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6,234 results for “phenotype”
Habitat range and phenotypic variation in twelve salt marsh plants on Sapelo Island, Georgia, USA.
We measured traits of twelve salt marsh plant species at sites around Sapelo Island GA in August of 1999. Plant species represent six families - Asteraceae: Aster tenuifolius L., Borrichia frutescens L., Iva frutescens L.; Bataceae: Batis maritima L.; Chenopodiaceae: Salicornia bigelovii Torrey, Salicornia virginica L.; Juncaceae: Juncus roemerianus Scheele; Plumbaginaceae: Limonium carolinianum (Walter) Britton; Poaceae: Distichlis spicata (L.) Greene, Spartina alterniflora Loisel., Spartina patens (Aiton) Muhl., Sporobolus virginicus (L.) Kunth; all nomenclature follows Radford et al. (1968). Soil cores were taken adjacent to each plant to measure soil water content, porewater salinity, and organic content.
Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans
<p><strong>Abstract: </strong><br>SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.</p> <p><strong>More Information:</strong></p> <ul> <li><strong>Preprint:</strong> <a href="https://www.researchsquare.com/article/rs-3304466/v1">Research Square.</a></li> <li><strong>Sample information</strong>: data_inventory.csv file.</li> <li><strong>Code</strong> code_github_repo.zip or at the <a href="https://github.com/ncborcherding/COVID_TCR">original github repo</a></li> <li><strong>Interactive Portal</strong>: <a href="https://cellpilot.emed.wustl.edu/">CellPilot</a></li> </ul>
The behavioral phenotype of early life adversity
<p>In this dataset, we categorized studies investigating the effects of early life adversity on behavior in mice and rats. The dataset is ideal for meta-analyses. For more information about the dataset and the project, see https://osf.io/ra947/</p>
Population persistence, phenotypic divergence and metabolic adaptation in yarrow (Achillea millefolium L.) along a climate gradient, CA, 1920 to 2023
This dataset provides insights into the persistence and adaptation of yarrow (Achillea millefolium L.) populations over a 100-year period of climate change. The data include plant height measurements and climatic variables (temperature and precipitation) from historical and resurveyed sites spanning a broad environmental gradient (1–3,200 m a.s.l.), alongside metabolic profiles obtained from a common-garden experiment. The dataset captures phenotypic changes in plant growth, metabolic diversity, and site-specific climatic shifts between 1920 and 2020. These data support analyses of how temperature and precipitation interact to shape plant responses over time and allow for exploring patterns of local adaptation in phenotypic and metabolic traits. This comprehensive dataset is valuable for understanding the ecological and evolutionary mechanisms underlying population persistence and can inform conservation strategies under future climate scenarios.
Data from: Visual pigment chromophore usage in Nicaraguan Midas cichlids: Phenotypic plasticity and genetic assimilation of cyp27c1 expression
<p>Code and Data associated with "Visual pigment chromophore usage in Nicaraguan Midas cichlids: Phenotypic plasticity and genetic assimilation of <em>cyp27c1</em> expression"</p> <h2><span>Abstract</span></h2> <p><span>The wide-ranging photic conditions found across aquatic habitats may act as selective pressures potentially driving rapid evolution and diversity in the visual system of teleost fishes. Fine-tuning of visual sensitivities in many fish species relies on regulating the two components of visual pigments, the opsin protein and the chromophore. Many studies have focused on opsin gene expression or opsin sequence divergence in fishes inhabiting contrasting habitats. However, variation in chromophore usage across photic habitats has received less attention. Species from the Nicaraguan Midas cichlid complex, <em>Amphilophus </em>cf <em>citrinellus </em>[Günther 1864], have independently colonized seven isolated crater lakes of varying photic conditions resulting in repeated examples of small adaptive radiations. Here, we investigate variation in <em>cyp27c1</em>, the main enzyme involved in chromophore exchange, in response to photic environments in the wild, we measure its genetic component using laboratory-reared fish and test the effect of different rearing light conditions on <em>cyp27c1</em> expression. We found that photic environments significantly predict variation in <em>cyp27c1</em> expression in wild populations and that this variation seems to be genetically assimilated in two populations. We found that light-induced <em>cyp27c1</em> expression is variable across populations (i.e., genotype-by-environment interactions) and correlated with local photic conditions thus highlighting <em>cyp27c1</em> as a key factor of visual ecology in cichlid fishes.</span></p> <p><span>Keywords: <em>cyp27c1 </em>gene expression, sensory ecology, visual plasticity, Neotropical cichlids </span></p>
Prediction and analysis of phenotypes in the Arabidopsis clock mutant prr7prr9 using the Framework Model v2 (FMv2)
