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2,212 results for “virality”
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>
Twitter Crawling for "Viral or Heboh" News
<p>The data we take is about tweet form 10 official account of news portal on twitter that mentioned word "Viral" or "Heboh". The dataset contain 5 columns and more than 500 tweet sorted by time.</p>
Aggregated Gut Viral Catalogue (AVrC)
<p>Despite the importance of the gut virome in human health and disease, identifying viral sequences from metagenomic datasets remains computationally challenging. Up to 99% of viral reads lack significant alignments to known viral genomes due to underrepresentation in reference databases. Recent machine learning tools can detect novel viral sequences based on features like k-mer composition or genomic signatures, but are limited to classifying assembled contigs into simplistic viral/non-viral categories. Several large-scale efforts have mined human gut metagenomes to establish viral catalogues, including the Gut Virome Database (33,242 viral OTUs), Cenote Human Virome Database (45,033 OTUs), and Gut Phage Database (142,809 OTUs). However, these catalogues have not been consistently compared for quality, diversity, and completeness. There is an unmet need to harmonize available gut viral sequences into a unified resource for comparing novel viruses against previous efforts. The Aggregated Gut Viral Catalog (AVrC) addresses this gap by harmonizing and aggregating previous mining efforts into a comprehensive resource to allow for the exploration of the Human gut viral diversity and the easier comparison of newly discovered viral sequences.</p>
Viral Pneumonia Classification Using Machine and Transfer Learning Techniques
<p>Pneumonia is considered a deadly and harmful disease throughout the world. Pneumonia can be lethal if not treated promptly with antibiotics. As a result, early detection of pneumonia increases the likelihood of recovery and lowers mortality. X-rays are one of the most important diagnostic tools for pneumonia. Because of its lower diagnostic costs, the chest X-ray is routinely used to diagnose various lung illnesses. Indeed, diagnosis can be subjective for various reasons, including disease presentation, which might be confusing in chest X-ray images or misdiagnosed as another condition. As a result, the employment of chest X-rays for the diagnoses of pneumonia disease is considered a way forward to fight the challenges being faced with during the examination process and expert readings of results. The dataset comprises 1,067 Pneumonia Chest X-ray images that were curated from the Hopskin Diagnostic Center Nigeria for Research Purposes. This was used to classify Pneumonia disease for pneumonia class encoding. The result yield Pneumonia Disease with High Accuracy, precision and Recall. </p>
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
Integrative structure determination of PTBP1-viral IRES complex in solution
<p>Ensemble structure model of the RNA-binding protein PTBP1 in complex with the internal ribosome entry site (IRES) of encephalomyocarditis virus (EMCV) RNA and data underlying these models.</p> <ul> <li>Main ensemble based on all restraints (corresponding to Figure 2 in the associated paper)</li> <li>Ensemble obtained with only DEER distance distribution restraints corresponding to Figure S7(A) in the Supplementary Material of the associated paper</li> <li>Validation ensemble obtained with all restraints after removing the conformers of the main ensemble from the raw ensemble corresponding to Figure S7(B) inthe Supplementary Material of the associated paper</li> <li>Ensemble obtianed with all restraints by fitting populations with a non-negative linear least squares (NNLLSQ) approach corresponding to Figure S8(A) in ths Supplementary Material of the associated paper</li> <li>Primary DEER-EPR data underlying site-to-site distance distributions for 35 spin-label pairs and corresponding distanace distributions</li> <li>Small-angle neutron scattering (SANS) curves a two detector distances with corresponding resolution files and a small-angle x-ray scattering (SAXS) curve</li> <li>Restraint file for the ensemble fit with MMMx software, specifying the mean distances and standrad deviations of distance distributions that were also used for specifying lower and upper distance bounds in CYANA generation of the raw ensemble</li> <li>Source data for the figures in the associated paper</li> <li>Source data for the tables in the associated paper</li> </ul> <p>All ensembles are ZIP files containing single PDB files for all conformers and an ensemble specification that reports populations for all conformers.</p>
Long-term nitrogen enrichment mediates the effects of nitrogen supply and co-inoculation on a viral pathogen
