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6,234 results for “phenotypes”
Predicting Phenotypic Traits Using Conglomerate RNA-seq Datasets -- Datasets
<p>Phenotype Annotations and Corresponding Gene Expression Matrices created for the paper Hadish et al. [REF]. Raw Phenotypic Annotations were downloaded and processed from NCBI BioProject and raw RNA-seq data was downloaded from NCBI SRA. All annotations are contained within a single file for either Tissue or Age. Gene expression matrices are for each of the 12 species used in this project in their unproccessed form. </p> <p>Scripts used in this project are located here: </p> <p>GitLab Repository: </p> <p>https://gitlab.com/ficklin-lab/predicting-phenotypic-traits-using-conglomerate-rna-seq-datasets</p> <p>Paper Citation: </p> <p>[REF]</p>
Data from de Vega et al_Flora "Host-driven phenotypic and phenological differentiation in sympatric races of a parasitic plant" [Dataset]
<p>Data from de Vega et al_Flora "Host-driven phenotypic and phenological differentiation in sympatric races of a parasitic plant"</p>
Table 1 in Tertiary hospital sewage as reservoir of bacteria expressing MDR phenotype in Brazil
<p><b>Table 1.</b> Antibiotic resistance profile and multiresistant phenotype of bacteria isolated in sewage from a tertiary hospital located in Ribeirão Preto, São Paulo, Brazil.</p><table><tbody><tr><th><b>Sampling point</b></th><th><b>Species</b></th><th><b>Antibiotic resistance profile*</b></th><th><b>Resistance phenotype</b></th></tr></tbody><tbody><tr><th>Ambulatory</th><td><i>Proteus mirabilis</i></td><td>AMO, AMC, AMP, CAZ, ERT, SXT, TET</td><td>MDR</td></tr><tr><th>care</th><td><i>Yersinia enterocolitica</i></td><td>AMO, AMC, AMP, CTX, CFO, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Enterococcus faecalis</i></td><td>CIP, CLI, GEN, SXT, TET, VAN</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP, CAZ, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th>Patient wards</th><td><i>Enterobacter cloacae</i> Complex</td><td>AMI, AMP, ASB, CPM, CFO, CAZ, CRO, CRX, CIP, ERT, GEN, IPM, MER, TZP</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella ozaenae</i></td><td>AMO, AMP, CTX, CFO, CAZ, CIP, ERT, GEN, IPM, MER, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, ERT, GEN, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella ozaenae</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, CIP, ERT, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMC, AMP, CTX, CAZ, ERT, IPM, MER, TET</td><td>MDR</td></tr><tr><th></th><td><i>Staphylococcus aureus</i></td><td>AMP, CIP, CLI, GEN, SXT, TCP, TET, VAN</td><td>MDR</td></tr><tr><th></th><td><i>Citrobacter youngae</i></td><td>AMO, AMC, AMP, CFO</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMC, AMP, SXT</td><td>-</td></tr><tr><th></th><td><i>Enterococcus gallinarum</i></td><td>CLI, SXT, TET, VAN</td><td>-</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>CAZ, CIP, GEN</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>CAZ</td><td>-</td></tr><tr><th>Confluent point</th><td><i>Klebsiella pneumoniae</i></td><td>AMP, ASB, CPM, CFO, CAZ, CRO, CRX, CIP, ERT, GEN, IPM, MER, TZP</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, ERT, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Serratia liquefaciens</i></td><td>AMO, AMP, CTX, CFO, CAZ, ERT, IPM, MER</td><td>MDR</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>AMI, AMP, ASB, CPM, CFO, CAZ, CRO, CRX</td><td>-</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP, CTX, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>CTX, CAZ, ERT, IPM, MER</td><td>-</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>CFO, CAZ, TET</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP, CAZ</td><td>-</td></tr><tr><th></th><td><i>Enterococcus faecium</i></td><td>CLI, SXT</td><td>-</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMP</td><td>-</td></tr></tbody></table>
BRESOV common bean ILs collection , phenotypic data
<p><strong><span>Dataset_ILS_2018</span></strong></p> <p><span>Dataset including phenotypic data recorded on the introgression line population (IL), in the multi-locations field trials performed in Spain, Romania, Italy in 2018 </span></p> <p><strong><span>Dataset_ILS_2019</span></strong></p> <p><span>Dataset including phenotypic data recorded on the introgression line population (IL), in the multi-locations field trials performed in Spain, Romania, Italy in 2019 </span></p> <p><strong><span>Dataset_ILS_2020</span></strong></p> <p><span>Dataset including phenotypic data recorded on the introgression line population (IL), in the multi-locations field trials performed in Spain, Romania, Italy in 2020</span></p>
