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1,363 results for “phenotypic data”
Data from: Local adaptation when competition depends on phenotypic similarity
Recent work incorporating demographic–genetic interactions indicates the importance of population size, gene flow, and selection in influencing local adaptation. This work typically assumes that density-dependent survival affects individuals equally, but individuals in natural population rarely compete equally. Among-individual differences in resource use generate stronger competition between more similar phenotypes (frequency-dependent competition) but it remains unclear how this additional form of selection changes the interactions between population size, gene flow, and local stabilizing selection. Here, we integrate migration–selection dynamics with frequency-dependent competition. We developed a coupled demographic-quantitative genetic model consisting of two patches connected by dispersal and subject to local stabilizing selection and competition. Our model shows that frequency-dependent competition slightly increases local adaptation, greatly increases genetic variance within patches, and reduces the amount that migration depresses population size, despite the increased genetic variance load. The effects of frequency-dependence depend on the strength of divergent selection, trait heritability, and when mortality occurs in the life cycle in relation to migration and reproduction. Essentially, frequency-dependent competition reduces the density-dependent interactions between migrants and residents, the extent to which depends on how different and common immigrants are compared to residents. Our results add new dynamics that illustrate how competition can alter the effects of gene flow and divergent selection on local adaptation and population carrying capacities.
Data from: Phenotypic interactions between tree hosts and invasive forest pathogens in the light of globalization and climate change
Invasive pathogens can cause considerable damage to forest ecosystems. Lack of coevolution is generally thought to enable invasive pathogens to bypass the defence and/or recognition systems in the host. Although mostly true, this argument fails to predict intermittent outcomes in space and time, underlining the need to include the roles of the environment and the phenotype in host–pathogen interactions when predicting disease impacts. We emphasize the need to consider host–tree imbalances from a phenotypic perspective, considering the lack of coevolutionary and evolutionary history with the pathogen and the environment, respectively. We describe how phenotypic plasticity and plastic responses to environmental shifts may become maladaptive when hosts are faced with novel pathogens. The lack of host–pathogen and environmental coevolution are aligned with two global processes currently driving forest damage: globalization and climate change, respectively. We suggest that globalization and climate change act synergistically, increasing the chances of both genotypic and phenotypic imbalances. Short moves on the same continent are more likely to be in balance than if the move is from another part of the world. We use Gremmeniella abietina outbreaks in Sweden to exemplify how host–pathogen phenotypic interactions can help to predict the impacts of specific invasive and emergent diseases. This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'.
Data from: The role of phenotypic plasticity on population differentiation
Several evolutionary processes shape the genetic and phenotypic differentiation of populations. Among them, the joint effects of gene flow, selection and phenotypic plasticity are poorly known, especially when trying to understand how maladaptive plasticity affects population divergence. We extended a quantitative genetic model of Hendry et al. (2001) to describe these joint effects on phenotypic and additive genetic divergence between two populations, and their phenotypic and genetic differentiation (PST and QST). With individual-based simulations, we tested our model predictions and further modeled allelic differentiation at neutral (FST) and adaptive (FSTQ) loci. While adaptive phenotypic plasticity allows for large phenotypic divergence and differentiation despite high gene flow, maladaptive plasticity promotes genetic divergence and generates countergradient variation, under extensive migration with phenotypic differences sometimes opposed to genetic differences. Maladaptive plasticity can also promote adaptive phenotypic divergence by reducing the effective gene flow. Overall, plasticity decouples genetic from phenotypic differences between populations, and blurs the correlation between phenotypic divergence and local adaptation. By deriving models of population differentiation for three different life cycles, we further describe the effect of a species' ecology on evolution in structured populations.
Data from: Genetic and phenotypic divergence in an island bird: isolation by distance, by colonisation or by adaptation?
Discerning the relative roles of adaptive and non-adaptive processes in generating differences among populations and species, as well as how these processes interact, are fundamental aims in biology. Both genetic and phenotypic divergence across populations can be the product of limited dispersal and gradual genetic drift across populations (isolation by distance), of colonisation history and founder effects (isolation by colonisation) or of adaptation to different environments preventing migration between populations (isolation by adaptation). Here we attempt to differentiate between these processes using island populations of Berthelot's pipit (Anthus berthelotii), a passerine bird endemic to three Atlantic archipelagos. Using microsatellite markers and approximate Bayesian computation, we reveal that the northwards colonisation of this species ca 8,500 years ago resulted in genetic bottlenecks in the colonised archipelagos. We then show that high levels of genetic structure exist across archipelagos, and that these are consistent with a pattern of isolation by colonisation, but not with isolation by distance or adaptation. Finally, we show that substantial morphological divergence also exists and that this is strongly concordant with patterns of genetic structure and bottleneck history, but not with environmental differences or geographic distance. Overall our data suggest that founder effects are responsible for both genetic and phenotypic changes across archipelagos. Our findings provide a rare example of how founder effects can persist over evolutionary timescales, and suggest that they may play an important role in the early stages of speciation.
