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720 results for “Identification data”
Additional data and code for "You can move, but you can't hide: identification of mobile genetic elements with geNomad"
<ul> <li><strong>benchmark_data:</strong> Data used to train and evaluate the classification models.</li> <li><strong>giant_virus_data:</strong> Sequences and metadata of giant viruses identified in public metagenomes.</li> <li><strong>neural_network_training:</strong> Code used to train geNomad's neural network-based classification model.</li> <li><strong>provirus_data:</strong> Data used to train and evaluate the conditional random field model employed by geNomad to identify provirus regions.</li> <li><strong>reference_sequences:</strong> Sequences of chromosomes, plasmids, and viruses that were used to build geNomad's marker dataset and to generate the training data for the classification models.</li> </ul>
Data from: Identification of anti-fungal bioactive terpenoids from the bioenergy crop switchgrass (Panicum virgatum)
<p>Plant derived bioactive small molecules have attracted attention of scientists across fundamental and applied scientific disciplines. We seek to understand the influence of these phytochemicals on functional phytobiomes. Increased knowledge of specialized metabolite bioactivities could inform strategies for sustainable crop production. We hypothesized that – consistent with accumulating evidence that switchgrass genotype impacts microbiome assembly – differential terpenoid accumulation contributes to switchgrass ecotype-specific microbiome composition. An initial in vitro plate-based disc diffusion screen of 18 switchgrass root derived fungal isolates revealed differential responses to upland- and lowland-isolated metabolites. To identify specific fungal growth-modulating metabolites, we tested fractions from root extracts on three ecologically important fungal isolates – <em>Linnemania elongata</em>, <em>Trichoderma</em> sp. and <em>Fusarium</em> sp. Saponins and diterpenoids were identified as the most prominent antifungal metabolites. Finally, analysis of liquid chromatography-purified terpenoids revealed fungal inhibition structure – activity relationships (SAR). Saponin antifungal activity was primarily determined by the number of sugar moieties – saponins glycosylated at a single core position were inhibitory whereas saponins glycosylated at two core positions were inactive. Saponin core hydroxylation and acetylation were also associated with reduced activity. Diterpenoid activity required the presence of an intact furan ring for strong fungal growth inhibition.</p>
Data from: Potential of MALDI−TOF MS-based proteomic fingerprinting for species identification of Cnidaria across classes, species, regions and developmental stages
<p><span>Morphological identification of cnidarian species can be difficult throughout all life stages due to the lack of distinct morphological characters. Moreover, in some cnidarian taxa genetic markers are not fully informative, and in these cases combinations of different markers or additional morphological verifications may be required. Proteomic fingerprinting based on MALDI-TOF mass spectra was previously shown to provide reliable species identification in different metazoans including some cnidarian taxa. For the first time, we tested the method across four cnidarian classes (Staurozoa, Scyphozoa, Anthozoa, Hydrozoa) and included different scyphozoan life-history stages (polyp, ephyra, medusa) into our dataset. Our results revealed reliable species identification based on MALDI-TOF mass spectra across all taxa with species-specific clusters for all 23 analyzed species. In addition, proteomic fingerprinting was successful for distinguishing developmental stages, still by retaining a species specific signal. Furthermore, we identified the impact of different salinities in different regions (North Sea and Baltic Sea) on proteomic fingerprints to be negligible. In conclusion, the effects of environmental factors and developmental stages on proteomic fingerprints seem to be low in cnidarians. This would allow using reference libraries built up entirely of adult or cultured cnidarian specimens for the identification of their juvenile stages or specimens from different geographic regions in future biodiversity assessment studies.</span></p>
ERA5 daily meteorological data (Puget Sound) for weather system identification
<p>This dataset includes the ERA5-based daily meteorological data over the US Puget Sound region. The data includes the following meteorological variables at 850hPa pressure level: temperature (T), relative humidity (RH), horizontal wind vector (U and V), vertical wind (W), and geopotential height (Z).</p> <p>The scripts here are used to establish the weather system classification model as in Chen et al. (submitted). More details, including the complete scripts for analysis/plotting will be updated here after the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, N. Sun, Weather Systems Connecting Modes of Climate Variability to Regional Hydroclimate Extremes. (submitted)</p>
Sample extraction and SNP sequencing data for: Identification of sex-linked SNP markers in wild populations of monomorphic birds
