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5,809 results for “virus”
Iceland as stepping stone for intercontinental spread of highly pathogenic avian influenza H5N1 virus between Europe and North America: data set on phylogeographic analysis
<p>Highly pathogenic avian influenza viruses (HPAIV) subtype H5 clade 2.3.4.4b have widely spread within the northern hemisphere since 2020 and threaten wild bird populations as well as poultry production. For the very first time, HPAIV were detected in wild birds and, subsequently, in poultry holdings in Iceland.</p> <p>Here, we present phylogeographic evidence that Iceland has been used as a stepping stone for HPAIV translocation from Northern Europe to North America in 2021 and describe two independent incursions of HPAI H5N1 clade 2.3.4.4b viruses of two different genotypes to Iceland in 2021 and 2022.</p>
Within-plant coexistence of viruses across nitrogen and phosphorus supply rates
Most species can be coinfected by multiple pathogens that may interact through shared resources (i.e., resource competition) or the host immune system (i.e., apparent competition). Community theory developed for free-living organisms suggests that coinfecting pathogens can persist if they satisfy the mutual invasion criterion of coexistence, establishing infections in hosts that are already infected. Furthermore, the likelihood of coexistence may depend on host nutrition which can affect shared resources and host immunity. Here we apply the novel approach of combining a dynamical model and experimental mutual invasibility trials to explore the effects of host nutrient supply on the coexistence of two viral plant pathogens. We focus on among-pathogen interactions mediated by shared resources. First, we used a model to generate hypotheses about how nitrogen (N) and phosphorus (P) supply rates affect the ability of two plant viruses to invade established infections of the other virus. Then, we experimentally manipulated the N and P supplied to oats (Avena sativa) in a growth chamber experiment and tested mutual invasion of two RNA viral pathogens, BYDV-PAV and CYDV-RPV. Nutrient supplies ranged from rates that barely kept hosts alive up to high, but sub-toxic, rates. Model simulations suggested that the viruses were more likely to invade established infections either when they could replicate at lower N and P concentrations or when plant N and P concentrations increased due to a combination of nutrient supply rates and resident virus nutrient use. In the experiment, each virus successfully invaded hosts infected by the other and had consistent growth rates across N and P supply rates. Our results suggest that BYDV-PAV and CYDV-RPV can coexist across a wide range environmental nutrient supply, which is consistent with the high levels of co-occurrence of these two viruses in field populations.
Deep splicing plasticity of the human adenovirus type 5 transcriptome as a driver of virus evolution nanopore data 48hpi
<p>Adenovirus infected MRC5 cells direct RNA sequencing of the mRNA using nanopore. From the paper Deep splicing plasticity of the human adenovirus type 5 transcriptome as a driver of virus evolution. Both the uncorrected fastq files and the lordec corrected files together with the normalised illumina data used to correct the nanpore data are here.</p>
Data for "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".
