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6,234 results for “phenotypes”
Data for: Phenotypic adaptation to temperature in the mosquito vector, Aedes aegypti
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Propagule phenotypic traits of Kandelia obovata and sampling sites information in China
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Data from: Phenotypic selection on an ornamental trait is not modulated by breeding density in a pied flycatcher population
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Phenotypic diversity of <em>Populus euphratica</em> and its environmental drivers in arid ecosystems
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Data from: Identifying genetic variation associated with environmental variation and drought-tolerance phenotypes in ponderosa pine
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A chromosome-level genome for the nudibranch gastropod Berghia stephanieae helps parse clade-specific gene expression in novel and conserved phenotypes
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Protea repens whole transcriptome count data for control and drought treatment for 8 populations, climatic data for the 8 populations and phenotypic data collected, and data used for linear mixed models for climate gene expression/trait correlation testing
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Bat-flower trait matching: Extreme phenotypic specialization affects diet preferences but not diet breadth
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Arabidopsis BioCON evolutionary study:Arabidopsis thaliana, Bromus inermis phenotypic selection analysis
We know a lot about the potential ecological effects of increasing concentrations to atmospheric CO2; however, we know relatively little about whether increased concentrations will also affect evolution. In collaboration with Jennifer Lau (KBS and Michigan State University), Peter Reich (U of MN Forestry) and Ruth Shaw (U of MN EEB) we are using a combination traditional quantitative genetic and QTL approaches to determine the effect elevated CO2 has on; patterns of selection, responses to selection, and the genetic basis of phenotypic variation.
Arabidopsis BioCON evolutionary study:Arabidopsis thaliana, Bromus inermis phenotypic selection analyses standardized across trtmnts
We know a lot about the potential ecological effects of increasing concentrations to atmospheric CO2; however, we know relatively little about whether increased concentrations will also affect evolution. In collaboration with Jennifer Lau (KBS and Michigan State University), Peter Reich (U of MN Forestry) and Ruth Shaw (U of MN EEB) we are using a combination traditional quantitative genetic and QTL approaches to determine the effect elevated CO2 has on; patterns of selection, responses to selection, and the genetic basis of phenotypic variation.
Incremental integration for tracking genotype-phenotype associations - dataset snapshot
<p>This dataset is an archive of data and code files used to generate figures and tables for a manuscript entitled "Incremental integration for tracking genotype-phenotype associations".</p> <p>Data files include the exact versions of raw materials as well as databases produced during the course of the analysis described in the manuscript. </p> <p>Code files include python and R components.</p> <p>Further details are available in the archive README file.</p>
Phenotype data (composition, functionality, end-product quality) of wheat grown in Flanders, Belgium
<p>Data for the Ph.D. dissertation entitled: Outline of four degrees of diversification for understanding bread wheat (<em>Triticum aestivum</em> L.) quality. For further details on the content of the files, the meaning of abbreviations and used techniques, it is referred to the Ph.D. manuscript. </p> <p>For further details, please contact the author. </p>
The Unified Phenotype Ontology (uPheno) - Version 2, Alpha release
<p>This is the Alpha release of the re-designed Unified Phenotype Ontology (uPheno), referred to internally as uPheno 2. The effort responsible for the content of uPheno 2 is the <a href="https://github.com/obophenotype/upheno/wiki/Phenotype-Ontologies-Reconciliation-Effort">Phenotype Reconciliation Effort</a>. The pipeline that generates uPheno 2 can be found here: <a href="https://github.com/obophenotype/upheno-dev">https://github.com/obophenotype/upheno-dev</a>.</p>
Tables S2-S3. SMR-HEIDI results for 8q24.21 locus between back pain and other phenotypes.
