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576 results for “salmonella”
Dynamics of macrophage polarization in Salmonella infection : Raw data
<p>Experimental raw data of the paper "Dynamics of macrophage polarization support <em>Salmonella</em> persistence in a whole living organism", Leiba et al.</p>
Supplementary dataset to publication: Oxford nanopore technologies - a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella, Thomas et al 2023
<p>Bacteria of the genus <em>Salmonella</em> pose a major risk to livestock, the food economy, and public health. <em>Salmonella</em> infections are one of the leading causes of food poisoning. The identification of serovars of <em>Salmonella</em> achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by <em>in silico</em> serotyping has been established as an alternative method for serotyping and the detection of genetic markers for <em>Salmonella</em>. Until now, WGS data generated with Illumina sequencing are used to validate <em>in silico</em> serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 <em>Salmonella</em> strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the <em>in silico</em> serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, <em>in silico</em> serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for <em>Salmonella in silico</em> serotyping and genetic marker detection.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,434 Salmonella enterica isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 8,558-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/8/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,434 <em>Salmonella enterica </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 125 different serotypes are represented in this dataset, with Typhimurium (including monophasic), Enteritidis and Infantis being the most represented ones and, together, corresponding to 56.2% of the dataset.</p> <p>File “Se_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Se_profiles_wgMLST.tsv” corresponds to a tab separated file with the 8,558-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Se_profiles_cgMLST_95.tsv”, “profiles/Se_profiles_cgMLST_98.tsv” and “profiles/Se_profiles_cgMLST_100.tsv” correspond to a 3,261-loci, 3,179-loci and 874-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>S. enterica</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/senterica">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 1,779 samples associated with four BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB16326">PRJEB16326</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB20997">PRJEB20997</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB30335">PRJEB30335</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB39988">PRJEB39988</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,434 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with SeqSero2 v1.2.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31540993/">Zhang et al. 2019</a>). wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 8,558-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/8">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31<sup>st</sup>, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 8,558-loci wgMLST profiles of the 1,434 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 3,261-loci, 3,179-loci and 874-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Salmonella enterica serovar Derby isolated from eggs show genomic and phenotypic traits that may be linked to inability to produce human infection.
<p><em><span>Salmonella enterica</span></em><span> serovar Derby causes foodborne disease (FBD) outbreaks worldwide, mainly from contaminated pork but also from chickens. During a major epidemic of FBD in Uruguay due to <em>S</em>. Enteritidis from poultry, we conducted a large survey of commercially available eggs, where we isolated many <em>S.</em> Enteritidis strains but surprisingly also a much larger number (ratio 5:1) of <em>S</em>. Derby strains. No single case of <em>S</em>. Derby infection was detected in that period, suggesting that the <em>S</em>. Derby egg strains were impaired for human infection. We sequenced fourteen of these egg isolates, as well as fifteen isolates from pork or human infection that were isolated in Uruguay before and after that period, and all sequenced strains had the same sequence type <span>(ST40). Phylogenomic genomic analysis was conducted using more than 3500 genomes from the same sequence type (ST), revealing that Uruguayan isolates clustered into four distantly related lineages. Population structure analysis (BAPS) suggested the division of the analyzed genomes into nine different BAPS1 groups, with Uruguayan strains clustering within four of them. </span>All egg isolates clustered together as a monophyletic group and showed marked differences in gene content with the strains in the other clusters. <span>Differences included the absence of a C-terminal fragment of the <em>speF</em> gene, as well as variations in the composition of mobile genetic elements, such as plasmids, insertion sequences, transposons, and phages, between egg isolates and human/pork isolates.