<p>Epidemiological and mobility data analysed in the paper "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".</p>
Supplementary Datasets for the publication "Rousettus aegyptiacus Fruit Bats Do Not Support Productive Replication of Cedar Virus upon Experimental Challenge"
<p>Cedar henipavirus (CedV), which was isolated from the urine of pteropodid bats in Australia, belongs to the genus Henipavirus in the family of Paramyxoviridae. It is closely related to the Hendra virus (HeV) and Nipah virus (NiV), which have been classified at the highest biosafety level (BSL4) due to their high pathogenicity for humans. Meanwhile, CedV is apathogenic for humans and animals. As such, it is often used as a model virus for the highly pathogenic henipaviruses HeV and NiV. In this study, we challenged eight Rousettus aegyptiacus fruit bats of different age groups with CedV in order to assess their age-dependent susceptibility to a CedV infection. Upon intranasal inoculation, none of the animals developed clinical signs, and only trace amounts of viral RNA were detectable at 2 days post-inoculation in the upper respiratory tract and the kidney as well as in oral and anal swab samples. Continuous monitoring of the body temperature and locomotion activity of four animals, however, indicated minor alterations in the challenged animals, which would have remained unnoticed otherwise.</p>
Supplemental Material to "Tenacity of Animal Disease Viruses on Wood Surfaces Relevant to Animal Husbandry"
<p>Data set for individual titre reduction of viruses over a period of time in multiple experiments.</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1839 (Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures)
Title: Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures <br>Species: Homo sapiens <br>Number of samples: 2705 <br>Number of named analytes: 10 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=61 <br>
CHIKVnext: Molecular epidemiology of Chikungunya virus
<p>CHIKVnext is an interactive resource to study the evolution and global spread of Chikungunya virus (CHIKV), built on the Nextstrain platform.</p>
A Multi-Site Investigation into the Epidemiology of Chikungunya Virus in Neglected Regions of Indonesia
<p>Supporting datasets for phylogenetic analysis of Indonesian chikungunya virus sequences using BEAST v1.10.4.</p> <p> </p>
Virus+ Sequence Masked Mouse Reference Genome (GRCm38)
<p>A version of the mouse genome (<a href="https://www.ncbi.nlm.nih.gov/assembly/327618">GRCm38</a>) masked for all possible viral sequences.</p> <p>See <a href="https://zenodo.org/record/4116107#.X5B7ti9h3UI">Virus+ Masked Human Genome</a> for a masked human reference database.</p> <p>The following commands were used to generate the additional virus sequence masked reference database:</p> <p><strong>1) Download all RefSeq and Neighbor nucleotide records:</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])">https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])</a></p> <p><strong>2) Shred the downloaded viral genomes using shred.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>shred.sh in=refseq_virus_reformated.fasta out=virus_shred.fasta.gz length=85 minlength=75 overlap=30</p> <p><strong>3) Map shredded virus sequence to the GRCm38</strong><strong> genome using bbmap.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmap.sh ref=GRCm38.fa.gz in=virus_shred.fasta.gz outm=map_mouse_all_viruses.sam minid=0.90</p> <p><strong>4) Mask virus sequenced mapped regions from the GRCm38 genome using bbmask.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmask.sh in=GRCm38.fa.gz out=GRCm38_virus_masked.fasta.gz sam=map_mouse_all_viruses.sam</p> <p><strong>5) Remove all N's to further reduce file size using <a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></strong><br> seqkit -is replace -p "n" -r "" GRCm38_virus_masked.fasta.gz > mouse_virus_masked.fasta_Ns_removed.gz</p> <p><strong>Additional References:</strong></p> <ol> <li><a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a></li> <li><a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></li> <li><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">NCBI Virus Genome RefSeq</a></li> </ol>
Virus+ Sequence Masked Human Reference Genome (hg19)
<p>A version of the human genome (hg19) originally masked for ribosomal, plant, animal, fungal and low-entropy sequences by Brian Bushnell (<a href="https://zenodo.org/record/1208052#.X5BuTy9h3UI">Bushnell Masked Human Genome</a>) additionally masked for all possible viral sequences.</p> <p>The following commands were used to generate the additional virus sequence masked reference database:</p> <p><strong>1) Download all RefSeq and Neighbor nucleotide records:</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])">https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])</a></p> <p><strong>2) Shred the downloaded viral genomes using shred.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>shred.sh in=refseq_virus_reformated.fasta out=virus_shred.fasta.gz length=85 minlength=75 overlap=30</p> <p><strong>3) Map shredded virus sequence to the hg19-masked human genome using bbmap.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmap.sh ref=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz in=virus_shred.fasta.gz outm=map_human_all_viruses.sam minid=0.90</p> <p><strong>4) Mask virus sequenced mapped regions from the hg19-masked human genome using bbmask.