<p><span>Single-nucleotide polymorphism (SNP) analyses are a powerful tool for population genetics, pedigree reconstruction and phenotypic trait mapping. However, the untapped potential of SNP markers to discriminate the sex of individuals in species with reduced sexual dimorphism or of individuals during immature stages remains a largely unexplored avenue. Here, we develop a novel protocol for molecular sexing of birds based on the detection of unique Z- and W-linked SNP markers. Our method is based on the identification of two unique loci, one in each sexual chromosome. Individuals are considered males when they show no calls for the W-linked SNP and are heterozygotic or homozygotic for the Z-linked SNP, while females show both Z- and W-linked SNP calls. We validated the method in the Jackdaw (<em>Corvus</em> <em>monedula</em>). The reduced sexual dimorphism in this species makes it difficult to sex individuals in the wild. We assessed the reliability of the method using 36 individuals of known sex and found that their sex was correctly assigned in 100% of cases. The sex-linked markers also proved to be widely applicable to discriminate males and females from a sample of 927 genotyped individuals of different maturity stages with an accuracy of 99.5%. Given that SNP markers are increasingly used in quantitative genetic analyses of wild populations, the approach we propose has a great potential to be integrated into broader genetic research programmes without the need for additional sexing techniques.</span></p>
Data for: Russian honey bee genotype identification through enhanced marker panel set
<p>Russian honey bees (RHB) are a breeding population developed by USDA-ARS as an effort to provide Varroa-resistant honey bees to beekeepers. The selection strategy for this breeding population was the first in honey bees to incorporate genetic stock identification (GSI). The original GSI approach has been in use for over a decade, and though effective, novel technologies and analytical approaches recently developed provide an opportunity for improvement. Here we outline a novel genotyping assay that capitalizes on the markers used in the GSI as well as novel loci recently identified in a whole genome pooled study of commercial honey bee stocks. Our approach utilizes a microfluidic platform and machine learning analyses to arrive at an accurate, high throughput assay. This novel approach provides an improved tool that can be readily incorporated into breeding decisions towards healthier more productive bees.</p>
Supplementarty Data: Retention time and fragmentation predictors increase confidence in identification of common variant peptides
<p>Supplementary data related to the paper "Retention time and fragmentation predictors increase confidence in identification of common variant peptides"</p> <p>The database directory contains the FASTA files of the four protein sequence databases used in the analysis of the paper.</p> <p>The data directory contains exports with lists of all peptide-to-spectrum matches obtained from the analysis.</p> <p>More information and scripts to reproduce the post-processing steps are available at: https://github.com/ProGenNo/VariantPeptideIdentification</p>
Data-driven Identification for Substance Misuse
ClinicalTrials.gov study NCT03833804. IPD Sharing: YES. Countries: 1. Publications: 3.
LeaData: a novel reference data of digital microscopic leather images for automatic species identification
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Data from: Machine learning identification of microhabitat features associated with occupancy of artificial nestboxes by hazel dormice (Muscardinus avellanarius) in a UK woodland site
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Data from: Molecular mapping and identification of quantitative trait loci for domestication traits in field cress (Lepidium campestre L.) genome
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Data from: Epicuticular compounds of Protopiophila litigata (Diptera: Piophilidae): identification and sexual selection across two years in the wild
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Data from: Modeling the mito-nuclear compatibility and its role in species identification
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Data from: Identification of plankton habitats in the North Sea
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Data for: On the identification of hypoxic regions in subject-specific cerebral vasculature by combined CFD/MRI
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Microscopy data from: Identification of genetic interactions with priB links the PriA/PriB DNA replication restart pathway to double-strand DNA break repair in Escherichia coli
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Data from: Evaluation of a single nucleotide polymorphism baseline for genetic stock identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current Large Marine Ecosystem
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Data from: Identification of druggable targets from the interactome of the Androgen Receptor and Serum Response Factor pathways in prostate cancer
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Data from: Integrative identification of incipient lineages in Heuchera longiflora (Saxifragaceae)
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Data from: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry
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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.