</span></span> <span>Egg isolates showed an acid susceptibility phenotype, reduced ability to reach the intestine after oral inoculation of mice, and reduced induction of SPI-2 <em>ssaG</em> gene, compared to human isolates from other monophyletic groups. Mice challenge experiments showed that mice infected intraperitoneally with human/pork isolates died between 1-7 days p.i., while all animals infected with the egg strain survived the challenge. Altogether, our results suggest that loss of gene functions and the absence of plasmids in egg isolates may explain why these <em>S</em>. Derby were not capable of producing human infection despite being at that time, the main serovar recovered from eggs countrywide.</span></p>
Bioinformatics for public health microbiologists: Module 1 dataset (South African Salmonella)
<p>Module 1 (WGS) dataset (paired-end Illumina reads of <em>Salmonella enterica </em>strains isolated from animals and animal products in South Africa)</p> <ul> <li> <p>module1_dataset_ZAsalmonella.tar.gz: raw Illumina paired-end reads</p> </li> <li> <p>trimmed_reads.tar.gz: trimmed Illumina paired-end reads (i.e., trimmed via fastp v0.23.4)</p> </li> <li> <p>contigs.tar.gz: assembled genomes (i.e., trimmed reads assembled into contigs using SKESA v2.5.1)</p> </li> <li> <p>prokka.tar.gz: whole-genome annotation results (i.e., produced via Prokka v1.14.6)</p> </li> <li> <p>enterobase_salmonella.tar.gz: publicly available assembled genomes (downloaded via Enterobase; https://enterobase.warwick.ac.uk/, accessed 1 June 2024)</p> </li> <li> <p>snippy_input.tsv: input file used for Snippy (https://github.com/tseemann/snippy)</p> </li> <li> <p><span>snippy_final.tar.gz: output files produced by Snippy (https://github.com/tseemann/snippy), Gubbins (https://github.com/nickjcroucher/gubbins), and SNP-sites (https://github.com/sanger-pathogens/snp-sites)</span></p> </li> </ul>
Data associated with "Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors"
<p>Data used for the publication Microbiota-derived metabolites inihibit Salmonella virulent subpopulation development by acting on single-cell behaviors. </p> <p> </p> <p>all_hi_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the presence of SCFAs.</p> <p>all_no_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the absence of SCFAs.</p> <p>odmeasurements.csv OD measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p> <p>gfpmeasurements.csv GFP measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p>
The OHEJP BeONE Project – Salmonella enterica genome assembly dataset
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies of 1,540 <em>Salmonella enterica</em> samples collected by the BeONE Consortium on behalf of the One Health European Joint Programme “BeONE: Building Integrative Tools for One Health Surveillance” (<a href="https://onehealthejp.eu/jrp-beone/">https://onehealthejp.eu/jrp-beone/</a>). Additionally, a complementary dataset is also made available (<a href="https://zenodo.org/record/7119735">https://zenodo.org/record/7119735</a>), comprising genome assemblies of 1,434 <em>S. enterica</em> samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA).</p> <p>File “<strong>BeONE_Se_metadata.xls</strong>x” contains the genome assembly statistics for each isolate, including European Nucleotide Archive accession numbers, in-silico Multi Locus Sequence Type and Serotype, and information regarding year of sampling, country and source.</p> <p>The archive “<strong>BeONE_Se_assemblies.zi</strong>p” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>This anonymized dataset of <em>S. enterica</em> genome assemblies was generated using Next Generation Sequencing data collected within the BeONE Consortium available at the European Nucleotide Archive under BioProject Accession Number <a href="http://www.ebi.ac.uk/ena/browser/view/PRJEB57179">PRJEB57179</a>. Read quality control, trimming and assembly were performed with Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,540 isolates passed the dataset curation step and were included in the final dataset. In-silico serotyping was performed with SeqSero2 v1.2.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31540993/">Zhang et al. 2019</a>).</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. </p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Ionome analysis of Salmonella mutants by Inductively coupled plasma mass spectrometry (ICP-MS)