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmask.sh in=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz out=human_virus_masked.fasta.gz sam=map_human_all_viruses<br> .sam</p> <p><strong>5) Remove all N's to further reduce file size using <a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></strong><br> seqkit -is replace -p "n" -r "" human_virus_masked.fasta.gz > human_virus_masked.fasta_Ns_removed.gz</p> <p><strong>Additional References:</strong></p> <ol> <li><a href="http://seqanswers.com/forums/showthread.php?t=42552">http://seqanswers.com/forums/showthread.php?t=42552</a> for additional information on the original masking of hg19</li> <li><a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a></li> <li><a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></li> <li><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">NCBI Virus Genome RefSeq</a></li> </ol>
MD simulation data: An Entropic Safety Catch Controls Hepatitis C Virus Entry and Antibody Resistance
<p><strong>Background</strong></p> <p>Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. All software is available from http://ambermd.org/. </p> <p><strong>Contents</strong></p> <p>There are three tarball (<strong>.tar.gz</strong>) files containing the <strong>core simulation data</strong>: one for wild type (WT), the second for the I438V A524T mutant and the third for the S449P mutant. Each contains:</p> <p>1. a source PDB (<strong>.pdb</strong>) file</p> <p>2. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.</p> <p>There is an additional tarball containing the <strong>control files</strong> <strong>and scripts</strong> used for running the MD simulations:</p> <p>1. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model</p> <p>2. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps</p> <p>3. control file <strong>prod.ctl</strong> used for the production run </p> <p>4. Executable <strong>run_prod</strong> that was used to perform the production run</p> <p>5. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs</p> <p>6. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs.</p>
Taking Antibiotics for a virus is like trying to sweeten lake Victoria with a bag of sugar
<p>Video in English (see also the Swahili version) warns that attempting to treat a virus or common cold by self-medication with antibiotics can lead to the development of Antimicrobial Resistance (AMR). It uses humour and Tanzanian metaphors for foolishness to make its point. This was originally developed as an audio piece, but still images have been added in post-production to create a video. </p><p>Audio and images produced as part of a Participatory Action Research (PAR) Workshop with young professionals in Mwanza, Tanzania to create public health messages on Antimicrobial Resistance in a post-COVID East Africa in June 2022. The project built upon data gathered in two international, interdisciplinary research projects (HATUA – 'Holistic Approaches to Understanding Antimicrobial Resistance in East Africa' and CARE – 'COVID-19 and Antimicrobial Resistance in East Africa – Impact and Response'), seeking to understand the wider medical and societal drivers of AMR in East Africa and identify possible interventions to curb the spread of AMR. The workshop ran for 9 days over a 3 week period and consisted of focus group style discussions with participants to explore issues surrounding AMR, antibiotic use, and public health messaging awareness in local communities (days 1-2), participant-led design of poster, radio, and video messages with feedback from the research team and introduction to filming/recording equipment (days 3-4), filming, shooting and recording materials within local settings in Mwanza with participants serving as actors, directors, and crew with guidance from research team (days 4-8) and a final in-person review and hands-on feedback of preliminary mock-ups of posters and videos (day 9). Participants have continued to collaborate via email and WhatsApp as materials were finalised. Final video production and editing was undertaken by the researchers. </p><p>Correspondence: mgk@st-andrews.ac.uk; kjf4@st-andrews.ac.uk</p><p> </p>
R Code and Re-analyzed Datasets for: Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses
<p>This submission includes all the scripts and data analyzed in the manuscript "Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses". This manuscript is a technical note on how genome formula data can be analyzed. There are no new experimental data in the manuscript, as published datasets are re-analyzed. Here we reproduce those datasets as formatted for our analysis, for the convenience of the reader. Please consult the README.txt file first.</p> <p>The corresponding paper was published in Viruses <em>16</em>(2): 270. (<a href="https://doi.org/10.3390/v16020270">https://doi.org/10.3390/v16020270</a>).</p> <p>This is the second version of the code, corresponding to the final version of the paper. The intial restricted version for review had a DOI 10.5281/zenodo.10355273.</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>
Supplementary data for Willemsen et al., 2024 "Novel high-quality amoeba genomes reveal widespread codon usage mismatch between giant viruses and their hosts".