Growing Malaria Parasites at a Critical Shaking Speed Mimicking Physiological Flow Reveals New Phenotypes for Invasion Ligands, Supporting Information
<p>This repository contains the dataset and analysis scripts associated with the upcoming publication titled <em>Growing malaria parasites at a critical shaking speed that mimics physiological flow conditions reveals new phenotypes for EBA and RH invasion ligands.</em> The repository includes a comprehensive collection of data and scripts used to generate all the plots, along with videos showing how the red blood cells behave in culture media for different shaking speeds in the different shaking vessels. The growth assay data used for this experiment was collected in four batches, labelled GA1 to 4:</p> <ul> <li><strong>GA1</strong>: compares the different knockout lines. </li> <li><strong>GA2</strong>: compares different hematocrits. </li> <li><strong>GA3</strong>: compares different growth vessels.</li> <li><strong>GA4</strong>: contains more repeats of lines from GA1.</li> </ul> <p>This repository offers all necessary resources to replicate the findings, including the complete codebase, raw data, and graphical representations of results. Researchers are encouraged to explore the included notebooks and datasets for detailed insights. Movies S1 to S4 show a top and side view of each type of plate/plask used on an orbital shaker as the shaking speed is increased.</p>
Fig. 5 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 5. UPGMA of Paspalum urvillei accessions from USDA germplasm.
Fig. 4 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 4. Principal Coordinate Analysis of Paspalum urvillei accessions from USDA germplasm.
Fig. 3 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 3. Principal Component Analysis of Paspalum urvillei accessions from USDA germplasm.
Fig. 2 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 2. The phenotypic structure of Paspalum urvillei accessions from USDA germplasm.
Fig. 1 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 1. Spatial clustering of Paspalum urvillei accessions from USDA germplasm.
Fig. 6 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 6. Violin plot of Paspalum urvillei accessions from USDA germplasm.
Table 2 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
<p><b>Table 2.</b> Summary statistics of evaluated variables of <i>Paspalum urvillei</i> from USDA germplasm accessions.</p><table><tbody><tr><th>Variable</th><th>Main category frequency</th><th>Trait diversity</th><th>PIC</th></tr></tbody><tbody><tr><th>Plant width</th><td>0.439</td><td>0.659</td><td>0.595</td></tr><tr><th>Foliage height</th><td>0.585</td><td>0.603</td><td>0.565</td></tr><tr><th>Plant height</th><td>0.537</td><td>0.627</td><td>0.576</td></tr><tr><th>Leaf length</th><td>0.317</td><td>0.736</td><td>0.688</td></tr><tr><th>Leaf width</th><td>0.561</td><td>0.616</td><td>0.571</td></tr><tr><th>Maturity</th><td>0.537</td><td>0.664</td><td>0.635</td></tr><tr><th>Foliage distribution</th><td>0.854</td><td>0.256</td><td>0.233</td></tr><tr><th>Stem size</th><td>0.951</td><td>0.093</td><td>0.089</td></tr><tr><th>Tillers</th><td>0.951</td><td>0.093</td><td>0.089</td></tr><tr><th>Foliage amount</th><td>0.585</td><td>0.485</td><td>0.368</td></tr><tr><th>Seed production</th><td>0.805</td><td>0.330</td><td>0.300</td></tr><tr><th>Winter survival</th><td>0.268</td><td>0.829</td><td>0.809</td></tr><tr><th>Mean</th><td>0.616</td><td>0.499</td><td>0.460</td></tr></tbody></table>
Table 1 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
<p><b>Table 1.</b> Description of <i>Paspalum urvillei</i> accessions of USDA germplasm.</p><table><tbody><tr><th>Plant ID</th><th>Plant #</th><th>Taxonomy</th><th>Country</th><th>City</th></tr></tbody><tbody><tr><th>PI 164065</th><td>1</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Florianópolis</td></tr><tr><th>PI 202046</th><td>2</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 203747</th><td>3</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>São Gabriel</td></tr><tr><th>PI 203749</th><td>4</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Rivera</td></tr><tr><th>PI 203752</th><td>5</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Porto Alegre</td></tr><tr><th>PI 204237</th><td>6</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Rivera</td></tr><tr><th>PI 