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3,108 results for “pathogen”
Lab disease outcomes data evaluating how antibiotic tolerant vs. non-tolerant cell-free supernatant from Pseudomonas aeruginosa affects the interaction between a fungal pathogen (Batrachochytrium dendrobatidis) and amphibian (Rana sylvaticus), 2022.
Microbes living on hosts and in the environment can play a key role in helping hosts to combat pathogens. However, antibiotic-induced alterations to microbial metabolite production could disrupt this dynamic. Here, we investigated whether antibiotic tolerance influences the anti-pathogenic properties of host-associated (living on the host; biofilms) and environmental (living in the soil of water column; planktonic) microbes in vitro and in vivo. For our model host and pathogen, we used the amphibian (Rana sylvatica)-Batrachochytrium dendrobatidis (Bd) system. For our model host-associated (biofilm) and environmental (planktonic) microbes, we used four strains of Pseudomonas aeruginosa that vary in their tolerance to antibiotics and their biofilm-forming capabilities: Planktonic, non-antibiotic tolerant (ΔsagS/VC); Planktonic, antibiotic tolerant (ΔsagS::sagS_L154A); Biofilm, non-antibiotic tolerant (ΔsagS::sagS_D105A); Biofilm, antibiotic tolerant (ΔsagS::sagS). We collected cell-free supernatants (CFS) from each strain to examine the effects of metabolites. We conducted four experiments. In our pathogen-only exposures to test direct effects of metabolites on Bd, we exposed Bd zoospores to each P. aeruginosa CFS at six concentrations. After 11 days of growth, we measured relative abundance of Bd across each treatment. In our host-only exposures to test effects of metabolites on host disease outcomes, we placed R. sylvatica tadpoles in individual units containing each P. aeruginosa CFS. After 48 hours, water was changed into clean well water (no CFS). Bd zoospores were immediately added to each experimental unit following the water change. After 5 days of Bd exposure, we measured snout-vent length (SVL), mass, developmental stage, and Bd quantification in the mouthparts using qPCR for each tadpole. In our host-pathogen exposures to test interactive effects of metabolites on hosts in the presence of the pathogen, we conducted the same experiment as above. However, ins
Simulated NGS read datasets for bacterial pathogenic potential prediction
<p>## Predicting pathogenic potentials from NGS reads: novel bacterial species</p> <p>This repository contains simulated Illumina read datasets for bacterial pathogenic potential prediction and associated metadata extracted from the IMG Database (https://img.jgi.doe.gov/). The reads are 250bp long and were simulated with Mason (https://www.seqan.de/apps/mason/) from genomes downloaded from NCBI. The training-validation-test split was done on the species level to ensure "novelty" of validation and test species. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. Additional, imbalanced training sets contain 2.5 million "nonpathogenic" and 17.5 million "pathogenic" reads, keeping the mean covarage constant for all species. The temporal benchmark test set contains reads from 3 additional pathogenic species in the Pantoea genus.</p> <p>## Predicting pathogenic potentials from NGS reads: novel strains of known species</p> <p>The BacPaCS datasets contain reads simulated from the dataset compiled by Barash et al. (https://doi.org/10.1093/bioinformatics/bty928). It this case, the training-validation-test split was done on the strain level (so different strains of the same species may be present in all three sets).</p>
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>
Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe
<p>The data in this repository were used to conduct the analysis outlined in the following bioRxiv preprint:</p> <ul> <li>Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley (2025) "Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe" <em>bioRxiv</em> doi:10.1101/2024.07.17.603912</li> </ul> <p>The codes used for the analyses are available at https://github.com/sarahhayes/avian_flu_sdm/ </p> <p>The following lookup table can be used to cross-reference between the variable descriptions in Tables 1 and 2 of the preprint and the files in this repository:</p> <p> </p> <table> <tbody> <tr> <td> <h3> Variable description </h3> </td> <td> <h3> Filename </h3> </td> </tr> <tr> <td> Minimum elevation (metres above sea level) </td> <td> elevation_min_10kres.tif </td> </tr> <tr> <td> Maximum elevation (metres above sea level) </td> <td> elevation_max_10kres.tif </td> </tr> <tr> <td> Difference between minimum and maximum elevation </td> <td> elevation_diff_10kres.tif </td> </tr> <tr> <td> Modal elevation (metres above sea level) </td> <td> elevation_mode_10kres.tif </td> </tr> <tr> <td> Normalised Difference Vegetation Index (NDVI) </td> <td> ndvi_*_quart_2022_eco_rasts.tif </td> </tr> <tr> <td> Land cover </td> <td> landcover_output_full_2022_10kres.tif </td> </tr> <tr> <td> Distance to coast </td> <td> dist_to_coast_10kres.csv </td> </tr> <tr> <td> Distance to inland water </td> <td> dist_to_water_output_10kres.csv </td> </tr> <tr> <td> Relative humidity </td> <td> mean_relative_humidity_q*_10kres_eco_quarts.tif </td> </tr> <tr> <td>Seasonal weighted mean of the month-wise difference in the minimum temperature and maximum temperature (degrees Celsius) </td> <td> mean_diff_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal weighted mean of monthly mean temperatures (degrees Celsius) (Mean monthly temperature for each month calculated using: Mean temperature = Minimum temperature + diurnal range/2)</td> <td> mean_mean_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal temperature variation (degrees Celsius)<br>(Difference between the maximum and minimum of<br>mean monthly temperature values across months<br>majority-represented within the season)</td> <td> variation_in_quarterly_mean_temp_q*_eco_rasts.tif </td> </tr> <tr> <td> Precipitation </td> <td> mean_prec_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal mean of daily zero-degree isotherm (metres<br>above sea level) </td> <td> isotherm_mean_q*_eco_rasts.tif </td> </tr> <tr> <td>Number of days the zerodegree isotherm was below 1 metre at midday at Coordinated Universal Time (UTC) </td> <td> isotherm_midday_days_below1_q*_eco_quarts.tif </td> </tr> <tr> <td> Chicken density </td> <td> chicken_density_2010_10kres.tif </td> </tr> <tr> <td> Duck density </td> <td> duck_density_2010_10kres.tif </td> </tr> <tr> <td> <em>Anatinae</em> (dabbling ducks) </td> <td> anatinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Anserinae</em> (swans and geese) </td> <td> anserinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Ardeidae</em> (herons) </td> <td> ardeidae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Arenaria/Calidris</em> (turnstones and sandpipers) </td> <td> arenaria_calidris_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Aythyini</em> (diving ducks)</td> <td> aythyini_rast_eco_bds.tif</td> </tr> <tr> <td> Laridae (gulls) </td> <td> laridae_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding within 2m of water surface </td> <td> around_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding >2m below water surface </td> <td> below_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet plants </td> <td> plant_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet scavenging </td> <td> scav_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet endothermic vertebrates </td> <td> vend_rast_eco_bds.tif </td> </tr> <tr> <td> Congregative </td> <td> cong_rast_eco_bds.tif </td> </tr> <tr> <td> Migratory </td> <td> migr_rast_eco_bds.tif </td> </tr> <tr> <td> Below threshold phylogenetic distance to known host species </td> <td> host_dist_rast_eco_bds.tif </td> </tr> <tr> <td> Species richness </td> <td> species_richness_rast_eco_bds.tif </td> </tr> </tbody> </table>
The Pathogen-Host Interactions Database, version 4.18