Host nutrient supply can mediate host–pathogen and pathogen–pathogen interactions. In terrestrial systems, plant nutrient supply is mediated by soil microbes, suggesting a potential role of soil microbes in plant diseases beyond soil-borne pathogens and induced plant defenses. Long-term nitrogen (N) enrichment can shift pathogenic and non-pathogenic soil microbial community composition and function, but it is unclear if these shifts affect plant–pathogen and pathogen–pathogen interactions. In a growth chamber experiment, we tested the effect of long-term N enrichment on infection by Barley Yellow Dwarf Virus (BYDV-PAV) and Cereal Yellow Dwarf Virus (CYDV-RPV), aphid-vectored RNA viruses, in a grass host. We inoculated sterilized growing medium with soil collected from a long-term N enrichment experiment (ambient, low, and high N soil treatments) to isolate effects mediated by the soil microbial community. We crossed soil treatments with a nitrogen supply treatment (low, high) and virus inoculation treatment (mock-, singly-, and co-inoculated) to evaluate the effects of long-term N enrichment on plant–pathogen and pathogen–pathogen interactions, as mediated by N availability. BYDV-PAV incidence (0.96) declined with low N soil (to 0.46), high N supply (to 0.61), and co-inoculation (to 0.32). Low N soil mediated the effect of N supply on BYDV-PAV: instead of N supply reducing BYDV-PAV incidence, the incidence increased. In addition, ambient and low N soil ameliorated the negative effect of co-inoculation on BYDV-PAV incidence. BYDV-PAV infection only reduced chlorophyll when plants were grown with low N supply and ambient N soil. Soil inoculant with different levels of long-term N enrichment had different effects on host–pathogen and pathogen–pathogen interactions, suggesting that shifts in the structure and function of soil microbial communities with long-term N enrichment may mediate disease dynamics.
COG-UK Viral Genome Sequences
<p>COG-UK Consortium has published dataset contains over 10K SARS-CoV-2 viral genome sequences available as open access. The current COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents a major threat to health in the UK and globally. To fully understand the transmission and evolution of the virus requires sequencing and analysing viral genomes at scale and speed. The numbers of samples calls for a rapid increase in the UK’s pathogen genome sequencing capacity rapidly and robustly. To provide this increased capacity to collect, sequence and analyse the whole genomes of virus samples in the UK, the COVID-19 Genomics UK (COG-UK) consortium is pooling the world-leading knowledge and expertise in genomics of the four UK Public Health Agencies, multiple regional University hubs, and large sequencing centres such as the Wellcome Sanger Institute.</p> <ul> <li>Protocols: https://www.cogconsortium.uk/protocols/</li> </ul>
Viral Metagenomes from water systems in the Mediterranean Sea
<p>The Water Framework Directive (WFD; 2000/60/EC) is the European umbrella for the assessment and regulation of ecological quality of water systems (lakes, rivers, transitional waters, coastal waters). The scope of WFD is to improve the ecological status of the aquatic ecosystems. To do so, a long time-series of monitoring campaigns within WFD exists with the ultimate goal to protect coastal ecosystems from degradation. Further, WFD project employs the calculation and improvement of ecological quality indices for the definition and assessment of eutrophication. In the submitted sub-project within WFD, the viral metagenome of 15 samples collected in 2014 and 2015 is sequenced and analyzed for several genes for the study of taxonomy and potential function of double-stranded DNA viruses.</p>
What do studies in wild mammals tell us about human emerging viral diseases in Mexico? database
<p>The database used in the article "<strong>What do studies in wild mammals tell us about human emerging viral diseases in Mexico?</strong>". It contains all available records of viral zoonotic and potential zoonotic species in Mexican wild mammals.</p> <p>The first file is a .csv file and the second one is .xls</p>
Characteristics of human and viral RNA binding sites and site clusters recognized by SRSF1 and RNPS1
<p>This dataset was developed for the following article:</p> <p> Rogan PK, Mucaki EJ and Shirley BC. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections [version 1; peer review: awaiting peer review]. <em>F1000Research</em> 2020, <strong>9</strong>:943 (<a href="https://doi.org/10.12688/f1000research.25390.1">https://doi.org/10.12688/f1000research.25390.1</a>)</p> <p><strong>Section 1. Extended Data Tables</strong></p> <p>This archive contains the extended data tables for the research article "A proposed mechanism for molecular pathogenesis of severe RNA-viral pulmonary infections". These tables provide SRSF1, RNPS1 and hnRNP A1 binding site and information-dense cluster counts across various RNA viral genomes [including multiple SARS-CoV-2 and influenza strains] and the human transcriptome, the estimated SARS-CoV-2 doubling time necessary for viral genome SRSF1 binding site availability to exceed sites within the host transcriptome, and an analysis of influenza, dengue, and aplastic anemia patients misdiagnosed as irradiated by established radiation gene signatures.These tables are:</p> <p><strong>Section 1 - Table 1.