Data and R scripts for: Variation in phenotypic plasticity across age-at-maturity genotypes in wild Atlantic salmon
<p>Data and R-scripts underlying the statistical analyses of the study. </p>
Data from: Phenotypic antimicrobial resistance in Staphylococcus sp. and Enterococcus sp. isolated from surface water – a preliminary report
<p><span>Water as a vehicle of antimicrobial resistance (AMR) is a well-known phenomenon. Therefore, One Health inspired surveillance campaign to detect circulation of antimicrobial resistance through the ecosystem are very often based on investigation of water samples. Bacteria belonging to the genera <em>Staphylococcus</em> and <em>Enterococcus</em> are good models in One Health research because of their ubiquitous occurrence and effortless isolation techniques. In this study, we aimed to compare different types of water sources as potential spreaders of AMR staphylococci and enterococci in the ecosystem. For this reason, we took water samples from the Drava River and small waterholes in a forest area. We hypothesised that rivers could collect diverse bacteria and resistance from the catchment area, while forest waterholes gather bacterial contamination from the local wildlife, thus a less diverse resistant bacterial community characterise them. From 22 and 10 water samples, collected from waterholes and the Drava River active floodplain, we isolated 21 and 13 bacterial strains, respectively. The bacterial community of the river proved more diverse, while waterhole samples contained resistant bacteria to more antimicrobials. In waterhole samples, we detected two methicillin resistant coagulase negative Staphylococcus strains. The most conspicuous difference between the two habitats was the predominance of multidrug resistant <em>S. sciuri</em> in the forest waterholes. We could conclude that water could be a good indicator of AMR contamination of the ecosystem, and large-scale sampling could reveal the exact epidemiological role of different water habitats.</span></p>
Supplementary data - Multiomic analysis of human kidney disease identifies a tractable inflammatory and pro-fibrotic tubular cell phenotype
Open the record for dataset details and reuse information.
Non-freely available datasets for the following work: "Comparison of consumption data and phenotypical antimicro-bial resistance in E. coli isolates of human urinary samples and of weaning and fattening pigs from surveillance and monitor-ing systems in Germany."
<p>Human and animal databases used for the creation of the work "Comparison of consumption data and phenotypical antimicrobial resistance in E. coli isolates of human urinary samples and of weaning and fattening pigs from surveillance and monitoring systems in Germany."</p>
Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity.
<p>This BBC_2024__README.txt file was generated on 2024-07-15 by Beatriz Baselga Cervera</p> <p>GENERAL INFORMATION</p> <p>1. Title of Dataset and code: Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity.</p> <p>2. Author Information</p> <p> Corresponding Investigator</p> <p> Name: Dr Beatriz Baselga-Cervera</p> <p> Institution: University of Minnesota Twin cities, Minnesota, US.</p> <p> Email: <a href="mailto:bbaselga@umn.edu">bbaselga@umn.edu</a>; beabaselga@gmail.com</p> <p> Co-investigator 1</p> <p> Name: Dr Noah Gettle</p> <p> Institution: Wellcome Sanger Institute, Hinxton, UK</p> <p> Email: noah.gettle@sanger.ac.uk</p> <p> Co-investigator 2</p> <p> Name: Dr Michael Travisano</p> <p> Institution: University of Minnesota Twin cities, Minnesota, US.</p> <p> Email:travisan@umn.edu</p> <p> </p> <p>4.Data collector: Dr Beatriz Baselga-Cervera</p> <p> </p> <p>5. Date of data collection: 2021-2022</p> <p> </p> <p>6. Geographic location of data collection: Saint Paul, US</p> <p> </p> <p>5. Funding sources that supported the collection of the data: Fundación Alfonso Martín Escudero, Madrid, Spain.</p> <p> </p> <p>6. Recommended citation for this dataset: Baselga-Cervera et al. (2024), Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity. Zenodo. Data set.