapse_count: number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a></li> <li>donor: vaccinee</li> <li>sample: sample ID (arbitrary)</li> <li>timepoint: time point at which sample was collected</li> <li>tissue: tissue from which sample was collected</li> <li>sorting: FACS sorting</li> <li>seq_type: sequence type (mAb or bulk)</li> <li>nuc_RS_19_312: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a></li> <li>nuc_denom_19_312: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".")</li> <li>nuc_RS_freq_19_312: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312)</li> </ul>
Mycobacteroides abscessus subp. bolletii strain associated with a persistent infection (genome assembly and annotation dataset)
<p>This dataset includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the prediction transcripts (.ffn files) and the respective amino acid sequences of the translated CDS sequences (.faa files) of a <strong><em>Mycobacteroides abscessus subp. bolletti </em></strong>strain associated with a persistente infection. (genome anotation was performed using Bakta v1.2.2 https://github.com/oschwengers/bakta)</p> <p>The raw sequence reads were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB57933; Run Accession: ERR10554471).</p>
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