</strong> RNPS1 and hnRNPA1 binding sites and Information-Dense Clusters for RNPS1 and<br> hnRNPA1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2A.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 1) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2B.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 2) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2C.</strong> Detailed Analysis of Information-Dense Clusters for RNPS1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2D.</strong> Detailed Analysis of Information-Dense Clusters for hnRNP A1 in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 3.</strong> Binding Site Analysis of Multiple Coronavirus Strains (Both Strands)<br> <strong>Section 1 - Table 4A.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Negative Strand Only)<br> <strong>Section 1 - Table 4B.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Both Strands)<br> <strong>Section 1 - Table 5.</strong> SRSF1, RNPS1 and hnRNPA1 Binding Sites and Information-Dense Clusters by Gene<br> <strong>Section 1 - Table 6A.</strong> Transcriptome-Wide Information Dense Clusters Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6B. </strong>Exome-Wide Information Dense Clusters within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 6C.</strong> Transcriptome-Wide Scan of Strong Binding Sites Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6D. </strong>Exome-Wide Scan of Strong Binding Sites within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 7.</strong> Rate of False Positives for Influenza, Dengue Virus and Aplastic Anemia Using<br> Radiation Signatures<br> <strong>Section 1 - Table 8.</strong> Radiation Model Genes Contributing to False Positives for Patients with Influenza A,<br> Dengue Virus, and Aplastic Anemia<br> <strong>Section 1 - Table 9A.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Positive-Strand Sites Only)<br> <strong>Section 1 - Table 9B.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Both Strands Considered)</p> <p><strong>Section 2. All SRSF1, hnRNPA1 and RNPS1 binding site tracks for human and viral genomes</strong></p> <p>We provide bedgraph tracks which provide the location and strength of binding sites (and binding site clusters) for SRSF1, RNPS1 and hnRNPA1 across the human transcriptome (GRCh37), the human exome (including +/-300nt surrounding the exon; non-intergenic only), and for all viral genome investigated in this study (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [two strains]). Note that if no clusters were found for a particular viral genome, a file for said genome will not be present in the Zenodo archive.</p> <p>Folder “Cluster-to-DRIPseq-Intersection-Tracks” contain tracks which indicate where binding site clusters have been identified, intersected with DRIP-seq and DRIPc-seq intervals which indicate where there is evidence of R-Loop formation in the human genome. The DRIP-seq dataset (GSE68845) is not strand specific. DRIPc-seq (GSE70189) is strand specific, and has been taken into account in the intersection (e.g. tracks only list positive strand clusters found in positive-strand DRIPc-seq intervals).</p> <p>Due to sheer size, the human transcriptome and exome tracks which indicate the location of individual binding sites are split into two separate files (separated by strand). While the custom tracks containing human binding site information are designed to be uploaded to the UCSC Genome Browser, files containing transcriptome-wide binding site information may be too large to be uploaded and may require further filtering (i.e. by chromosome).</p> <p>To be classified as a cluster, binding sites on the same strand must have <em>Ri</em> values which sum to >50 bits, each binding site must have a neighboring site within 25nt, and all binding sites in the cluster must have <em>R<sub>i</sub></em> greater than a minimum bit threshold. For human transcriptomes and exomes, this bit minimum was set to <em>R<sub>sequence</sub></em>. The bit minimum for viral binding sites was set to 0.1 * <em>R<sub>sequence</sub></em>. The information density-based clustering algorithm utilized in this work is described in Lu and Rogan 2018 (<a href="https://f1000research.com/articles/7-1933/v2">https://f1000research.com/articles/7-1933/v2</a>) and archived source code is available through Zenodo (<a href="https://dx.doi.org/10.5281/zenodo.1892051">https://dx.doi.org/10.5281/zenodo.1892051</a>).</p> <p><strong>Section 3. Binding site clusters - lollipop plots</strong></p> <p>Lollipop plots present the genomic coordinates and information densities of clusters across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). The height of the "lollipop" corresponds to the information density of a cluster. Labels above "lollipops" present the start and end genomic coordinate (GRCh37) of the cluster followed by the number of sites in the cluster enclosed in brackets. Lollipop plots associated with human transcriptomes/exomes each contain a single gene. Influenza has 8 segments and each segment requires its own plot, other viral genomes examined are presented in a single plot.