</p> <p>DATA & FILE OVERVIEW</p> <p> </p> <p>1. Description of dataset</p> <p>In this study, we study the genotype to phenotype map that provides the basis for the diverse morphologies and unique adaptations observed in nascent multicellularity carried over from their unicellular ancestors under high and low nutrient availability conditions. We carried out a NetLogo agent-based model simulation of the multicellular snowflake yeast growth. Populations characterization was conducted with a Coulter Counter multisize 4 and FlowCam 3. The populations studied were the genetically uniform diploid wild-type <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). The population data represent the six independent isolates per strain in two different media, YPD and SD.</p> <p>2. File list:</p> <ul> <li>NetLogo Agent Base model: </li> <ul> <li>File 1 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Netlogo%20Snowflake%203D.nlogo3d?versionId=6fc2e8c0-02be-4956-875e-b4e48dd37163">Netlogo Snowflake 3D.nlogo3d</a> </li> </ul> </ul> <p> File 1 Description: Netlogo code for the 3D model/</p> <ul> <ul> <li>File name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Data_NETLOGO_model.csv?versionId=bb3e52cd-aac2-4f3e-a395-40a4995a1192">Data_NETLOGO_model.csv</a> </li> </ul> </ul> <p> File 2 Description: Data obtained from the Netlogo model.</p> <ul> <li>Coulter Counter size distribution data of all the populations: </li> <ul> <li>File 3 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Counter_Counter_Raw_data.xls?versionId=389afe4d-46c3-4569-92cb-ea73355a4ed7">Counter_Counter_Raw_data.xls</a></li> <li>File 3 description: Data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> <li>File 4 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Coulter_Counter_Raw_data_heterozygous_contructions.xlsx?versionId=e1fa6610-3f88-414c-b07a-03cc11b913e4">Coulter_Counter_Raw_data_heterozygous_contructions</a></li> <li>File 4 description: Size distributions of the heterozygous construct knockout (ACE2/<em>ace</em>2Δ) and homozygous missense (ACE2/<em>ace2Δ</em>) in YPD and SD at 24h growth. </li> </ul> <li>FlowCam image data:</li> <ul> <li>File 5 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/FlowCamPicturesData.zip?versionId=1e80636d-3022-4feb-828e-bc9552fef091">FlowCamPicturesData.zip</a></li> <li>File 5 description: Pictures generated by the FlowCam.</li> <li>File 6 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Flow_Cam_Raw%20data.xls?versionId=efd469dc-66f9-4504-8de3-f96c74f07f38">Flow_Cam_Raw data.xls</a></li> <li>File 6 desciption: FlowCam data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> <li>Data generated statistically:</li> <ul> <li>File 7 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_means.csv?versionId=48184a51-e3b5-407d-8ce1-ecdf5d7e1c99">bootstrapped_means.csv</a></li> <li>File 7 description: bootstrapped means from the Coulter Counter data to calculate the relative contributions to phenotypic variation.</li> <li>File 8 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_vars.csv?versionId=3ff5b6aa-6644-4264-8a6b-448c8a733689">bootstrapped_vars.csv</a></li> <li>File 8 description: bootstrapped variance from the Coulter Counter data to calculate the phenotypic noise.</li> <li>File 9 name: overlapPairs_values.xlsx </li> <li>File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data.</li> </ul> <li>R codes for data generated statistically:</li> <ul> <li>File 10 name: <a href="Code%20for%20bootstrapping.docx">R_code_bootstrapping.docx</a></li> <li>File 10 description: R code to obtain bootstrapped means and variance from the <a>Counter_Counter_Raw_data.xls</a> data to calculate the relative contributions to phenotypic variation.</li> <li>File 11 name: <a href="R%20code%20Overlap%20KDE%20distributions%20from%20the%20Coulter%20Counter%20data.docx">R_code_Overlap_KDE_distributions_from _the_Coulter_Counter_data</a></li> <li>File 11 description: R code to obtain overlapping indexes (η) of the Kernel density estimations (KDE) distributions from <a>Counter_Counter_Raw_data.xls</a></li> </ul> <li>R codes for figures:</li> <ul> <li>File 12 name: R_code_Fig2.docx</li> <li>File 12 description: R code for Figure 2 panels B and C. Panels are created from <a>Data_NETLOGO_model.csv</a>.