<p>This upload contains or links to the biological data, FMv2 model and simulations for the Chew et al. 2017 paper (bioRxiv <a href="https://doi.org/10.1101/105437">https://doi.org/10.1101/105437</a> ), updated 2022 as bioRxiv <a href="https://doi.org/10.1101/105437v2">https://doi.org/10.1101/105437v2</a>, mostly testing and simulating the effect of a slow circadian clock in the <em>prr7prr9 </em>double mutant compared to the Col wild type plants, with controls in <em>lsf1 </em>and <em>prr7 </em>single mutants. This is one of the outputs from the EU TiMet project, <a href="https://fairdomhub.org/projects/92">https://fairdomhub.org/projects/92</a>.</p> <p>Several data files contain results generated in the same studies, but not covered by the publication. For example, additional time points (18 or 21 days of growth), many additional metabolites, and additional genotypes including <em>pgm</em>, <em>lhy cca1, </em>and in one case, <em>toc1 </em>and <em>gi</em>.</p> <p>This data archive was updated during submisson to the journal _in Silico _Plants in 2022, and is formatted as a Research Object, generated by the Snapshot function of FairdomHub, based on <a href="https://fairdomhub.org/investigations/123">Investigation https://fairdomhub.org/investigations/123.</a> The same Snapshot is shared on FairdomHub and will be from the University of Edinburgh Datashare.</p> <p>We request that users gives appropriate credit to the authors of any data released here, as a norm of academic practice, including data released under CC-0 licence on the FairdomHub.</p>
Data: DEAE-Dextran enhances the lentiviral transduction of primary human mesenchymal stromal cells from all major tissue sources without affecting their proliferation and phenotype
<p>This data set includes all the raw data collected for the following article: "DEAE-Dextran enhances the lentiviral transduction of primary human mesenchymal stromal cells from all major tissue sources without affecting their proliferation and phenotype"</p>
Humans display a reduced set of consistent behavioral phenotypes in dyadic games
<p>Socially relevant situations that involve strategic interactions are widespread among animals and humans alike. To study these situations, theoretical and experimental research has adopted a game theoretical perspective, generating valuable insights about human behavior. However, most of the results reported so far have been obtained from a population perspective and considered one specific conflicting situation at a time. This makes it difficult to extract conclusions about the consistency of individuals’ behavior when facing different situations and to define a comprehensive classification of the strategies underlying the observed behaviors. We present the results of a lab-in-the-field experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral rules dictating individuals’ actions. By analyzing our data with an unsupervised clustering algorithm, we find that all the subjects conform, with a large degree of consistency, to a limited number of behavioral phenotypes (envious, optimist, pessimist, and trustful), with only a small fraction of undefined subjects. We also discuss the possible connections to existing interpretations based on a priori theoretical approaches. Our findings provide a relevant contribution to the experimental and theoretical efforts toward the identification of basic behavioral phenotypes in a wider set of contexts without aprioristic assumptions regarding the rules or strategies behind actions. From this perspective, our work contributes to a fact-based approach to the study of human behavior in strategic situations, which could be applied to simulating societies, policy-making scenario building, and even a variety of business applications.</p> <p> </p> <p>The data from the "dr Brain" experiment is organized in two separated files: drbrain_users.csv<br> and drbrain_decisions.csv.</p> <p><br> 1.) drbrain_users.csv contains information about the participants of the experiment (or users).<br> There is one row per user, with the following information about each one of them:</p> <p>User_ID: unique ID number to identify the user.<br> Age: user's age<br> Gender: user's gender<br> Experiment_number: Number of the experiment the user participated in. For organizational reasons, our research actually was made 45 experiments (or replicas) run over a period of 2 days, each one run with differnt users. A user was only allowed to participate in one experiment. Each experiment included between 10-25 users typically, and they played around 13-18 game rounds, typically. Each round and each couple of users played in different games (that is, different values of S, Sucker's payoff, and T, Temptation to defect, while the values of P=5 , Punishment, and R=10, Reward, were always fixed).<br> Earnings: number of points the user obtained in total, over all rounds.</p> <p><br> 2.) drbrain_decisions.csv contains the information of the all game rounds for all experiments and all users.<br> User_ID: unique ID number to identify the user. <br> Experiment_number: Number of the experiment the user participated in.