</p> <p>File naming convention for human plots:</p> <ul> <li>RBP_Gene.png</li> <li>e.g. RNPS1_ADK.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>Virus[.InfluenzaSegment].RiThreshold.Strand.RBP.png</li> <li>e.g. Wuhan-Hu-1.complete-genome.4.2-bits.PosStrand.hnRNPA1.png</li> </ul> <p>The specified Ri threshold indicates all binding sites which comprise a cluster have <em>R<sub>i</sub></em> greater-than or equal to the threshold.</p> <p><strong>Section 4. Ri(b,l) matrices for all binding sites scanned</strong></p> <p>The information theory-based position weight matrices for the following RNA binding proteins (RBP) used in this study: SRSF1, hnRNPA1 and RNPS1. We investigated binding using two different RNPS1 binding models. While similar, these two models contained binding site information on opposing sides of the binding site motif which is why we found it prudent to scan with both models.</p> <p>Structure of each file:</p> <p>Line #1: Start position, End position and<em> R<sub>sequence</sub></em> [average strength of sequences used to generate the model]</p> <p>Subsequent lines describe the information on each position of the binding site:</p> <ul> <li>First four columns: <em>R<sub>i</sub></em> contribution of nucleotide at this position of the matrix [A, C, G, T]</li> <li>Row #5: Position of the matrix</li> <li>Last four columns: Number of binding sites used to generate model with a particular nucleotide at this position of the matrix [A, C, G, T]</li> </ul> <p>Example:</p> <p>-2.965775 1.282153 0.034225 -4.906891 0 1 19 8 0</p> <p>At zero position of the matrix (first nucleotide), a ‘C’ would have a positive contribution to binding site strength, a ‘G’ would be relatively neutral, and an ‘A’ or ‘T’ would negatively contribute to binding site strength.</p> <p>Generation of R<sub>i</sub>(b,l) matrices and computation of <em>R<sub>i</sub></em> values and can be accomplished by utilizing the Delila package (<a href="https://alum.mit.edu/www/toms/delila/delilaprograms.html">https://alum.mit.edu/www/toms/delila/delilaprograms.html</a>).</p> <p><strong>Section 5. Ri and intersite distance - histograms</strong></p> <p>Two sets of histograms present <em>R<sub>i</sub></em> distribution and intersite distance distribution across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). </p> <p>File naming convention for human plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Human-[DRIPc]-AllChrs-RBP[-RiThreshold].png</li> <li>e.g. IntersiteDistances500-Human-AllChrs-hnRNPA1-4.6-bits.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Strand-RBP-Virus[.InfluenzaSegment][-RiThreshold].png</li> <li>e.g. IntersideDistances1000-PosStrandOnly-SRSF1-top50000sitesReplicate1-HIV-1-Strain-B.png</li> </ul> <p>Intersite distance thresholds of 500 or 1000 were assigned for all intersite distance histograms. Any distances above the corresponding threshold were excluded from the plot. Plots presenting <em>R<sub>i</sub></em> distributions contain a dashed line indicating <em>R<sub>sequence</sub></em> if it is visible within the scope of the plot.</p> <p><strong>Section 6. Perl Scripts and Descriptions</strong></p> <p>This archive contains all Perl scripts discussed in this archive's associated manuscript and a document file which describes them ("Perl-Script-Descriptions-Page.docx"). The programs and their general functions are as follows:</p> <p>“ClusterToDRIPseqAnalysisProgram.pl” – reports which information-dense clusters are located within DRIPc- and/or DRIP-seq intervals (individually and by gene)</p> <p>“ClusterToDRIPseqAnalysisProgram.GeneDensityFinder.pl” – uses the output from script “ClusterToDRIPseqAnalysisProgram.pl” to determine the number and the density of information-dense clusters within a gene (total clusters within the gene and those within DRIPc-seq intervals)</p> <p>“calculateIntersiteDistance.pl” – determines the distance between all binding sites in the same gene from a list of genomic coordinates</p> <p>“removeOutliersHigherThanN.pl” – discards intersite distances computed by script “calculateIntersiteDistance.pl” that are greater than a specified threshold</p> <p>“getStatisticsOnCol.pl” – calculates the count, geometric mean, median, arithmetic mean, and standard deviation of values from the output of script “removeOutliersHigherThanN.pl”</p> <p>“ScanDataSummaryProgram.pl” – determines the number of binding sites (above a specified <em>R<sub>i</sub></em> threshold) found within known genes (the program also reports the total expression of those genes using external A549 and pneumocyte expression datasets) from binding site coordinate data</p> <p>“TotalBindingSitePerCellCalculator.pl” – estimates the number of binding sites expressed in a single A549 or pneumocyte cell at any given time.</p>
Type III interferons may suppress viral infections by triggering cell death -- Imaging Dataset
<p>This dataset accompanies the article "Type III interferons may suppress viral infections by triggering cell death". Earlier version is available as a preprint, <a href="https://doi.org/10.1101/2024.09.09.612051" target="_blank" rel="noopener">https://doi.org/10.1101/2024.09.09.612051</a>. The updated dataset includes quantifications for Figure 7C and Figure 7D.</p>
