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3,576 results for “strain”
Genome sequence assembly and annotation of MATA and MATB strains of <em>Yarrowia lipolytica</em>
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Complete genome sequence of Picosynechococcus sp. strain NKBG15041c, a fast-growing marine cyanobacterium
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Host suitability and fitness-related parameters in Coptera haywardi ([Hymenoptera]: [Diapriidae]) reared on irradiated Ceratitis capitata ([Diptera]: [Tehritidae]) pupae stemming from the genetic sexing Vienna-8 strain with a temperature-sensitive lethal mutation
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The consolidated and reconciled annotations from all of the WGS strains used in this study
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Data for: Genome editing of an African elite rice variety confers resistance against endemic and emerging Xanthomonas oryzae pv. oryzae strains
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Matlab applications for: A mechanical model to interpret distributed fiber optic strain measurement at displacement discontinuities
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Strains and constructs for: A chimeric nuclease substitutes a phage CRISPR-Cas system to provide sequence specific immunity against subviral parasites
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Data for: EMBER multi-dimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains
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Fig. 11 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 11 Fusarium longipes (InaCC F974). a. Culture grown on PDA; b–c. sporodochia on carnation leaves; d. sporodochial conidiophores; e–f. branched conidiophores; g. falcate-shaped macroconidia; h. microconidia; i. chlamydospores. — Scale bars: b–k = 10 µm.
Fig. 10 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 10 Fusarium kotabaruense (ex-type InaCC F963). a. Culture grown on PDA; b. mycelium on carnation leaves; c–h. conidiophores and conidiogenous cells; i–k. conidia. — Scale bars: b = 200 µm; c–d = 50 µm; e–f, h–k = 10 µm; g = 5 µm.
Fig. 9 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 9 Fusarium sulawense (ex-type InaCC F964). a. Culture grown on PDA; b–c. sporodochia on carnation leaves; d–h. aerial conidiophores and conidiogenous cells; i. aerial conidia; j–k. sporodochial conidiophores and conidiogenous cells; l–m. sporodochial conidia. — Scale bars: b–c = 50 µm; d–g, i–m = 10 µm; h = 5 µm.
Fig. 7 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 7 Fusarium desaboruense (ex-type InaCC F950). a. Culture grown on PDA; b–c. sporodochia on carnation leaves; d–h. aerial conidiophores and conidiogenous cells; i–k. aerial conidia; l. sporodochial conidiophores and phialides; m. sporodochial conidia. — Scale bars: b–d = 20 µm; e–m = 10 µm.
Fig. 8 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 8 Fusarium tanahbumbuense (ex-type InaCC F965). a. Culture grown on PDA; b–c. sporodochia on carnation leaves; d–g. aerial conidiophores and conidiogenous cells; h–i. aerial conidia; j–l. sporodochial conidiophores and conidiogenous cells; m–o. sporodochial conidia. — Scale bars: b–c = 50 µm; d–o = 10 µm.
Fig. 3 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 3 Maximum likelihood tree inferred from the combined cmdA, ITS, rpb2, tef1, and LSU sequence datasets of the Fusarium incarnatum-equiseti species complex (FIESC) including 11 Indonesian isolates (indicated in blue). Bootstrap support values and Bayesian posterior probabilities are given at each node. The tree is rooted to Fusarium circinatum (NRRL 25331) and Fusarium fujikuroi (NRRL 13566).
Fig. 5 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 5 Pathogenicity test of Fusarium spp. that belong to other species complexes. a. Plants before inoculation; b. wilting symptom caused by Fusarium odoratissimum InaCC F856, seven weeks after inoculation; c. control; d. positive control Fusarium odoratissimum (InaCC F856); e. Fusarium proliferatum (InaCC F992); f. Fusarium desaboruense (InaCC F950); g. Fusarium lumajangense (InaCC F872 T); h. Fusarium longipes (InaCC F974); i. FIESC (Indo161); j. Fusarium lumajangense (InaCC F993).
Fig. 2 Maximum likelihood tree inferred from the combined cmdA, tef1 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 2 Maximum likelihood tree inferred from the combined cmdA, tef1, tub, rpb1, and rpb2 sequence datasets of the Fusarium fujikuroi species complex (FFSC) including eight Indonesian isolates (indicated in blue). Bootstrap support values and Bayesian posterior probabilities are given at each node. The tree is rooted to Fusarium nirenbergiae (CBS 744.97) and F. oxysporum (CBS 716.74).