<p>PHI-base is an online database (available at <a href="http://www.phi-base.org">phi-base.org</a>) that catalogues experimentally verified pathogenicity, virulence and effector genes from fungal, oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.</p> <p>Each entry in PHI-base is curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as literature references in which the original experiments are described. Each gene in PHI-base is presented with its nucleotide sequence and deduced amino acid sequence (available in a FASTA file), as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, we have annotated genes using ontologies, controlled vocabularies, and links to external sources (including UniProt, Gene Ontology, Enzyme Commission, NCBI Taxonomy, EMBL, PubMed and FRAC).</p> <p>This PHI-base dataset is a Frictionless Data Package that contains an export of the PHI-base database in CSV format (comma-separated values), plus a FASTA file with sequences for each gene in the database. This version of the dataset, version 4.18, contains 5,828 publications, covering 23,497 pathogen–host interactions and 10,614 pathogen genes across 335 pathogen species and 265 host species.</p>
Genomic Epidemiology Dataset for Important Nosocomial Pathogenic Bacteria Acinetobacter baumannii
<p>The<strong> </strong>infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called 'the clones of high risk' often drive the spread of resistance within particular species. </p><p>Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. </p><p>In this dataset, we present the results of a comprehensive genomic epidemiology analysis of 17,546 genomes of a dangerous bacterial pathogen <i>Acinetobacter baumannii</i>. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), intrinsic<i> blaOXA-51-like</i> gene variants, capsular (KL) and oligosaccharide (OCL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to nine known international clones of high risk. The presence of antimicrobial resistance genes within the genomes is also reported. </p><p>These data will be useful for researchers in the field of <i>A. baumannii</i> genomic epidemiology, resistance analysis and prevention measure development.</p>
Data from Neutral genetic structuring of pathogen populations during rapid adaptation
<p><strong>Datasets and temporary dataframes relating to the article "Neutral genetic structuring of pathogen populations during rapid adaptation".</strong></p> <p>These datasets and temporary dataframes are necessary to run the scripts from the public GitLab repository: <a href="https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation">https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation</a>. Please refer to this public GitLab repository for the latest version of the codes and to perform all analyses presented in the article.</p> <p>Original datasets from the demogenetic model:</p> <ul> <li>Output_RandomDesign.txt</li> <li>Output_RegularDesign_With_host_alternation.txt</li> <li>Output_RegularDesign_Without_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_With_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_Without_host_alternation.txt</li> </ul> <p>All remaining files correspond to temporary dataframes generated by the scripts in the GitLab repository, provided here for reproducibility of the results and to save time at certain time-consuming scripts.</p>
A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen - Supplemental data Fig 4A
<p>Raw data used for Fig. 4A of the article "A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen" published in Cell Host & Microbe. This Western Blot dataset is composed of 4 images:</p> <ul> <li>Western Blot 1: whole cell lysate (raw image, and annotated image)</li> <li>Western Blot 2: purified pili (raw image, and annotated image)</li> </ul>
Dataset: Relation of pest insect-killing and soilborne pathogen-inhibition abilities to species diversification in environmental Pseudomonas protegens
<p>This dataset is related to "<em>Relation of pest insect-killing and soilborne pathogen-inhibition abilities to species diversification in environmental Pseudomonas protegens</em>" and contains all the data obtained from insect experiments and plant-pathogen inhibition assays, as well as the code used for phylogenetic and Biolog anaylsis. </p>
Graphic Illustration of Neal Platt's Talk: Targeted sequencing of pathogen DNA from museum specimens
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Neal Platt at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Kendra Phelp's Talk: A harmonized taxonomic resource is critical for accurately interpreting host-pathogen interactions
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Kendra Phelps at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</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.