PhasAGE Training School 2 - Phase separations and transitions by viral proteins: from viral factories to interference with host cell functions- LECTURE
<p>The Training School 2 “Biomolecular condensates in cell function, aging and disease” is the <strong>second</strong> edition of a series of PhasAGE training activities.</p> <p> </p> <p>The main goal of this training school is to raise awareness and provide expertise on fundamental aspects of phase separation and formation of <strong>biomolecular condensates</strong>, specifically covering the importance of this process to cellular biology and its contribution to the aging process and age-related diseases.</p>
Identifying and profiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with Machaon - Pre-computed features for replication
<p>Machaon's computed features that were used in the structural comparisons with Spike protein.</p> <p>DATA_PDBS_vir_whole_1-3.zip files are parts of a single folder.</p> <p> </p>
Antagonism between viral infection and innate immunity at the single-cell level -- Immunostaining Imaging Dataset
<p>This dataset accompanies the article "Antagonism between viral infection and innate immunity at the single-cell level", at the time of submission available as a <a href="https://doi.org/10.1101/2022.11.18.517110">preprint</a>.</p>
Code and data for manuscript: Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.
<p>This is the source code and data required to reproduce data analysis and figures from the manuscript, "Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir". </p>
Adaptive changes in the genomes of wild rabbits after 16 years of viral epidemics
<p>Since its introduction to control overabundant alien rabbits (Oryctolagus cuniculus), the highly virulent Rabbit Haemorrhagic Disease Virus (RHDV) has caused regular annual disease outbreaks in Australian rabbit populations. Although initially reducing rabbit abundance by 60%, continent-wide, experimental evidence has since indicated increased genetic resistance in wild rabbits that have experienced RHDV-driven selection. To identify genetic adaptations, which explain the increased resistance to this biocontrol virus, we investigated genome-wide SNP (single nucleotide polymorphism) allele frequency changes in a South Australian rabbit population that was sampled in 1996 (pre-RHD genomes) and after 16 years of RHDV outbreaks. We identified several SNPs with changed allele frequencies within or in proximity of genes that have roles potentially important for increased RHD resistance. Many of the identified genes are known to be involved in virus infections or immunity, or had previously been identified as being differentially expressed in healthy vs. acutely RHDV-infected rabbits. Furthermore, we show in a simulation study that the allele/genotype frequency changes cannot be explained by drift alone, and that several candidate genes had also been identified as being associated with surviving RHD in a different Australian rabbit population. Our unique dataset allowed us to identify candidate genes for RHDV resistance that have evolved under natural conditions, and over a time span that would not have been feasible to study in an experimental setting. Moreover, it provides a rare example of host genetic adaptations to virus-driven selection in response to a suddenly emerging infectious disease.</p>
Unbiased metagenomic sequencing complements specific routine diagnostic methods and increases chances to detect rare viral strains
<p>Raw Illumina MiSeq data in zipped FASTQ format.</p> <p>Files are named by sample type and time point (weeks after transplantation).</p>
Supplementary Material: Conformational Ensemble of the Poliovirus 3CD Precursor Observed by MD Simulations and Confirmed by SAXS: A Strategy to Expand the Viral Proteome?
<p>Supplementary video for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(11), 5962-5986; doi:10.3390/v7112919; http://www.mdpi.com/1999-4915/7/11/2919.</p> <p><strong>Movie S1.</strong> Dynamic interface between 3C and 3D domains revealed by accelerated MD. The 3C and 3D domains are colored cyan and blue, respectively. The active-site residues of the protease (His-40, Glu-71, Cys-147) and the polymerase (Asp-416, Asp-511, Asp-512) domains are represented by red spheres to help identifying the relative orientations of two domains.</p>
Host-derived viral transporter protein for nitrogen uptake in infected marine phytoplankton
<p>Dataset for the article "Host-derived viral transporter protein for nitrogen uptake in infected marine phytoplankton", Monier et al.</p> <p>Data for all phylogenetic tree reconstructions (raw and masked protein sequence alignments in fasta format, tree file in newick format) and placement file (jplace format) of two environmental sequences are available.</p> <p>Data for all assay experiments are available: ammonium and urea assays, Omnilog phenotype screening (Nitrogen substrates).</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.