<p>In many Gram-negative bacteria, the stress sigma factor of RNA polymerase, σS/RpoS, remodels global gene expression to reshape the physiology of quiescent cells and ensure their survival under non-optimal growth conditions. In the foodborne pathogen <i>Salmonella enterica</i> serovar Typhimurium, σS is also required for biofilm formation and virulence.</p><p>We have previously shown that a Δ<i>rpoS</i> mutation affects the <i>Salmonella</i> ionome. Indeed, inductively coupled plasma mass spectrometry analyses have unraveled a significant effect of the Δ<i>rpoS </i>mutation on the cellular concentration of manganese, magnesium, cobalt and potassium, suggesting that σS controls fluxes of ions that might be important for the fitness of quiescent cells (Metaane et al. 2022, PLoS ONE 17(3): e0265511).</p><p>Study: These findings prompted us to evaluate the impact on the<i> Salmonella</i> ionome of deletions of genes encoding the <i>Salmonella</i> Mn2+ transporters (<i>sitABCD</i> and <i>mntH</i>), the Co2+ transporter (<i>cbiMNQO</i> operon) and small proteins of unkown function (<i>yqaE</i> and <i>yqjDEK</i>) that accumulate in quiescent <i>Salmonella</i> under the tight control of σS (Levi-Meyrueis et al. PloS one. 2014; 9(5):e96918, Lago et al. Scientific reports. 2017; 7(1):2127 and Metaane et al. 2022, PLoS ONE 17(3): e0265511).</p><p>Material and Methods: Cell-associated contents of several elements were measured by inductively coupled plasma mass spectrometry (ICP-MS) as previously described in Metaane <i>et al </i>2022 PLoS ONE 17(3): e0265511.Dried cell pellets were prepared by V. Monteil and F. Norel (Institut Pasteur, Université de Paris, CNRS UMR3528, Biochimie des Interactions Macromoléculaires, F-75015, Paris, France). Cell-associated contents of several elements were measured by S. Ayrault and L. Bordier (ICP-MS platform, Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRSUVSQ,Université Paris-Saclay, 91191, Gif-sur-Yvette, France)</p><p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong></p><p><strong>Linked studies:</strong></p><ul><li>NOREL Francoise, MONTEIL Veronique, DOUCHE Thibaut, & MATONDO Mariette. (2023). Global effects of deletions of the sitABCD, mntH, cbiMNQO and corA genes, encoding transporters for manganese, cobalt and magnesium on protein abundance in Salmonella enterica serovar Typhimurium grown to stationary phase in LB. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8279780</li><li>Metaane S, Monteil V, Douché T, Giai Gianetto Q, Matondo M, Maufrais C, Norel F. Loss of CorA, the primary magnesium transporter of <i>Salmonella, </i>is alleviated by MgtA and PhoP-dependent compensatory mechanisms. PloS one. 2023;18(9):e0291736.</li></ul>
Data from: Assessing the contributions of intraspecific and environmental sources of infection in urban wildlife: Salmonella enterica and white ibis as a case study
Conversion of natural habitats into urban landscapes can expose wildlife to novel pathogens and alter pathogen transmission pathways. Because transmission is difficult to quantify for many wildlife pathogens, mathematical models paired with field observations can help select among competing transmission pathways that might operate in urban landscapes. Here we develop a mathematical model for the enteric bacteria Salmonella enterica in urban-foraging white ibis (Eudocimus albus) in south Florida as a case study to determine (i) the relative importance of contact-based versus environmental transmission among ibis and (ii) whether transmission can be supported by ibis alone or requires external sources of infection. We use biannual field prevalence data to restrict model outputs generated from a Latin hypercube sample of parameter space and select among competing transmission scenarios. We find the most support for transmission from environmental uptake rather than between-host contact and that ibis–ibis transmission alone could maintain low infection prevalence. Our analysis provides the first parameter estimates for Salmonella shedding and uptake in a wild bird and provides a key starting point for predicting how ibis response to urbanization alters their exposure to a multi-host zoonotic enteric pathogen. More broadly, our study provides an analytical roadmap to assess transmission pathways of multi-host wildlife pathogens in the face of scarce infection data.