<br> Round_number: Number of the round within a given experiment.<br> S: Value for the "Sucker's payoff" in the game of that round.<br> T: Value for the "Temptation to defect" in the game of that round. <br> Game: Name of the game corresponding to those values of S and T for that round<br> Action: Action chosen by the user (C: cooperate, D: defect)<br> Opponent_ID: ID number of the user's opponent in that round. <br> Opponent_Action: Action (C or D) chosen by the user's opponent in that round.</p> <p>--------</p> <p>For more details, see our research article:</p> <p>Humans display a reduced set of consistent behavioral phenotypes in dyadic games.<br> Julia Poncela-Casasnovas, Mario Gutiérrez-Roig, Carlos Gracia-Lázaro, Julian Vicens, Jesús Gómez-Gardeñes, Josep Perelló, Yamir Moreno, Jordi Duch and Angel Sánchez.<br> Science Advances Vol. 2, no. 8, 2016.<br> DOI: 10.1126/sciadv.1600451<br> http://advances.sciencemag.org/content/2/8/e1600451</p>
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
<p>We present a one-year-long <strong>M</strong>ulti-<strong>S</strong>ensor dataset with <strong>P</strong>henotypic trait measurements from honey <strong>B</strong>ees (MSPB). Data were continuously collected between April-2020 and April-2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), <em>Varroa</em> destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis on honey bees, such as bee acoustics analysis, multi-modal hive monitoring, queen presence detection, <em>Varroa </em>infection detection, hive population estimation, biological analysis of bees, etc.</p> <h3>Related Info</h3> <p>The data collection process, feature pre-processing, preliminary data analysis, and usage notes can be found in our paper <a href="https://arxiv.org/abs/2311.10876">https://arxiv.org/abs/2311.10876</a></p> <p>Check the project webpage (<a href="https://zhu00121.github.io/MSPB-webpage/">https://zhu00121.github.io/MSPB-webpage/</a>) and Github repo (<a href="https://github.com/MuSAELab/MSPB">https://github.com/MuSAELab/MSPB</a>) for more information.</p> <h3>Citation</h3> <p>Kindly cite the following paper:</p> <p>@misc{zhu2023mspb,</p> <p> title={MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees}, </p> <p> author={Yi Zhu and Mahsa Abdollahi and Ségolène Maucourt and Nico Coallier and Heitor R. Guimarães and Pierre Giovenazzo and Tiago H. Falk},</p> <p> year={2023},</p> <p> eprint={2311.10876},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={eess.AS}</p> <p>}</p> <h3>Contact</h3> <p>You can contact us at Yi.Zhu@inrs.ca, if you encounter any questions accessing the data.</p>
Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants
<p>This dataset consists of the reference data files, metadata and processed results files for the paper "Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants," which investigates clonality in normal human dermal fibroblast cell populations in 32 cell lines from distinct donors, using bulk whole-exome sequencing and single-cell RNA-sequencing data.</p> <p>This dataset contains everything required to reproduce the results presented in the paper from processed data and results of our data processing workflows. Our analyses can be reproduced using the <a href="https://github.com/davismcc/fibroblast-clonality">source code</a> and instructions available at our <a href="https://davismcc.github.io/fibroblast-clonality/">project website</a>.</p> <p>The <em>entire</em> analysis workflow from raw data to final results is also reproducible but is substantially more complicated and computationally intensive. It also requires large datasets to be obtained from other repositories. Specifically, single-cell RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-7167. Whole-exome sequencing data is available through the HipSci portal (www.hipsci.org). Combined with the dataset in this repository and following the instructions on the project website, it is possible to run our entire analysis pipeline.</p> <p> </p>
Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
<p><strong>Abstract</strong></p> <p>Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.</p> <p><strong>Data Description</strong></p> <p>This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold. </p> <p>As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.</p> <p>Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.</p> <p><strong>Paper & Code</strong></p> <p>The original paper for this article can be accessed here:</p> <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> <p>To access the codes relevant for this project, please access the project GitHub Repos:</p> <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> <p>If using this work, please cite it based on the above paper, or using the following BibTex:</p> <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> <p> </p> <p><strong>Data Access</strong></p> <p>The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi</p> <p><strong>Funding</strong></p> <p>ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).</p>
Selection for phenotypic plasticity in Rana sylvatica tadpoles, 1998.