liampshaw/Pathogen-host-range: Pathogen-host-range initial code release
<p>Release of code and dataset for publication of associated paper: "The phylogenetic range of bacterial and viral pathogens of vertebrates" (doi: 10.1111/mec.15463).</p>
Data: Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels
<p>This is the experimental data set of Zetzsche, et. al. (2020, Scientific Reports: doi.org/10.1038/s41598-020-77200-0) based on a three-year field trial (2014/15, 2015/16, 2016/7) of 178 German elite winter wheat cultivars.</p> <p>The table (QLB_BRIWECS_WW_fieldtrial_adjustMeans_treatments.csv) subsumes the adjusted mean values of four fungal disease scores (average ordinates) and six yield-related traits investigated at four treatments (T1: 110 kg N ha<sup>-1</sup>, no fungicides; T2: 110 kg N ha<sup>-1</sup> + fungicide; T3: 220 kg N ha<sup>-1</sup>, no fungicides; T4: 220 kg N ha<sup>-1</sup> + fungicide) of two replicates each over three years. Data of each trait are considered independent for all four treatments. Details of the plant material, the experimental site, the trail design as well as the phenotyping of the diseases and agronomical traits are given in the material and methods section of the related publication. Further metadata on the plant material and the trial design are provided in the Supplementary information of the publication.</p>
Simulated NGS datasets for real-time detection of novel pathogens
<p>Datasets based on the <a href="https://doi.org/10.5281/zenodo.3678563">bacterial</a> and <a href="https://doi.org/10.5281/zenodo.4312525">viral</a> simulated NGS datasets. Fastq files correspond to tests sets of those datasets. Basecall files were generated based on the fastq files with an 8nt simulated barcode between the mates of a read pair. The "rn" datasets containg random length subreads (25-250bp) of the original validation and training reads.</p> <p>The Nanopore datasets were resimulated with <a href="https://github.com/liyu95/DeepSimulator">DeepSimulator 1.5</a> (Li et al., 2020) based on the original datasets (i.e. using the same species composition as the original data). The test Nanopore dataset contains full reads (target average length: 8kb) and the training and validation datasets - 250bp subreads.</p>
PHI-base: the Pathogen-Host Interactions Database, version 5.1
<p><strong>Download the dataset here: <a title="Download PHI-base 5.1" href="https://zenodo.org/records/16738930/files/phi-base_v5.1.zip?download=1">phi-base_v5.1.zip</a></strong></p> <p>The Pathogen–Host Interactions Database (PHI-base) is an online database that catalogues experimentally-verified pathogenicity, virulence and effector genes from fungal, oomycete, and bacterial pathogens, which infect animal, plant, fungal, and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.</p> <p>Information in PHI-base is manually curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as references to the literature in which the original experiments are described. Annotations are made using terms from ontologies and controlled vocabularies, including the <a href="https://www.geneontology.org/">Gene Ontology</a> (GO), <a href="https://pubmed.ncbi.nlm.nih.gov/21030441/">Brenda Tissue Ontology</a> (BTO), and the <a href="https://obofoundry.org/ontology/phipo.html">Pathogen–Host Interaction Phenotype Ontology</a> (PHIPO).</p> <p>PHI-base 5 includes data that was curated using a new curation process described in <a href="https://doi.org/10.7554/eLife.84658">Cuzick et. al</a> (2023). Data releases for PHI-base 5 do not use the same schema as data releases from PHI-base 4, but all data records from PHI-base 4 that can be made compatible with the new schema are included with this release. Data releases from PHI-base 4 and PHI-base 5 will occur in parallel until such time that all data from PHI-base 4 can be migrated to PHI-base 5. The PHI-base 4 data releases are available on Zenodo at <a href="https://zenodo.org/doi/10.5281/zenodo.5356870">https://zenodo.org/doi/10.5281/zenodo.5356870</a>.</p> <p>For more information about the planned transition from PHI-base 4 to PHI-base 5, see the <a href="https://phi5.phi-base.org/#/help">Help</a> and <a href="https://phi5.phi-base.org/#/announcements">Announcements</a> page on the PHI-base 5 website.