</li> <li>File 13 name: R_code_Fig3.docx</li> <li>File 13 description: R code for Figure 3 panels A to E. Panels are created from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 13 name: R_code_Fig4.docx</li> <li>File 13 description: R code for Figure 4, data from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 14 name: R_code_FigS1.docx</li> <li>File 14 description: R code for Figure S1. Raw data from <a>Data_NETLOGO_model.csv</a> </li> <li>File 15 name: R_code_FigS2.docx</li> <li>File 15 description: R code for Figure S2. Raw data from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 16 name: R_code_FigS3.docx</li> <li>File 16 description: R code for Figure S3. Raw data from <a>Coulter_Counter_Raw_data_heterozygous_contructions</a>.xlsx</li> <li>File 16 name: R_code_FigS4.docx</li> <li>File 16 description: R code for Figure S4. Raw data from <a>Counter_Counter_Raw_data.xls</a></li> </ul> </ul> <p> </p> <p>METHODOLOGICAL INFORMATION</p> <p>Strains: ancestral wildtype (Y55 strains), C1W8.1 and C1W8.2 multicellular derived strains isolated after 60 days of selection in YPD media, constructed ACE2 gene knockouts, and strains containing the ACE2 missense mutation (ACE2 c.1934 A>T).</p> <p>Media: Growth media used in this study were Yeast Peptone Dextrose media (YPD; 1% (v/w) yeast extract, 2% (v/w) peptone, 2% (v/w) D-glucose, pH 5.8) and Standard minimal (SD; Yeast nitrogen base with amino acids (YNB w/AA) 6.7 g L-1, 0.5% (v/w) D-glucose).</p> <p>Phenotypic characterization of the different strains was conducted in a Coulter Counter Multisizer 4 and FlowCam® 3.0 Fluid Imaging Technologies. Replicate populations of different individual isolates per strain were analyzed to obtain the population distributions in both YPD and SD media.</p> <p>Agent Base Model data was generated in Netlogo (https://ccl.northwestern.edu/netlogo/).</p> <p> </p> <p>3. Detailed description</p> <ul> <li>NetLogo Agent Base model: </li> <ul> <li>File 1 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Netlogo%20Snowflake%203D.nlogo3d?versionId=6fc2e8c0-02be-4956-875e-b4e48dd37163">Netlogo Snowflake 3D.nlogo3d</a> </li> </ul> </ul> <p>o File 1 Description: Netlogo code for the 3D model.</p> <p>o File name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Data_NETLOGO_model.csv?versionId=bb3e52cd-aac2-4f3e-a395-40a4995a1192">Data_NETLOGO_model.csv</a> </p> <p>o File 2 Description: Data obtained from the Netlogo model.</p> <p>§ Page 1: Agent Base Model data generated with Netlogo.</p> <pre> Column 1: run number</pre> <pre> Column 2: size-of-turtle(cells)</pre> <p> Column 3: [step]</p> <p> Column 4: N</p> <p> Column 5: ticks(generations)</p> <pre> Column 6:distance-turtle(cells)</pre> <p> Column 7: diameter cells</p> <p> Column 8: radius cells</p> <p> Column 9: diameter cluster</p> <p> Column 10: radius cluster</p> <p> Column 11: volume</p> <p> Column 12: SAVr (surface area/volume ratio)</p> <p> Column 13: packing</p> <p> Column 14: Ratio</p> <ul> <li>Coulter Counter size distribution data of all the populations: </li> <ul> <li>File 3 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Counter_Counter_Raw_data.xls?versionId=389afe4d-46c3-4569-92cb-ea73355a4ed7">Counter_Counter_Raw_data.xls</a></li> <li>File 3 description: Data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: Coulter Coulter data runs at 24h</p> <p> Column 1: Volumen (um3)</p> <p> Column 2: Diameter (um2)</p> <p>Column 3: ace2Δ _k_1_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 4: ace2Δ _k_1_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 5: ace2Δ _k_2_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 6: ace2Δ _k_2_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 7: ace2Δ _k_3_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 8: ace2Δ _k_3_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 9: ace2Δ _k_4_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is SD media.</p> <p>Column 10: ace2Δ _k_4_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is YPD media.