The hypothesis that phenotypic plasticity is an adaptation to environmental variation rests on the two assumptions that plasticity improves the performance of individuals that possess it, and that it evolved in response to selection imposed in heterogeneous environments. The first assumption has been upheld by studies showing the beneficial nature of plasticity. The second assumption is difficult to test since it requires knowing about selection acting in the past. However, it can be tested in its general form by asking whether natural selection currently acts to maintain phenotypic plasticity. We adopted this approach in a study of plastic morphological traits in larvae of the wood frog, Rana sylvatica. First we reared tadpoles in artificial ponds for 18 days, in either the presence or absence of Anax dragonfly larvae (confined within cages to prevent them from killing the tadpoles). These conditioning treatments produced dramatic differences in size and shape: tadpoles from ponds with predators were smaller and had relatively short bodies and deep tail fins. We estimated selection by Anax on the two kinds of tadpoles by testing for non-random mortality in overnight predation trials. Dragonflies imposed strong selection by preferentially killing individuals with relatively shallow and short tail fins, and narrow tail muscles. The same traits that exhibited the strongest plasticity were under the strongest selection, except that tail muscle width exhibited no plasticity but experienced strong increasing selection. A laboratory competition experiment, testing for selection in the absence of predators, showed that tadpoles with deep tail fins grew relatively slowly. In the cattle tanks, where there were also no free predators, the predator-induced phenotype survived more poorly and developed slowly, but this cost was apparently not associated with particular morphological traits. These results indicate that selection is currently promoting morphological plasticity in
Figures S1-S7. SMR-HEIDI analysis results for 8q24.21 locus between BP and selected phenotypes.
<p><strong>Supplementary Figures S1-S7. </strong><strong>SMR-HEIDI analysis results for rs6651255 between BP and selected phenotypes.</strong></p> <p>This project contains the following figures:</p> <ul> <li> <p>Figure S1. SMR-HEIDI analysis results for rs6651255 between BP and LDH.</p> </li> <li> <p>Figure S2. SMR-HEIDI analysis results for rs6651255 between BP and <em>GSDMC</em> expression in skeletal muscle (GTEx v6).</p> </li> <li> <p>Figure S3. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in Brain anterior cingulate cortex BA24 (GTEx v6).</p> </li> <li> <p>Figure S4. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in CD8 cell line (CEDAR).</p> </li> <li> <p>Figure S5. SMR-HEIDI analysis results for rs6651255 between BP and heel bone mineral density (UKBB).</p> </li> <li> <p>Figure S6. SMR-HEIDI analysis results for rs6651255 between BP and disc problem phenotype (UKBB)</p> </li> <li> <p>Figure S7. SMR-HEIDI analysis results for rs6651255 between BP and height (UKBB).</p> </li> </ul> <p> </p> <p><strong>Figures legend:</strong></p> <p>Each figure consists of four parts (1 – top left; 2- top right; 3- bottom left; 4 – bottom right):</p> <ol> <li> <p>Regional association plots for GWAS-1 (in our case BP GWAS) and GWAS-2 (expression or complex trait). Blue triangles represent SNPs used to calculate HEIDI test. Crossed triangle is leading SNP for which SMR test was computed.</p> </li> <li> <p>Z-Z plot (GWAS-1 on y-axis and GWAS-2 on x-axis).</p> </li> <li> <p>Visualization of LD matrix for SNPs used in calculation of HEIDI test.</p> </li> <li> <p>Plot of SMR regression coefficient estimates. The plot visualizes the heterogeneity of SMR coefficient. Blue color represents SNPs used to calculate HEIDI test.</p> </li> </ol>
Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set
<p><strong>53 marker MELC Run in human tonsil</strong>. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.</p> <p> </p>
Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy
<p>These are the data tables used to produce results in the publication:</p> <p>"Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy."<br> Phillip A. Richmond & Frans van der Kloet et al.</p> <p>Submitting to Frontiers in Cellular and Developmental Biology, 2020, Peroxisomal Special Issue. </p> <p>These tables include normalized measurements from four omics technologies, with no identifying information included. For details on processing, see the manuscript or contact:</p> <p>prichmond (at) cmmt (dot) ubc (dot) ca. </p> <p>Description of Files</p> <ul> <li>Sample mapping <ul> <li>20180314_sib_pairs.xlsx <ul> <li>Excel sheet describing family numbering, etc. used as a mapping table within the sheets below. </li> </ul> </li> </ul> </li> <li>Methylation: <ul> <li>DMRs_5_Families_ALL_0.10DB_Dec2019.csv <ul> <li>Significant methylated regions with delta beta at least 10 percent when a single family is left out</li> </ul> </li> <li>ALD_Deconvoluted_Betas_Dec2019.csv <ul> <li>All fitted betas for every subject (single CpG)</li> </ul> </li> <li>ALD_Limma_Final_Dec2019_CHR.csv <ul> <li>All fitted effects using limma modeling per CpG </li> </ul> </li> </ul> </li> <li>RNA: <ul> <li>Count_data.txt <ul> <li>The raw count table summed at the gene level using featureCounts.</li> </ul> </li> <li>Pvalues_all_23_01_2019.csv <ul> <li>All pvalues and log fold changes for the genes included in the modeling process (also with family left out)</li> </ul> </li> <li>Tmm_norm_counts_5_2_2020.csv <ul> <li>Tmm normalized RNA count data</li> </ul> </li> </ul> </li> <li>Proteomic <ul> <li>Report_Precursor_Peptides.xls <ul> <li>The proteomic data as an excel spreadsheet</li> </ul> </li> </ul> </li> <li>Pvalues_prot_13_3_2019.xlsx <ul> <li>The pvalues and log fold changes (also with family left out)</li> </ul> </li> <li>Lipids: <ul> <li>Lipid_data.csv <ul> <li>The lipid data (metabolites with missings are removed)</li> </ul> </li> <li>Pvalues_lipids.csv <ul> <li>Pvalues for the lipid data (also with family left out)</li> </ul> </li> </ul> </li> </ul> <p><br> NOTE: For use of these data files for processing and reproducing results of the manuscript, please see https://github.com/Phillip-a-richmond/ALD_Modifier_Project. </p> <p> </p>
Data from: Genetic admixture increases phenotypic diversity in the nectar yeast Metschnikowia reukaufii,
<p>Raw data and supplementary files for the manuscript "Genetic admixture increases phenotypic diversity in the nectar yeast <em>Metschnikowia reukaufii</em>."</p> <p>-------------------</p> <p><strong>Table S5.xlsx </strong>-- Pairwise correlations between phenotypic traits of <em>Metschnikowia reukaufii</em>.</p> <p><strong>Table S6.xlsx</strong> -- Detailed results obtained in tests of phylogenetic signal for different phenotypic traits and indices of overall performance of <em>Metschnikowia reukaufii</em>.</p> <p><strong>Table S7.xlsx</strong> -- Detailed model fitting results obtained for phenotypic traits and indices of overall performance of <em>Metschnikowia reukaufii</em>.</p> <p><strong>mronlyvcf-renamed.vcf</strong> -- High coverage SNPs obtained from whole genome mapping of 73 <em>Metschnikowia reukaufii</em> strains to diploid reference (mean coverage = 47.9×, range 23 – 116×).</p> <p><strong>MR_phenotypes.xlsx</strong> -- Phenotypic data obtained for 73 <em>Metschnikowia reukaufii</em> strains.</p>
Supporting data for "The methylome of Biomphalaria glabrata and other mollusks: enduring modification of epigenetic landscape and phenotypic traits by a new DNA methylation inhibitor"
<p>Methylome of the fresh water snail <em>Biomphalaria glabrata</em>. DNA was extracted from the feet of 10 individuals of <em>B. glabrata</em> originally isolated from Brazil. These snails have been cultivated in the laboratory since 1960. Tissue were grinded at 4°C and incubated in 1 ml volume of lysis buffer (20 mM TRIS pH 8; 1 mM EDTA; 100 mM NaCl; 0.5% SDS), with 0.3 mg of proteinase K at 55°C for 1 night. Afterwards, lysate was purified with phenol-chloroform and DNA was isopropanol precipitated. The extracted DNA (around 138ng/µL) was poled in equivalent amounts and Whole Genome Bisulfite Sequencing was done by GATC-biotech (www.gatc-biotech.com). The principle of this treatment is to convert non-methylated cytosines of gDNA into deoxy-uracil, whereas methylated cytosines remain intact. WGBS