</p> <h2>Release statistics</h2> <p>This version of the PHI-base 5 dataset contains the following types of information:</p> <table style="border-collapse: collapse; border-width: 1px; width: 40.2168%; height: 362.8px;"> <thead> <tr style="height: 19.6px;"> <th style="border-width: 1px; width: 84.846%; height: 19.6px;">Data type</th> <th style="border-width: 1px; width: 15.4054%; height: 19.6px;">Count</th> </tr> </thead> <tbody> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Genes</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">9457</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Interactions</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">31094</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Pathogen species</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">303</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Host species</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">237</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Diseases</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">343</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">References</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">5202</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;"><strong>Annotations</strong></td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;"> </td> </tr> <tr style="height: 10px;"> <td style="border-width: 1px; width: 84.846%; height: 10px;">Pathogen-host interaction phenotype</td> <td style="border-width: 1px; width: 15.4054%; height: 10px;">18260</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Gene-for-gene phenotype</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">452</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Pathogen phenotype</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">9413</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Host phenotype</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">14</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">GO biological process</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">1453</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">GO cellular component</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">85</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">GO molecular function</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">152</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Post-translational modification</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">6</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Physical interaction</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">53</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">WT RNA expression</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">36</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">WT protein expression</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">2</td> </tr> </tbody> </table> <h2>File contents</h2> <ul> <li> <p><strong>phi-base_v5.1.xlsx</strong>: the PHI-base dataset as an Excel spreadsheet. This format follows the layout of the PHI-base 5 website, with sheets corresponding to the sections of gene pages on the website. This format is designed for use by non-technical users.</p> </li> <li> <p><strong>phi-base_v5.1.json</strong>: the PHI-base dataset in JSON format. This is modelled on the export format used by PHI-Canto, the curation tool used by PHI-base. This format is primarily intended for programmatic usage and has additional information (e.g. metadata for curation sessions) that is not included in the spreadsheet format.</p> </li> <li> <p><strong>phi-base.schema.json</strong>: a <a href="https://json-schema.org/">JSON Schema</a> file for the JSON format of the dataset. This is included as documentation for the fields in the JSON file, but can also be used to validate the dataset.</p> </li> </ul>
Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)
<p>Supplemental datasets associated with publication: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (<em>Brassica napus</em>)</p> <p><strong>Abstract</strong></p> <ul> <li>Crops are affected by several pathogens, but these are rarely studied in parallel to identify common and unique genetic factors controlling diseases. Broad-spectrum quantitative disease resistance (QDR) is desirable for crop breeding as it confers resistance to several pathogen species.</li> <li>Here, we use associative transcriptomics (AT) to identify candidate gene loci associated with <em>Brassica napus</em> constitutive QDR to four contrasting fungal pathogens: <em>Alternaria brassicicola</em>, <em>Botrytis cinerea</em>, <em>Pyrenopeziza</em><em> brassicae</em> and <em>Verticillium longisporum. </em>We did not identify any loci associated with broad-spectrum QDR to fungal pathogens with contrasting lifestyles. Instead, we observed QDR dependent on the lifestyle of the pathogen—hemibiotrophic and necrotrophic pathogens had distinct QDR responses and associated loci, including some loci associated with early immunity. Furthermore, we identify a genomic deletion associated with resistance to <em>V. longisporum </em>and potentially broad-spectrum QDR.