<p><strong>Supplementary Table S2. SMR-HEIDI results for 8q24.21 locus between back pain and other complex traits. </strong></p> <p><strong>Column headers of Tables S2:</strong></p> <p>Secondary Trait Name: Name of complex trait<br> Dataset: Name of dataset (UKB Neale's lab or UKB Gene Atlas)<br> Index_SNP: RsID of SNP used as target in SMR-HEID analysis<br> Proxy_SNP: RsID of top SNP in the selected locus - SNP presented in both GWAS data with minimum P-values in back pain GWAS<br> r(proxy_SNP,index_SNP): linkage disequilibrium coefficient between index and proxy SNP<br> beta_SMR: Beta SMR for proxy SNP<br> p_SMR: P-value of beta SMR<br> qFDR-BH SMR: P-value of beta SMR after Bonferroni correction<br> p_HEIDI: P-value of HEIDI test<br> n_HEIDI: Number of SNPs used in HEIDI test<br> SNPs_HEIDI: List of rsID of SNPs used in HEIDI test</p> <p> </p> <p><strong>Supplementary Table S3.</strong> <strong>SMR-HEIDI results for 8q24.21 locus between back pain and expression of genes.</strong></p> <p><strong>Column headers of Tables S3:</strong></p> <p>Secondary Trait Name: Name of complex trait<br> Tissue: Name of the tissue from which the samples were taken to study gene expression<br> Gene_name: Gene name corresponding to the transcript name<br> Transcript Name: Transcript name (ID)<br> Dataset: Name of dataset (CEDAR, GTEx_v6)<br> beta_SMR: Beta SMR for proxy SNP<br> p_SMR: P-value of beta SMR<br> qFDR-BH SMR: P-value of beta SMR after Bonferroni correction<br> p_HEIDI: P-value of HEIDI test<br> n_HEIDI: Number of SNPs used in HEIDI test<br> SNPs_HEIDI: List of rsID of SNPs used in HEIDI test</p> <p> </p> <p>Part of the article: Williams FMK et al. "Sequence variation at 8q24.21 and risk of back pain"</p>
Machine learning in mass spectrometry: A MALDI-TOF MS approach to phenotypic antibacterial screening
<p>Dataset relating to the publication:</p> <p>Machine learning in mass spectrometry: A MALDI-TOF MS approach to phenotypic antibacterial screening</p> <p>by Luuk Nico van Oosten and Christian D. Klein</p> <p>Published in the Journal of Medicinal Chemistry, 2020</p> <p><strong>Important notice:</strong></p> <p><strong>The data are free to use for non-commercial, academic purposes, provided that the original source is<br> cited and the authors and the publication are credited in any derivative work.</strong></p> <p><strong>A patent application has been filed for the method described by van Oosten and Klein, which uses mass<br> spectrometry and machine learning to identify the pharmacological or other effects of compounds on cell<br> cultures and other biological systems.</strong></p> <p>Therefore, a license for the commercial use of the method must be negotiated by contacting either</p> <p>Anke Faller<br> Universität Heidelberg<br> Dezernat Forschung<br> Rechts- und Strukturfragen der Forschungsförderung<br> Seminarstraße 2, 69117 Heidelberg<br> Tel. +49 6221 54-12611<br> anke.faller(at)zuv.uni-heidelberg.de</p> <p>or</p> <p>Prof. Dr. C. Klein; c.klein(at)uni-heidelberg.de<br> Medicinal Chemistry<br> Institute of Pharmacy and Molecular Biotechnology IPMB<br> Heidelberg University, INF 364<br> D-69120 Heidelberg<br> Germany<br> Phone: ++49-6221-54-4875<br> FAX : ++49-6221-54-6430</p> <p> </p>
Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC colon data-set
<p> 50 marker MELC Run in human colon. Each image shows the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins and transcription factors. Images contain 2024 x 2024 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>
WiDiv_PAM_Clustering_Integrated_Phenotypes
<p>Root phenotype and performance data for the Wisconsin Diversity Panel collected from the field under well-watered and water stress conditions in Willcox, AZ, in 2016. Images of root architecture and anatomy were analyzed for each genotype, two replicates per treatment. Architectural data was collected using DIRT. Anatomical data was collected with RootScan2 and MIPAR. Also included is the R script to conduct a PAM clustering analysis to identify clusters of root phenotypes related to performance.</p>
Data from: Floral trait differentiation in Anacamptis coriophora: phenotypic selection on scents, but not on colour
<p>Current divergent selection may promote floral trait differentiation among conspecific populations in flowering plants. However, whether this applies to complex traits such as colour or scents has been little studied, even though these traits often vary within species. In this study, we compared floral colour and odour as well as selective pressures imposed upon these traits among seven populations belonging to three subspecies of the widespread, generalist orchid <i>Anacamptis coriophora</i>. Colour was characterised using calibrated photographs and scents were sampled using dynamic headspace extraction and analysed using gas chromatography-mass spectrometry. We then quantified phenotypic selection exerted on these traits by regressing fruit set values on floral trait values. We showed that the three studied subspecies were characterised by different floral colour and odour, with one of the two predominant floral volatiles emitted by each subspecies being taxon-specific. Plant size was positively correlated with fruit set in most populations, while we found no apparent link between floral colour and female reproductive success. We detected positive selection on several taxon-specific compounds in <i>A. coriophora</i> subsp. <i>fragrans</i>, whereas no selection was found on floral volatiles of <i>A. coriophora</i> subsp. <i>coriophora</i> and <i>A. coriophora</i> subsp. <i>martrinii</i>. This study is one of the first to document variation in phenotypic selection exerted on floral scents among conspecific populations. Our results suggest that selection could contribute to ongoing chemical divergence among <i>A. coriophora</i> subspecies.</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>
Using ontologies to extract disease--phenotype associations from literature
<p>This dataset contains disease-phenotype associations. We developed and used a text-mining system which utilizes semantic relations in the phenotype ontologies and statistical methods to extract disease-phenotype associations from the literature.</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
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.