was done according to the Lister protocol (sequence 2 forward strands only). The reference genome (Biomphalaria-glabrata-BB02_SCAFFOLDS_BglaB1.fa) and annotation (Biomphalaria-glabrata-BB02_BASEFEATURES_BglaB1.3.gff3) used in this project are available on VectorBase (https://www.vectorbase.org/). To align our short reads, we chose to use two specific bisulfite mapping tools, BSMAP 1.0.0 (https://code.google.com/p/bsmap/) and Bismark 0.10.2 (www.bioinformatics.babraham.ac.uk /projects/bismark/), to compare their efficiency and convenience to finally work with the more suitable one on our datasets. IGV (Interactive Genomics Viewer, https://www.broadinstitute.org/igv/) was used to visualized final alignments.<br> BSMAP performed better than Bismark and was used for downstream analyses. Without default parameters alignement efficiency for BSMAP is 47.1%, allowing for 2 mismatches increases it to 55.6%. Methylation occurs predominantly in CpGs. (C methylated in CpG context: 12.4%, C methylated in CHG context: 0.5%, C methylated in CHH context: 0.5%) The major part of CpG sites, 95.7% were unmethylated, of the remaining 4.3% of CpG sites around 3.8% had low methylation, and 0.5% were completely methylated. Methylation is of the mosaic type. Methylation is relatively low with 1.2% of total cytosines. Our analyses suggested that conserved genes and genes with stable expression are localized in high methylated regions of the genome. Finally, we see that repetitive sequences were predominantly situated in low methylated regions of <em>B. glabrata</em>. </p> <p>Wiggle files were generated for CpG pairs only.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
Phenotypic data related to genetic architecture of transmission stage production and virulence in schistosome parasites
<p>These data were generated related to the study of the <strong>Genetic architecture of transmission stage production and virulence in schistosome parasites</strong>.</p> <p><strong>Abstract:</strong> Both theory and experimental data from multiple pathogens suggest that the production of transmission stages should be strongly associated with virulence, but the genetic bases of parasite transmission/virulence traits are poorly understood. In the blood fluke <em>Schistosoma mansoni</em>, parasite genotypes show extensive variation in numbers of cercariae larvae shed from infected snails. Furthermore, high shedding parasites cause high mortality to snails while low shedding parasites cause low mortality, consistent with expected trade-offs between parasite transmission and virulence. To understand the genetic basis of transmission stage production/virulence, we conducted reciprocal crosses between schistosomes from two laboratory populations that differ 8-fold in cercarial shedding and in their virulence to inbred snail hosts. Each parasite generation, we determined four-week cercarial shedding profiles in inbred <em>Biomphalaria glabrata</em> snails infected with single parasite larvae. We sequenced the whole genome of the F0 parents and the exome of the F1 progeny and 188 F2 progeny from each cross, and used linkage mapping to reveal quantitative trait loci (QTLs) underlying transmission stage production. Cercarial production is polygenic: we found three major QTLs on chromosome 1, 3 and 5 (Log-of-the-odds (LOD) = 5.61, 8.19, 6.25) and two minor QTLs on chromosome 2 and 4. These QTLs act additively and explained 28.56% of the phenotypic variation in cercarial shedding. Alleles inherited from the high and low shedding parents were co-dominant at all QTLs, except for chr. 1 and chr. 4 where the “high cercarial shedding” allele is recessive. These results demonstrate that the genetic architecture of key traits directly relevant to schistosome ecology can be dissected using classical linkage mapping approaches, and set the stage for fine mapping and functional validation of the genes involved using the growing armory of functional and cell biology tools available for this parasite.