Antimicrobial resistance - Salmonella, E. Coli, prevalence ESBL data
<p>The database contains the evidence presented by the Data Visualization tool (available on EFSA website) accompanying the publication of the 2015 European Union Summary Report on antimicrobial resistance (AMR). Data correspond to occurrence of resistance in Salmonella from animals and humans, occurrence of resistance in E. Coli in animals and prevalence of ESBL-producing E.coli in animals and meat, in EU Member States.</p> <p>Format XLSX; Contact zoonoses_support@efsa.europa.eu (EFSA); FWD@ecdc.europa.eu (ECDC)</p> <p> </p>
Antimicrobial resistance - Salmonella, E. Coli, prevalence ESBL data
<p>The database contains the evidence presented by the Data Visualization tool (available on EFSA website) accompanying the publication of the 2015 European Union Summary Report on antimicrobial resistance (AMR). Data correspond to occurrence of resistance in Salmonella from animals and humans, occurrence of resistance in E.Coli in animals and prevalence of ESBL-producing E.coli in animals and meat, in EU Member States.</p> <p> </p> <p><strong>Format XLSX; Contact zoonoses_support@efsa.europa.eu (EFSA); FWD@ecdc.europa.eu (ECDC)</strong></p>
Differential gene expression in iPSC-derived macrophages after IFNg stimulation and Salmonella infection
<p>We used likelihood ratio test implemented in DESeq2 v1.10.0 (test = “LRT”) to test if a model that allowed different mean expression in each condition explained the data better than a null model assuming the same mean expression across conditions. See the manuscript for more details: http://www.biorxiv.org/content/early/2017/05/18/102392 .</p> <p>We used the following commands in DESeq2:<br> #Run DESeq2<br> dds = DESeq2::DESeqDataSetFromMatrix(combined_expression_data_filtered$counts, design, ~condition_name) <br> dds = DESeq2::DESeq(dds, test = "LRT", reduced = ~ 1)</p> <p>#Extract differentially expressed genes in each condition<br> ifng_genes = results(dds, contrast=c("condition_name","IFNg","naive")) <br> sl1344_genes = results(dds, contrast=c("condition_name","SL1344","naive")) <br> ifng_sl1344_genes = results(dds, contrast=c("condition_name","IFNg_SL1344","naive"))</p>
The genomic and epidemiological virulence patterns of Salmonella enterica serovars in the United States
<p>The serovars of <i>Salmonella enterica </i>display dramatic differences in pathogenesis and host preferences. We developed a process (patent pending) for grouping <i>Salmonella</i> isolates and serovars by their public health risk. We collated a curated set of 12,337 <i>S. enterica</i> isolate genomes from human, beef, and bovine sources in the US. After annotating a virulence gene catalog for each isolate, we used unsupervised random forest methods to estimate the proximity (similarity) between isolates based upon the genomic presentation of putative virulence traits We then grouped isolates (virulence clusters) using hierarchical clustering (Ward's method), used non-parametric bootstrapping to assess cluster stability, and externally validated the clusters against epidemiological virulence measures from FoodNet, the National Outbreak Reporting System (NORS), and US federal sampling of beef products. We identified five stable virulence clusters of <i>S. enterica</i> serovars. Cluster 1 (higher virulence) serovars yielded an annual incidence rate of domestically acquired sporadic cases roughly one and a half times higher than the other four clusters combined (Clusters 2-5, lower virulence). Compared to other clusters, cluster 1 also had a higher proportion of infections leading to hospitalization and was implicated in more foodborne and beef-associated outbreaks, despite being isolated at a similar frequency from beef products as other clusters. We also identified subpopulations within 11 serovars. Remarkably, we found <i>S.</i> Infantis and<i> S.</i> Typhimurium subpopulations that significantly differed in genome length and clinical case presentation. Further, we found that the presence of the pESI plasmid accounted for the genome length differences between the <i>S. </i>Infantis subpopulations. Our results show that <i>S. enterica</i> strains associated with highest incidence of human infections share a common virulence repertoire. This work could be updated regularly and used in combination with foodborne surveillance information to prioritize serovars of public health concern. </p><p>Files contained in this repository will reproduce elements of figures 3,4,6, and 7 of the accompanying PLOS One manuscript. </p><p> </p>