</li> <li>This is the first time AT has been used for several pathosystems simultaneously to identify host genetic loci involved in broad-spectrum QDR. We highlight constitutively expressed candidate loci for broad-spectrum QDR with no antagonistic effects on susceptibility to the other pathogens studies as candidates for crop breeding. In conclusion, this study represents and advancement in our understanding if broad-spectrum QDR in <em>B. napus </em>and is a significant resource for the scientific community. </li> </ul> <p><strong>Description of data files</strong></p> <p><strong>Full dataset for input into AT analysis </strong>Full datasets (infection phenotypes for <em>A. brassicicola, B. cinerea, </em>or <em>V.longisporum, </em>ROS measurements for chitin, flg22, or elf18) and link to original <em>P. brassicae </em>dataset. These datasets were used for input into the Associative Transcriptomics pipeline (Nichols, 2022, <a href="https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075">https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075</a>). </p> <p><strong>Table S1 </strong>Mean, normalized phenotype data for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). These data were used for association transcriptomic analysis.<strong> </strong></p> <p><strong>Table S2 </strong>Full list of single nucleotide polymorphism (SNP) markers and significance levels from genome-wide association (GWA) analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. The best fit model for GWA analysis is indicated in the tab title. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates SNP location along the chromosome; the y-axis indicates the -log10(p) (P value). Qqplots are included to demonstrate model fit.</p> <p><strong>Table S3</strong> Full list of gene expression markers (GEMs) and significance levels from GEM analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates GEM location along the chromosome; the y-axis indicates the -log10(p) (P value). </p> <p><strong>Table S4 </strong>184 gene expression markers (GEMs) associated with chitin-induced ROS compared with GEMs associated with resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and<em> Verticillium longisporum</em>) and ROS response induced by flg22, and elf18. Lists correspond to Venn diagrams in Fig. 2. The first tab includes all 184 GEMs associated with chitin-induced ROS. The subsequent tabs include lists of shared GEMs associated with chitin-induced ROS response and each additional trait (quantitative disease resistance (QDR) to each fungal pathogen or additional PAMP-induced ROS responses). The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S5</strong> Enrichment analyses to determine if the number of gene expression markers (GEMs) shared between different lists is greater than the number of GEMs that would be expected by chance (e.g., lists of quantitative disease resistance (QDR) GEMs for two fungal pathogens). The representation factor is the number of overlapping GEMs divided by the expected number of overlapping GEMs drawn from two independent groups (traits), considering the total number of GEMs sequenced (53884). A representation factor > 1 indicates more overlap than expected of two groups, a representation factor < 1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups by the number of genes expected for independent groups of genes. </p> <p><strong>Table S6 R</strong>esults from Weighted Co-expression Gene Network Analysis (WGCNA). The first tab indicates significant modules from WGCNA analysis. Black and magenta modules are associated with antagonistic effects on resistance/susceptibility to all four pathogens. The second tab includes a full list of the GEM markers (Table S3), which are in significant WGCNA modules. The third, fourth and, fifth tabs indicate all significant GEMs in the black module, GO terms associated with GEMs in the black module, and all GO terms associated with the black module, respectively. The sixth, seventh and, eighth tabs indicate all significant GEMs in the magenta module, GO terms associated with GEMs in the magenta module, and all GO terms associated with the magenta module, respectively.