</p> <p> </p> <p>This dataset is made of 4 tables:</p> <ul> <li>F0_parental_populations.csv</li> <li>F1.csv</li> <li>F2.csv</li> <li>sex.tsv</li> </ul> <p> </p> <p><strong>F0_parental_populations.csv</strong></p> <p> </p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of <em>Schistosoma mansoni</em> parasite. We have compared the transmission stage production between two different populations of <em>S. mansoni</em> parasite. This dataset was originally published in Le Clec'h et al., 2019 (Striking differences in virulence, transmission and sporocyst growth dynamics between two schistosome populations. Parasites and Vectors. 2019 Oct 16;12(1):485. doi: 10.1186/s13071-019-3741-z).</p> <p> </p> <p>This table is made of 9 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>schistosoma_population</strong>: the population of schistosome used for the infection of the snail. Each snail was infected with a single parasite genotype. We have used SmLE (high shedder/highly virulent population) and SmBRE (low shedding/low virulent population).</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4).</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined by PCR <sup>1</sup>.</li> </ul> <p> </p> <p><strong>F1.csv</strong></p> <p> </p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of F1 progeny from the cross SmLE x SmBRE (see the manuscript for details).</p> <p> </p> <p>This table is made of 11 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>cross</strong>: F1A or F1B cross. Each snail was infected with a single parasite genotype from either F1A or F1B progeny.</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4).</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>PO</strong>: the total phenoloxidase activity in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>2</sup>.</li> <li><strong>Hb</strong>: the hemoglobin rate in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>3</sup>.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined by PCR <sup>1</sup>.</li> </ul> <p> </p> <p><strong>F2.csv</strong></p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of F2 progeny from the cross SmLE x SmBRE (see the manuscript for details).</p> <p> </p> <p>This table is made of 10 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>cross</strong>: F2A or F2B cross. Each snail was infected with a single parasite genotype from either F2A or F2B progeny.</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4)</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>PO</strong>: the total phenoloxidase activity in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>2</sup>.</li> <li><strong>Hb</strong>: the hemoglobin rate in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>3</sup>.</li> </ul> <p> </p> <p><strong>sex.csv</strong></p> <p> </p> <p>This table contains the <em>in silico</em> sexing of F0 parents, F1 parents and F2 progeny of <em>S. mansoni</em> parasites.</p> <p>This table is made of 4 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample</li> <li><strong>read_depth</strong>: the read depth ratio between the Z-linked and pseudo-autosomal regions.</li> <li><strong>ratio</strong>: computed ratio between the Z-linked and pseudo-autosomal regions.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined <em>in silico</em>: a ratio around 1 corresponds to a male carrying two Z chromosomes while a ratio around 0.5 corresponds to a female carrying only one Z chromosome.</li> </ul> <p><strong>Notes:</strong></p> <p><sup>1</sup>. Le Clec’h W, Chevalier F et al. Real-time PCR for sexing Schistosoma mansoni cercariae. Mol Biochem Parasitol. Jan-Feb 2016; 205(1-2):35-8.doi: 10.1016/j.molbiopara.2016.03.010. Epub 2016 Mar 26.</p> <p><sup>2</sup>. Le Clec’h W et al. Characterization of hemolymph phenoloxidase activity in two Biomphalaria snail species and impact of Schistosoma mansoni infection. Parasit Vectors. 2016 Jan 22; 9:32.doi: 10.1186/s13071-016-1319-6.</p> <p><sup>3</sup>. Le Clec'h et al. Striking differences in virulence, transmission and sporocyst growth dynamics between two schistosome populations. Parasit Vectors. 2019 Oct 16; 12(1):485. doi: 10.1186/s13071-019-3741-z.</p>
Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
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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
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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.