User Guide – Dashboard on Salmonella
<p>The EFSA dashboard on <em>Salmonella</em> is a graphical user interface for searching and querying the large amount of data collected each year by EFSA from EU Member States and other reporting countries based on Zoonoses Directive 2003/99/EC. The <em>Salmonella</em> dashboard shows summary statistics for the monitoring results of the pathogen with regard to major food categories and animal species, <em>Salmonella</em>-positive official samples in the context of food safety criteria and process hygiene criteria, the occurrence of <em>Salmonella</em> in major food categories and the achievement of <em>Salmonella</em> reduction targets in poultry populations. The <em>Salmonella</em> data and related statistics can be displayed interactively using charts, graphs and maps in the online EFSA dashboard. The main statistics can also be viewed and downloaded in tabular format. Detailed information on the use and features of the <em>Salmonella</em> dashboard can be found in the present user guide that can also be downloaded from the online tool.</p>
Salmonella, Shiga toxin-producing Escherichia coli O157:H7 and Listeria monocytogenes numbers during dry-aging of beef loins
<p>This dataset contains bacterial count data and loin characteristics from an experimental study assessing the survival/growth of <em>Salmonella</em>, <em>Escherichia coli</em> O157:H7 and <em>Listeria monocytogenes</em> during dry-aging of beef loins, after artificial inoculation. </p> <p>Four different csv files are provided with tabular data. A detailed description of the data is provided in the readme file.</p> <p> </p>
EFSA cgMLST gene lists for Escherichia coli and Salmonella enterica chewieNS schema
<p>Annex A contains the list of cgMLST loci of <em>Escherichia coli</em> and <em>Salmonella enterica</em> used in the cgMLST analysis in the EFSA One Health WGS System.</p>
Global Salmonella plasmids - Supplemental Data
<p>Sequence data and analysis tool results from a large-scale plasmid characterization from global Salmonella data</p>
Supplementary information for yqiC and global transcriptome in Salmonella
<p>Supplementary information (Additional files 1-22, including 3 files, Table S1-S9, Fig. S1-S10) in the article entitled "Effects of colonization-associated gene <em>yqiC</em> on global transcriptome, cellular respiration, and oxidative stress in <em>Salmonella </em>Typhimurium"</p>
Salmonella Enterica Centrifuge DB
<p>Plascope DB for Salmonella Enterica. It can be used to perform binary classification of plasmid contigs.</p>
The Supplementary Material for the article entitled "Comparative analysis of global transcriptomes in nontyphoidal Salmonella clinical isolates from pediatric patients with and without bacteremia after infecting human intestinal epithelium in vitro"
<p>The Supplementary Material (Additional files 1-5, including Table S1-S4 and Figure S1) for this article.</p> <p> </p> <p><strong>Table S1.</strong> Upregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S2.</strong> Downregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S3. </strong>The enriched GO terms in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S4. </strong>The enriched KEGG pathways in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Figure S1. </strong>The enriched GO terms and KEGG pathways in Group B relative to Group A. Bar charts show the enriched GO terms (A) and the enriched KEGG pathways (B) by significance power. Color of bars indicate power of significance and length in x axes of bar indicate number of annotated genes in the particular term of pathway. Cnetplots show the relationship between GO term (C) and KEGG pathways (D). Dot size representing GO terms and KEGG pathways indicates number of significantly changed and its annotated genes. The GO terms or KEGG pathways connected through their common and annotated genes. </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.