<p>Supplementary data for Willemsen et al., 2024 "Novel high-quality amoeba genomes reveal widespread codon usage mismatch between giant viruses and their hosts". The data set consists of five folders: “Codon_usage_amoebae_and_viruses”, "Genome_annotations_amoebae", "Phylogenetic_trees_18S_amoebae", “Phylogenomic_trees_amoebae”, and "Viral_integration_detection_amoebae". The “Codon_usage_amoebae_and_viruses” folder contains for each amoeba host the calculated codon usage tables in the subfolder "codon_usage_table_host", the calculated codon usage preferences using different scores in the subfolder "codon_usage_scores_host", and the calculated codon usage preferences of giant viruses versus each host in the subfolder "codon_usage_scores_viruses_vs_host". The giant viruses in the subfolder "codon_usage_scores_viruses_vs_host" are organised by viral family and genus in separate sub-subfolders. The "Genome_annotations_amoebae" folder contains the generated genome annotations in different formats and the manually curated mitochondrial genome annotations for each amoeba host. The "Phylogenetic_trees_18S_amoebae" contains for the eukaryotic phyla <em>Discosea</em>, <em>Heterolobosea</em>, and <em>Tubulinea, </em>the 18S rRNA nucleotide alignments, distance matrices, and computed phylogenetic trees. The folder "Phylogenomic_trees_amoebae" contains for the eukaryotic clades <em>Amoebozoa</em> and <em>Discoba, </em>the protein alignment matrices and computed phylogenomic trees. The folder "Viral_integration_detection_amoebae" contains the MCP databases used (fasta file, alignment file, HMM profile and DIAMOND BLASTX database) and the MCP sequences detected in this study and the blast results of these. </p>
Latent infection of an active giant endogenous virus in a unicellular green alga
<p>Additional data for Latent infection of an active giant endogenous virus in a unicellular green alga.</p>
VirHunter: a deep learning-based method for detection of novel RNA viruses in plant sequencing data
<p>This storage contains 2 archives: toy datasets to test the training of the VirHunter and weights of the fully trained VirHunter models for 3 host species (peach, grapevine, sugar beet) and for fragment sizes 500 and 1000. .</p> <p>The toy dataset consists of 3 archived files: 'viruses.fasta', 'host.fasta', 'bacteria.fasta'.</p> <p>'viruses.fasta' contains 10000 randomly selected plant viruses from the virus dataset described in the paper.</p> <p>'host.fasta' consists of peach chromosome 2.</p> <p>'bacteria.fasta' consists of 10 bacterial genomes selected randomly: GCF_000284415, GCF_000590555, GCF_001548055, GCF_002795265, GCF_003330825, GCF_003957805, GCF_005845345, GCF_009176625, GCF_010748935, GCF_014681765</p> <p> </p>
Resistance test to bean common mosaic virus (BCMV) in common bean
<p>This video is part of a series of videos prepared by SERIDA partner for the BRESOV project (GA 774244) The video briefly describes a test for resistance to BCMV, a common disease in bean crops</p> <p> </p> <p>https://www.youtube.com/watch?v=ukEVm_yC26Q</p>
Cellular and Humoral Immune Responses after Immunisation with Low Virulent African Swine Fever Virus in the Large White Inbred Babraham Line and Outbred Domestic Pigs
<p>Raw data for manuscript. Contains temperature, clinical scores, qPCR, blood cell numbers and immune responses over time for two groups of pigs immunised with low virulent African swine fever virus and challenged with highly virulent virus. Data for each panel or figure is displayed on a separate worksheet in the file. The readme worksheet contains a brief description of each figure. The majority of data is displayed in an XY table format, with the number of days post immunisation with low virulent virus indicated.</p>
ScienceDex guides
Understand access before you commit
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.