276255</th><td>7</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Montevideo</td></tr><tr><th>PI 283025</th><td>8</td><td><i>P. urvillei</i></td><td>Uruguay (3),</td><td>Tacuarembo</td></tr><tr><th>PI 304040</th><td>9</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304041</th><td>10</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304042</th><td>11</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304043</th><td>12</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304044</th><td>13</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304045</th><td>14</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Montenegro</td></tr><tr><th>PI 304047</th><td>15</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304048</th><td>16</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304050</th><td>17</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 310253</th><td>18</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 310255</th><td>19</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Tupancireta</td></tr><tr><th>PI 310257</th><td>20</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310258</th><td>21</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310263</th><td>22</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310266</th><td>23</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Julio de Castilho</td></tr><tr><th>PI 364984</th><td>24</td><td><i>P. urvillei</i></td><td>South Africa (4)</td><td>Limpopo</td></tr><tr><th>PI 365131</th><td>25</td><td><i>P. urvillei</i></td><td>Swaziland (6)</td><td>Malkerns</td></tr><tr><th>PI 202296</th><td>26</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 300079</th><td>27</td><td><i>P. urvillei</i></td><td>South Africa (4)</td><td>East London</td></tr><tr><th>PI 303957</th><td>28</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304146</th><td>29</td><td><i>P. urvillei</i></td><td>Mexico (5)</td><td>Lagos-Aquascalientes</td></tr><tr><th>PI 404506</th><td>30</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 404885</th><td>31</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Paysandu</td></tr><tr><th>PI 404886</th><td>32</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Tranqueras</td></tr><tr><th>PI 462304</th><td>33</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Cerro Largo</td></tr><tr><th>PI 462305</th><td>34</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Cerro Largo</td></tr><tr><th>PI 462306</th><td>35</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Salto</td></tr><tr><th>PI 509008</th><td>36</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Ivoti</td></tr><tr><th>PI 509013</th><td>37</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Villa Maria</td></tr><tr><th>PI 509014</th><td>38</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Olivia</td></tr><tr><th>PI 509015</th><td>39</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Arroyito</td></tr><tr><th>PI 509016</th><td>40</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 509017</th><td>41</td><td><i>P. urvillei</i></td><td>Bolivia (7)</td><td>Villa Montes</td></tr></tbody></table>
Interactive Heatmap of all Disease Phenolog Behavioural Phenotypes
<p><span><span>An interactive clustermap of behavioural tracking data for a panel of 25 <em>C. elegans</em> disease model phenologs (associated with the linked published paper). This is a static html file that can be opened in a browser and zoomed in for a detailed inspect of how the various strains differ from the control (N2). Mousing over the heatmap shows the name of features at each position so that a more intutive conclusion of the data, i.e., 'Strain A is slow' or 'Strain B is more curved', can be reached.</span></span><span></span></p>
Systematic creation and phenotyping of Mendelian disease models in C. elegans: towards large-scale drug repurposing
<p>Data collected for the eLife OpenAccess paper: Systematic creation and phenotyping of Mendelian disease models in <em>C. elegans</em>: towards large-scale drug repurposing. (doi: 10.7554/eLife.92491.1)</p> <p>Contains: extracted features, calculated stats, normalised z-scores and timerseries data of all the disease model mutants generated. In addition, there is a static .html file that allows for mousing over the clustermaps to easily view differences in strains compared to the N2 wild-type. Dataset also contains, metadata and feature summary/file name information of FDA-library drug screen and the confirmation screen of the hit from this (i.e., all data collected in published in the associated paper). </p>
