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193 results for “pathogen resistance”
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>
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>
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>
Antibiotic resistant pathogen outbreak investigation: an interdisciplinary module to teach fundamentals of evolutionary biology
<p>The evolution of resistance to antibiotics provides a timely and relevant topic for teaching undergraduate students evolutionary biology. Here, we present a module incorporating modified sequencing data from eight antibiotic resistant pathogen outbreaks in hospital settings with bioinformatics and phylogenetic analyses. This module uses whole genome sequencing data from hospital outbreaks investigated by the Centers for Disease Control and Prevention to provide examples of antibiotic resistance spread. Students work in groups to analyze outbreak data to identify the bacterial species and antibiotic resistance genes, to infer a phylogenetic tree examining relatedness among isolates, and to determine a possible source of the outbreak. Students then compile their results in individual reports and provide recommendations for preventing the further spread of antibiotic resistant organisms. In addition to providing genomic outbreak data, we include a teaching concepts guide discussing three integral components of the module: how evolutionary biology concepts of natural selection and competition impact antibiotic resistance; outbreak investigation information to aid in phylogenetic analysis and creation of recommendations; and instructions for the bioinformatics protocol. Completion of this module provides students an opportunity to think critically about the evolution of resistance, practice bioinformatics techniques, and relate evolutionary biology to current events.</p>
Row sequcenes data for assessing the risks of potential pathogens and antibiotic resistance genes among heterogeneous habitats in a temperate estuary wetland
<p>The study included 118 usable samples within three different habitats (water, soil, and sediment) across the Liaohe River basin to the Red Beach wetland collected from seven papers, and all of the sequence files were uploaded for availability.</p>
Phage selection drives resistance-virulence trade-offs in Ralstonia solanacearum plant pathogenic bacterium irrespective of the growth temperature
<p><span>While temperature has been shown to affect the survival and growth of bacteria and their phage parasites, it is unclear if trade-offs between phage resistance and other bacterial traits depend on the temperature. Here, we experimentally compared the evolution of phage resistance-virulence trade-offs and underlying molecular mechanisms in phytopathogenic <em>Ralstonia</em> <em>solanacearum</em> bacterium at 25 °C and 35 °C temperature environments. We found that experimental growth conditions selected for small colony variants (SCVs) with increased growth rate and mutations in the quorum-sensing (QS) signalling receptor gene, <em>phcS</em>. Interestingly, SCVs were also phage-resistant and reached higher frequencies in the presence of phages in both temperature environments. Evolving phage resistance was costly in terms of reduced carrying capacity, biofilm formation and reduced virulence i<em>n planta</em> possibly due to loss of QS-mediated expression of key virulence genes. We also observed mucoid phage-resistant colonies that showed loss of virulence and reduced twitching motility likely due to parallel mutations in prepilin peptidase gene pilD. Moreover, phage-resistant SCVs from 35 °C-phage treatment had parallel mutations in genes encoding type II secretion system (T2SS) genes (<em>gspE</em> and <em>gspF</em>), indicating that defects in pseudopilus made bacterium resistant to the phage. Additional transcriptomic analysis revealed upregulation of CBASS and type Ⅰ restriction-modification phage defence systems in response to phage exposure, which coincided with reduced expression of motility and virulence-associated genes, including <em>pilD</em> and type II and III secretion systems. Together, these results suggest that phage resistance-virulence trade-offs are not affected by the growth temperature but can be mediated through both pre- and post-infection phage resistance mechanisms.</span></p>
SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae
<p><strong>Parameters</strong></p><p>Force field: AMBER ff19SB</p><p>Water type: TIP3P</p><p>Ions: NaCl </p><p>Ligand topology force field: GAFF2</p><p>Temperature: 298k</p><p>Pressure: 1 bar</p><p>minimization step: 20000 on 5 nanoseconds</p><p>initial velocity is changed by changing "ntx" and "ig"</p><p>C2: ntx = 5 , ig = 8</p><p>C3: ntx = 2 , ig = 5</p><p> </p><p><strong>Uploads</strong>- </p><p>1. Zip file of all 3 main files</p><p>2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>3. Zip file of C1</p><p>4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>5. Zip file of C2</p><p>6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>7. Zip file of C3</p><p>8. Zip and unzip file of <strong>Initial</strong> PDB of complex prior to MD simulation with <strong>Post</strong> MD PDB (C1, C2, C3)</p><p>9. Zip file of <strong>topology</strong> files for C1, C2, and C3</p>
Fig. 1 in Detection of Escherichia fergusonii - an emerging pathogen harbouring drug resistant genes from seafood samples of Tamil Nadu, India
Fig. 1 — Gene specific PCR amplification of Escherichia fergusonii (lane 1 – 100 bp DNA ladder, lane 2 – positive control (clinical E. fergusonii), lane 3 – negative control, lane 4 – E011, lane 5 – E060)
Multispecies coinfections and presence of antibiotics shape resistance and fitness costs in a pathogenic bacterium
<p>Increasing antimicrobial resistance (AMR) poses a challenge for treatment of bacterial diseases. In real life, bacterial infections are typically <span>embedded within complex multispecies communities and influenced by the environment, which can shape </span>costs and benefits of AMR. However, knowledge of such interactions and their implications for AMR <em>in vivo</em> is limited. <span>To address this knowledge gap, we investigated fitness-related traits of a pathogenic bacterium (</span><em>Flavobacterium</em> <em>columnare</em><span>)</span> <span>in its fish host, capturing the effects of bacterial antibiotic resistance, multispecies coinfections</span> <span>(metazoan fluke </span><span><em>Diplostomum</em> <em>pseudospathaceum</em></span><span>), and antibiotic exposure. </span>We quantified real-time replication and virulence of sensitive and resistant bacteria and demonstrate that both bacteria can benefit from coinfection in terms of persistence and replication, depending on the coinfecting partner and antibiotic presence. We also show that antibiotics can benefit resistant bacteria by increasing bacterial replication under coinfection with flukes. These results emphasize the importance of diverse, inter-kingdom coinfection interactions and antibiotic exposure in shaping costs and benefits of AMR, supporting their role as significant contributors to the spread and long-term persistence of resistance.</p>
Multispecies coinfections and presence of antibiotics shape resistance and fitness costs in a pathogenic bacterium
Open the record for dataset details and reuse information.
Phage selection drives resistance-virulence trade-offs in Ralstonia solanacearum plant pathogenic bacterium irrespective of the growth temperature
Open the record for dataset details and reuse information.
Population studies of the wild tomato species Solanum chilense reveal geographically structured major gene-mediated pathogen resistance
<p>Natural plant populations encounter strong pathogen pressure and defense-associated genes are known to be under selection dependent on the pressure by the pathogens. Here we use populations of the wild tomato Solanum chilense to investigate natural resistance against Cladosporium fulvum, a well-known ascomycete pathogen of domesticated tomatoes. Host populations used are from distinct geographical origins and share a defined evolutionary history. We show that distinct populations of S. chilense differ in resistance against the pathogen. Screening for major resistance gene mediated pathogen recognition throughout the whole species showed clear geographical differences between populations and complete loss of pathogen recognition in the south of the species range. In addition, we observed high complexity in a homologues of Cladosporium resistance (Hcr) locus, underlying the recognition of C. fulvum, in central and northern populations. Our findings show that major gene mediated recognition specificity is diverse in a natural plant-pathosystem. We place major gene resistance in a geographical context that also defined the evolutionary history of that species. Data suggest that the underlying loci are more complex than previously anticipated, with small-scale gene recombination being possibly responsible for maintaining balanced polymorphisms in the populations that experience pathogen pressure.</p>
Data from: Experimental evolution of insect immune memory versus pathogen resistance