</p> <p><strong>Table S7 </strong>Shared gene expression markers (GEMs) associated with resistance to different pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>). Lists correspond to matrices and Venn diagrams in Fig. 3. The first tab includes all GEMs associated quantitative disease resistance (QDR) to the fungal pathogens. The subsequent tabs include lists of shared GEMs associated with QDR to two or more fungal pathogens. The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S8 </strong>List of genes in linkage disequilibrium with the top marker for <em>Verticillium longisporum</em> resistance from genome-wide association (GWA) analysis on chromosome A09 (107 genes)(Tab 1) and the homoeologous region on C08 (Tab 2). Their percentage identity and query coverage in <em>Brassica napus</em> reference genotypes Quinta, Tapidor, Westar and Zhongshuang 11 compared to the <em>B. napus</em> pantranscriptome is indicated. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p> </p>
Dataset for publication: An inter-laboratory study characterizes the impact of bioinformatic approaches on genome-based cluster detection for foodborne bacterial pathogens
<p>This dataset is part of a dry-lab interlaboratory study conducted across Germany, regarding bacterial outbreak detection based on NGS data, with a focus on bioinformatic analysis of four species to identify potential variability caused by different data analysis approaches and human interpretation. Participants were asked to follow their usual in-house protocols while adhering to the general guidelines. A quality assessment (with sample exclusion) was followed by 7-gene Multilocus-Sequence Typing (MLST), core genome Multilocus Sequencing Typing (cgMLST), and SNP calling. The participants were then asked to identify clusters. The study was not intended to resemble a standard proficiency test with a passing/failing grade, but rather to investigate and quantify obvious variability in the results and, where possible, the reasons for it. For this purpose, the datasets included borderline cases in terms of quality.</p>
Genomic Typing, Antimicrobial Resistance Gene, Virulence Factor and Plasmid Replicon Dataset for the Important Pathogenic Bacteria Klebsiella pneumoniae
<p>The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called 'the clones of high risk' often drive the spread of resistance within particular species. </p> <p>Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. </p> <p>In this dataset, we present the results of a genomic epidemiology analysis of 61,857 genomes of a dangerous bacterial pathogen <em>Klebsiella pneumoniae</em> obtained from NCBI Genbank database. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), capsular (KL) and oligosaccharide (OL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to clonal groups (CG). The presence of antimicrobial resistance and virulence genes, as well as plasmid replicons, within the genomes is also reported. </p> <p>These data will be useful for researchers in the field of <em>K. pneumoniae</em> genomic epidemiology, resistance analysis and prevention measure development.</p>
Version 4.2 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI)
<p><em>(Version </em>20230306<em>, </em>btmsp files modified May 31, 2023, additional taxonomic information added Dec 27, 2024<em>) </em></p> <p>Version 4.2 (20230306) of the RKI MALDI-ToF mass spectra database represents the third update of the original database (version 20161027, <a href="http://doi.org/10.5281/zenodo.163517">https://doi.org/10.5281/zenodo.163517</a>). The RKI Database v.4.2 now contains a total of 11055 MALDI-ToF mass spectra from 1601 microbial strains of highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as <em>Bacillus anthracis</em>, <em>Brucella melitensis</em>, <em>Yersinia pestis</em>, <em>Burkholderia mallei / pseudomallei</em> and <em>Francisella tularensis</em> as well as a selection of spectra of their close and distant relatives. The database can be used as a reference for the diagnosis of BSL-3 bacteria using proprietary and free software packages for MALDI-ToF MS-based microbial identification. The spectral data are provided as a zip archive (<a href="https://zenodo.org/records/14562231/files/zenodo%20db%20230306.zip?download=1&preview=1">zenodo db 230306.zip</a>) containing the original mass spectra in their native data format (Bruker Daltonics). Please refer to the pdf file (<a href="https://zenodo.org/records/14562231/files/230306-ZENODO-Metadata.pdf?download=1&preview=1">230306-ZENODO-Metadata.pdf</a>) for information on cultivation conditions, sample preparation and details of the spectra acquisition. Please do not try to print this document (>1600 pages!).