High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations
<p>This repository contains: all code, phenomic data, extracted features, calculated stats, normalised z-scores and timerseries data for all of the disease mutant phenologs and data in our paper: High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations.</p>
A high-precision method of segmenting complex postures in C. elegans and deep phenotyping to analyze lifespan
<p>The data for our paper, "A high-precision method of segmenting complex postures in <em>C. elegans</em> and deep phenotyping to analyze lifespan", includes the following three components:</p> <ol> <li>Synthetic image dataset, CSB-1 dataset, and MD dataset, which originate from the paper <em>"WormSwin: Instance segmentation of C. elegans using Vision Transformer"</em>.</li> <li>BBC010 dataset, sourced from the paper <em>"Annotated high-throughput microscopy image sets for validation".</em></li> <li>Training weights for the Synthetic image dataset, CSB-1 dataset, MD dataset, and BBC010 dataset, as well as the pretrained weights used for worm tracking. The training weights from the synthetic image dataset can serve as pretrained weights for training on other datasets.</li> </ol> <p>Our experimental results are based on the average of multiple training runs; here, we have only uploaded one set of weights per dataset to facilitate reproducibility for readers. For more detailed information on the datasets, please refer to the relevant papers.</p> <p> </p>
Functional consequences of phenotypic variation between locally adapted populations: swimming performance and ventilation in extremophile fish
<p>Natural selection drives the evolution of traits to optimize organismal performance, but optimization of one aspect of performance can often influence other aspects of performance. Here, we asked how phenotypic variation between locally adapted fish populations affects locomotion and ventilation, testing for functional trade-offs and trait-performance correlations. Specifically, we investigated two populations of livebearing fish (<em>Poecilia mexicana</em>) that inhabit distinct habitat types (hydrogen-sulfide-rich springs and adjacent nonsulfidic streams). For each individual, we quantified different metrics of burst swimming during simulated predator attacks, steady swimming, as well as gill ventilation. Coinciding with predictions, we documented significant population differences in all aspects of performance, with fish from sulfidic habitats exhibiting higher steady swimming performance and higher ventilation capacity, but slower burst swimming. There was a significant functional trade-off between steady and burst swimming, but not between different aspects of locomotion and ventilation. While our findings about population differences in locomotion performance largely parallel the results from previous studies, we provide novel insights about how morphological variation might impact ventilation and ultimately oxygen acquisition. Overall, our analyses provided insights into the functional consequences of previously documented phenotypic variation, which will help to disentangle the effects of different sources of selection that may coincide along complex environmental gradients.</p>
Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.
Data and code from: Phenotypic memory drives population growth and extinction risk in a noisy environment
<p>Random environmental fluctuations pose major threats to wild populations. As patterns of environmental noise are themselves altered by global change, there is growing need to identify general mechanisms underlying their effects on population dynamics. This notably requires understanding and predicting population responses to the color of environmental noise, i.e. its temporal autocorrelation pattern. Here, we show experimentally that environmental autocorrelation has a large influence on population dynamics and extinction rates, which can be predicted accurately provided that a memory of past environment is accounted for. We exposed near to 1000 lines of the microalgae <em>Dunaliella salina</em> to randomly fluctuating salinity, with autocorrelation ranging from negative to highly positive. We found lower population growth, and twice as many extinctions, under lower autocorrelation. These responses closely matched predictions based on a tolerance curve with environmental memory, showing that non-genetic inheritance can be a major driver of population dynamics in randomly fluctuating environments. </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.