Under strong pathogen pressure, insects often evolve resistance to infection. Many insects are also protected via immune memory ('immune priming'), whereby sub-lethal exposure to a pathogen enhances survival after secondary infection. Theory predicts that immune memory should evolve when the pathogen is highly virulent, or when pathogen exposure is relatively rare. However, there are no empirical tests of these hypotheses, and the adaptive benefits of immune memory relative to direct resistance against a pathogen are poorly understood. To determine the selective pressures and ecological conditions that shape immune evolution, we imposed strong pathogen selection on flour beetle (Tribolium castaneum) populations, infecting them with Bacillus thuringiensis (Bt) for 11 generations. Populations injected first with heat-killed and then live Bt each generation evolved high basal resistance against multiple Bt strains. In contrast, populations injected only with a high dose of live Bt evolved a less effective but strain-specific priming response. Control populations injected with heat-killed Bt did not evolve priming; and in the ancestor, priming was effective only against a low Bt dose. Intriguingly, one replicate population first evolved priming and subsequently evolved basal resistance, suggesting the potential for dynamic evolution of different immune strategies. Our work is the first report showing that pathogens can select for rapid modulation of insect priming ability, allowing hosts to evolve divergent immune strategies (generalized resistance vs. specific immune memory) with potentially distinct mechanisms.
Quantitative disease resistance in wild Silene vulgaris to its endemic pathogen Microbotryum silenes-inflatae collection sites
<p>Details presented on the collections sites of <em>Silene vulgaris</em> seeds from natural populations, for use in the study, Quantitative Disease Resistance in wild <em>Silene vulgaris</em> to its Endemic Pathogen <em>Microbotryum silenes-inflatae</em>. In this study the seeds were collected a mixed half and full sibling families (seeds from the same maternal plants) and used to produce plants that were then cloned by vegetative cuttings. The infection rates of these clones, as well as of their F1 offspring, by the fungus Microbotryum silenes-inflatae are also presented.</p>
Genetic covariance in immune measures and pathogen resistance in decorated crickets (Gryllodes sigillatus) is sex and pathogen specific
<p>Insects are important models for studying immunity in an ecological and evolutionary context. Yet, most empirical work on the insect immune system has come from phenotypic studies meaning we have a limited understanding of the genetic architecture of immune function in the sexes.</p> <p>We use nine highly inbred lines to thoroughly examine the genetic relationships between a suite of commonly used immune assays (haemocyte count, implant encapsulation, total phenoloxidase activity, antibacterial zone of inhibition, pathogen clearance) and resistance to infection by three generalist insect pathogens (the gram-negative bacterium <em>Serratia marcescens</em>, the gram-positive bacterium <em>Bacillus cereus</em> and the fungus <em>Metarhizium robertsii</em>) in male and female <em>Gryllodes sigillatus</em>.</p> <p>There were consistent positive genetic correlations between haemocyte count, antibacterial and phenoloxidase activity and resistance to <em>S. marcescens</em> in both sexes, but these relationships were less consistent for resistance to <em>B. cereus</em> and <em>M. robertsii</em>. In addition, the clearance of <em>S. marcescens</em> was genetically correlated with the resistance to all three pathogens in both sexes. Genetic correlations between resistances to the different pathogen species were inconsistent, indicating that resistance to one pathogen does not necessarily mean resistance to another. Finally, while there is ample genetic (co)variance in immune assays and pathogen resistance, these genetic estimates differed across the sexes and many of these measures were not genetically correlated across the sexes suggesting that these measures could evolve independently in the sexes.</p> <p>Our finding that the genetic architecture of immune function is sex and pathogen specific suggests that the evolution of immune function in male and female <em>G. sigillatus</em> is likely to be complex. Similar quantitative genetic studies that measure a large number of assays and resistance to multiple pathogens in both sexes are needed to ascertain if this complexity extends to other species.</p>
Quantifying intraspecific variation in host resistance and tolerance to a lethal pathogen