</p> <p>Version 20230306 of the RKI database contains for the first time files in the <em>btmsp</em> format (e.g. <a href="https://zenodo.org/records/14562231/files/2023-May-23-Bacillus-RKI-Database-568.btmsp?download=1&preview=1">2023-May-23-Bacillus-RKI-Database-568.btmsp </a> <a href="https://zenodo.org/api/files/35e90a0c-653d-4ba4-bf93-50b2bd80d073/2023-May-23-Bacillus-RKI-Database-570.btmsp"> </a>and others). These files were generated using the MALDI Biotyper software (Bruker Daltonics) and contain a total of 1601 main spectra (msp) from the BSL-3 database in the proprietary data format of the MALDI Biotyper software. *.<em>btmsp </em>files can be imported and used for identification with this software solution. Please refer to the manufacturer's manual for details on importing <em>btmsp </em>files. Note that the btmsp file available in database version 4 is broken and cannot be imported.</p> <p>The pkf files (<a href="https://zenodo.org/records/14562231/files/230306_ZENODO_30Peaks_0.75.pkf?download=1&preview=1">230306_ZENODO_30Peaks_0.75.pkf</a>, <a href="https://zenodo.org/records/14562231/files/230306_ZENODO_45Peaks_0.75.pkf?download=1&preview=1">230306_ZENODO_45Peaks_0.75.pkf</a>) represent two versions of the MS peak list data in a Matlab compatible format. The latter data can be imported into MicrobeMS, a free Matlab-based software solution developed at the RKI. MicrobeMS can be used for the identification of microorganisms by MALDI-ToF MS and is available at <a href="https://wiki-ms.microbe-ms.com">https://wiki-ms.microbe-ms.com</a>.</p> <p>The Excel file <a href="https://zenodo.org/records/14562231/files/Taxonomy%20information%20-%20RKI%20MALDI-ToF%20MS%20database%20of%20HPB%20at%20ZENODO%20v.4.xlsx?download=1&preview=1">Taxonomy information - RKI MALDI-ToF MS database of HPB at ZENODO v.4.xlsx</a> contains additional taxonomic information such as a detailed list of bacterial MALDI-ToF mass spectra (sheet #1), overviews on the number of spectra per strain, species or bacterial genus (sheet #2), numbers of strains per species, or genus (sheet #3), etc.</p> <p>The RKI mass spectrometry database is updated regularly.</p> <p>The author would like to thank the following individuals for providing microbial strains and species or mass spectra thereof. Without their help, this work would not have been possible.</p> <ul> <li><strong>Wolfgang Beyer</strong> - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany</li> <li><strong>Guido Werner</strong> - Robert Koch-Institute, Nosocomial Pathogens and Antibiotic Resistances (FG13), Wernigerode, Germany</li> <li><strong>Alejandra Bosch</strong> - CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina</li> <li><strong>Michal Drevinek</strong> - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic</li> <li><strong>Roland Grunow, Daniela Jacob, Silke Klee, Susann Dupke </strong>and <strong>Holger Scholz</strong> - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany</li> <li><strong>Jörg Rau </strong>- Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany</li> <li><strong>Jens Jacob</strong> - Robert Koch-Institute, Hospital Hygiene, Infection Prevention and Control (FG14), Berlin, Germany</li> <li><strong>Martin Mielke</strong> - Robert Koch-Institute, Department 1 - Infectious Diseases, Berlin, Germany</li> <li><strong>Monika Ehling-Schulz</strong> - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria</li> <li><strong>Armand Paauw</strong> - Department of Medical Microbiology, CBRN protection, Universitair Medisch Centrum Utrecht, TNO, Rijswijk, The Netherlands</li> <li><strong>Herbert Tomaso</strong><strong> </strong>– Friedrich-Löffler-Institut (FLI), Federal Research Institute for Animal Health, Jena, Germany</li> <li><strong>Gabriel Karner</strong><strong> </strong>- Karner Düngerproduktion GmbH, Research & Development, Neulengbach, Austria</li> <li><strong>Rainer </strong><strong>Borriss</strong><strong> </strong>- Institute of Marine Biotechnology e.V. (IMaB), Greifswald, Germany</li> <li><strong>Le Thi Thanh Tam</strong><strong> </strong>- Division of Plant Pathology and Phyto-Immunology, Plant Protection Research Institute, Hanoi, Socialist Republic of Vietnam</li> <li><strong>Xuewen</strong><strong> Gao</strong><strong> </strong>- College of Plant Protection, Nanjing Agricultural University, Key Laboratory of Integrated Management of Crop Diseases and Pests, Nanjing, People’s Republic of China</li> </ul> <p>For a detailed description of the database see: Lasch, P., Beyer, W., Bosch, A. <em>et al.</em> A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <em>Sci Data</em> <strong>12</strong>, 187 (2025). <a href="https://doi.org/10.1038/s41597-025-04504-z">https://doi.org/10.1038/s41597-025-04504-z</a></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.