Bioinformatic pipeline: Vast differences in strain-level diversity in the gut microbiota of two closely related honey bee species
<p>This data-set contains the full bioinformatic pipeline used to analyze metagenomic samples in the study "Vast differences in strain-level diversity in the gut microbiota of two closely related honey bee species" (Ellegaard et al. 2020, Current Biology). </p> <p>New metagenomic samples were generated for the study, for which the raw data is available on the NCBI Sequence Read Achive, under accession: PRJNA59809.</p> <p>The data of this submission consist of 9 tar-balls, as further described here below. Download and unpack to view the contents (tar -zxvf filename.tar.gz). For each tarball, all directories contain README.txt files, describing the contents of the directory. Due to size constraints, some intermediate files have been omitted, and some workflows are demonstrated for a subset of the data. However, the full analysis can be reproduced from the raw data, using the provided scripts.</p> <p>All scripts are included within the directories where they were applied. Perl-scripts contain documentation, which can be viewed by typing: "perl script_name.pl -h". For R scripts, the usage is indicated as a comment in the top lines of each script. Note that many of the scripts require specific input-files to be present in the run-directory. Their usage is demonstrated within the workflow directories in bash-scripts (*.sh). Commands used for generating plots and some statistics are given within workflow directories in text-files "R.commands" when applicable.</p> <p>Aside from custom code, the pipeline also utilizes various open-source Software packages, which are detailed in the file "software_dependencies.txt". Note, while many of the scripts will run fast on any computer, some steps of the pipeline are computationally demanding, and will require significant computing time, as well as storage space. When scripts are known to be time-consuming, this is indicated in the script help message.</p> <p>Description of tarballs.</p> <p>raw_data_processing.tar.gz: Describes the quality-control and trimming of raw data, and includes info on the sequencing run.</p> <p>databases.tar.gz: Contains all databases used for analysis, in addition to relevant meta-data.</p> <p>mapping_stats.tar.gz: Contains a file with the number of reads mapped to the honey bee gut microbiota database and the host genomes, for each sample. Bash-scripts are provided, detailing how the mapping was done and quantified.</p> <p>orthologs_phylogenies.tar.gz: Contains the pipeline for inferring orthologous gene-families and core genome phylogenies, as well as scripts for filtering of single-copy core gene families.</p> <p>assemblies.tar.gz: Contains the final de novo metagenome assembly files (contig fasta-files), gener<br> ated for both complete and rarefied read subsets. Bash-scripts detailing the assembly commands are also provided.</p> <p>SDP_validation.tar.gz: Contains the pipeline for metagenomic validation of candidate SDPs. Final output-files, containing the percentage identity of recruited metagenomic ORFs to database core genes, are provided for each candidate SDP. Additionally, a small example dataset is provided, where the intermediate result-files can be viewed.</p> <p>community_profiling.tar.gz: Contains the pipeline for community profiling, i.e. the quantification of individual community members (SDPs) across samples. Final output files are provided, including mapped read coverage on core gene families and corresponding plots. A small bam-file (containing data from a single subset sample), is also provided, in order to demonstrate the pipeline, together with all scripts used.</p> <p>snv_profiling.tar.gz: Contains the pipeline used for SNV profiling, including filtering and analysis. Final filtered vcf-files are provided for each SDP. Analytical output files are also provided, including data on shared SNV fractions, distance matrices, and cumulative curves.</p> <p>metagenomic_ORF_analyses.tar.gz: Contains the pipeline for analysis of metagenomic ORFs. This includes prediction of ORFs, clustering, annotation and functional characterization. ORF sequences, annotation files, and cluster-files are provided.</p>
High strain rate micro-compression for crystal plasticity constitutive law parameters identification
<p>Experimental data and numerical model attached to the article "High strain rate micro-compression for crystal plasticity constitutive law parameters identification" (DOI to be generated soon)</p>
Data from: Host-induced genome instability rapidly generates phenotypic variation across Candida albicans strains and ploidy states
<p>Candida albicans is an opportunistic fungal pathogen of humans that is typically diploid yet has a highly labile genome tolerant of large-scale perturbations including chromosomal aneuploidy and loss-of-heterozygosity events. The ability to rapidly generate genetic variation is crucial for C. albicans to adapt to changing or stressful environments, like those encountered in the host. Genetic variation occurs via stress-induced mutagenesis or can be generated through its parasexual cycle, in which tetraploids arise via diploid mating or stress-induced mitotic defects and undergo nonmeiotic ploidy reduction. However, it remains largely unknown how genetic background contributes to C. albicans genome instability in vitro or in the host environment. Here, we tested how genetic background, ploidy, and the host environment impacts C. albicans genome stability. We found that host association induced both loss-of-heterozygosity events and genome size changes, regardless of genetic background or ploidy. However, the magnitude and types of genome changes varied across C. albicans strain background and ploidy state. We then assessed if host-induced genomic changes resulted in fitness consequences on growth rate and nonlethal virulence phenotypes and found that many host-derived isolates significantly changed relative to their parental strain. Interestingly, diploid host-associated C. albicans predominantly decreased host reproductive fitness, whereas tetraploid host-associated C. albicans increased host reproductive fitness. Together, these results are important for understanding how host-induced genomic changes in C. albicans alter its relationship with the host.</p> <p>IMPORTANCE Candida albicans is an opportunistic fungal pathogen of humans. The ability to generate genetic variation is essential for adaptation and is a strategy that C. albicans and other fungal pathogens use to change their genome size. Stressful environments, including the host, induce C. albicans genome instability. Here, we investigated how C. albicans genetic background and ploidy state impact genome instability, both in vitro and in a host environment. We show that the host environment induces genome instability, but the magnitude depends on C. albicans genetic background. Furthermore, we show that tetraploid C. albicans is highly unstable in host environments and rapidly reduces in genome size. These reductions in genome size often resulted in reduced virulence. In contrast, diploid C. albicans displayed modest host-induced genome size changes, yet these frequently resulted in increased virulence. Such studies are essential for understanding how opportunistic pathogens respond and potentially adapt to the host environment.</p> <p> </p>
Genomic analysis of the diversity, antimicrobial resistance and virulence potential of Campylobacter jejuni and Campylobacter coli strains from a private health care center in Central Chile
<p>Supplementary Dataset for the work entitled "Genomic analysis of the diversity, antimicrobial resistance and virulence potential of Campylobacter jejuni and Campylobacter coli strains from a private health care center in Central Chile".</p> <p>This dataset includes bacterial draft genome sequence reannotations of 69 C. jejuni and 12 C. coli strains, the fasta file containing the nucleotide sequence of <em>Campylobacter</em> pathogenicity genes screened for the virulome analysis and the fasta file for the in-house database for plasmid screening analysis in <em>Campylobacter </em>genomes.</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.