<p>Testing for intraspecific variation for host tolerance or resistance in wild populations is important for informing conservation decisions about captive breeding, translocation, and disease treatment. Here, we test the importance of tolerance and resistance in multiple populations of boreal toads (<em>Anaxyrus boreas boreas</em>) against <em>Batrachochytrium dendrobatidis</em> (Bd), the amphibian fungal pathogen responsible for the greatest host biodiversity loss due to disease.</p> <p>Boreal toads have severely declined in Colorado (CO) due to Bd, but toad populations challenged with Bd in western Wyoming (WY) appear to be less affected. We used a common garden infection experiment to expose post-metamorphic toads sourced from four populations (2 in CO and 2 in WY) to Bd and monitored changes in mass, pathogen burden, and survival for eight weeks. We used a multi-state modeling approach to estimate weekly survival and transition probabilities between infected and cleared states, reflecting a dynamic infection process that traditional approaches fail to capture.</p> <p>We found that WY boreal toads are more tolerant to Bd infection with higher survival probabilities than those in CO when infected with identical pathogen burdens. WY toads also appeared more resistant to Bd with a higher probability of infection clearance and an average of five days longer to reach peak infection burdens. Our results demonstrate strong intraspecific differences in tolerance and resistance that likely contribute to why population declines vary regionally across this species.</p> <p>Our multi-state framework allowed us to gain inference on typically hidden disease processes when testing for host tolerance or resistance. Our findings demonstrate that describing an entire host species as 'tolerant' or 'resistant' (or lack thereof) is unwise without testing for intraspecific variation.</p>
Virulence and antibiotic resistance plasticity of Arcobacter butzleri: insights on the genomic diversity of an emerging human pathogen (genome assembly, annotation dataset, core- and pan-genome loci)
<p>This dataset refers to the analysis of 49 <em>Arcobacter butzleri</em> genomes and includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the predicted transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files), the respective amino acid sequences of the translated CDS sequences (.faa files), the nucleotide alignments of all the 1165 core-genome loci, the nucleotide alignments of the genes <em>hecA</em>, <em>tetR </em>and <em>porA</em>, the categorized amino acid sequences of the six hypervariable regions of PorA, and the nucleotide sequences of the first allele of each of the 7474 pan-genome loci with the respective complete allelic profile matrix.</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB34441).</p>
Data Analysis for Context- and sex-dependent links between sire sexual success and offspring pathogen resistance
<p>Data Analysis for "Context- and sex-dependent links between sire sexual success and offspring pathogen resistance"</p> <p>By Aijuan Liao and Tadeusz J. Kawecki</p>
Pathogen non-planktonic phases within the urinary tract impact early infection and resistance evolution - Data
<p>This upload contains data that is necessary to re-create the figures for the manuscript "<strong>Infection dynamics in the urinary tract - The importance of non-planktonic phases during early infection and resistance evolution</strong>" by Raatz et al. published in The ISME Journal (<a href="https://doi.org/10.1093/ismejo/wrae191">https://doi.org/10.1093/ismejo/wrae191</a>).</p> <p>This manuscript is available as a bioRxiv preprint at https://www.biorxiv.org/content/10.1101/2023.10.23.563535.</p> <p>The related upload with DOI 10.5281/zenodo.10025282 contains the scripts to compute this data set, as well as the scripts for plotting.</p> <p> </p>
Supplementary Data for: Whole genome sequencing elucidates the species-wide diversity and evolution of fungicide resistance in the early blight pathogen Alternaria solani
<p>Supplementary Data for: Whole genome sequencing elucidates the species-wide diversity and evolution of fungicide resistance in the early blight pathogen Alternaria solani</p> <p>This repository contains:</p> <p>SNP call data / VCF file</p> <p>Scripts for all processing steps from mapping up to PCA and phylogenetic analyses (script.ts)<br> Scripts for population genomic analyses with LEA and PopGenome (scripts.SE)<br> All script names are self explanatory.</p>
ScienceDex guides
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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.