</p> <p>Column 11: ace2Δ _k_5_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 12: ace2Δ _k_5_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 13: ace2Δ _k_6_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 14: ace2Δ _k_6_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is YPD media.</p> <p>Column 15: ace2Δ _m_1_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 16: ace2Δ _m_1_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 17: ace2Δ _m_2_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 18: ace2Δ _m_2_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 19: ace2Δ _m_3_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 20: ace2Δ _m_3_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 21: ace2Δ _m_4_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is SD media.</p> <p>Column 22: ace2Δ _m_4_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is YPD media.</p> <p>Column 23: ace2Δ _m_5_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 24: ace2Δ _m_5_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 25: ace2Δ _m_6_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 26: ace2Δ _m_6_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is YPD media.</p> <p>Column 27: C1W8.1 _m_1_SD. C1W8.1 evolved strain isolate one is SD media.</p> <p>Column 28: C1W8.1 _m_1_YPD. C1W8.1 evolved strain isolate one is YPD media.</p> <p>Column 29: C1W8.1 _m_2_SD. C1W8.1 evolved strain isolate two is SD media.</p> <p>Column 30: C1W8.1 _m_2_YPD. C1W8.1 evolved strain isolate two is YPD media.</p> <p>Column 31: C1W8.1 _m_3_SD. C1W8.1 evolved strain isolate three is SD media.</p> <p>Column 32: C1W8.1 _m_3_YPD. C1W8.1 evolved strain isolate three is YPD media.</p> <p>Column 33: C1W8.1 _m_4_SD. C1W8.1 evolved strain isolate four is SD media.</p> <p>Column 34: C1W8.1 _m_4_YPD. C1W8.1 evolved strain isolate four is YPD media.</p> <p>Column 35: C1W8.1 _m_5_SD. C1W8.1 evolved strain isolate five is SD media.</p> <p>Column 36: C1W8.1 _m_5_YPD. C1W8.1 evolved strain isolate five is YPD media.</p> <p>Column 37: C1W8.1 _m_6_SD. C1W8.1 evolved strain isolate six is SD media.</p> <p>Column 38: C1W8.1 _m_6_YPD. C1W8.1 evolved strain isolate sic is YPD media.</p> <p>Column 39: C1W8.2 _m_1_SD. C1W8.2 evolved strain isolate one is SD media.</p> <p>Column 40: C1W8.2 _m_1_YPD. C1W8.2 evolved strain isolate one is YPD media.</p> <p>Column 41: C1W8.2 _m_2_SD. C1W8.2 evolved strain isolate two is SD media.</p> <p>Column 42: C1W8.2 _m_2_YPD. C1W8.2 evolved strain isolate two is YPD media.</p> <p>Column 43: C1W8.2 _m_3_SD. C1W8.2 evolved strain isolate three is SD media.</p> <p>Column 44: C1W8.2 _m_3_YPD. C1W8.2 evolved strain isolate three is YPD media.</p> <p>Column 45: C1W8.2 _m_4_SD. C1W8.2 evolved strain isolate four is SD media.</p> <p>Column 46: C1W8.2 _m_4_YPD. C1W8.2 evolved strain isolate four is YPD media.</p> <p>Column 47: C1W8.2 _m_5_SD. C1W8.2 evolved strain isolate five is SD media.</p> <p>Column 48: C1W8.2 _m_5_YPD. C1W8.2 evolved strain isolate five is YPD media.</p> <p>Column 49: C1W8.2 _m_6_SD. C1W8.2 evolved strain isolate six is SD media.</p> <p>Column 50: C1W8.2 _m_6_YPD. C1W8.2 evolved strain isolate sic is YPD media.</p> <p>Column 51: Y55_1_SD. Y55 ancestral strain isolate one is SD media.</p> <p>Column 52: Y55_1_SD. Y55 ancestral strain isolate one is YPD media.</p> <p>Column 53: Y55_2_SD. Y55 ancestral strain isolate two is SD media.</p> <p>Column 54 Y55_2_SD. Y55 ancestral strain isolate two is YPD media.</p> <p>Column 55: Y55_3_SD. Y55 ancestral strain isolate three is SD media.</p> <p>Column 56: Y55_3_SD. Y55 ancestral strain isolate three is YPD media.</p> <p>Column 57: Y55_4_SD. Y55 ancestral strain isolate four is SD media.</p> <p>Column 58: Y55_4_SD. Y55 ancestral strain isolate four is YPD media.</p> <p>Column 59: Y55_5_SD. Y55 ancestral strain isolate five is SD media.</p> <p>Column 60: Y55_5_SD. Y55 ancestral strain isolate five is YPD media.</p> <p>Column 61: Y55_6_SD. Y55 ancestral strain isolate six is SD media.</p> <p>Column 62: Y55_6_SD. Y55 ancestral strain isolate sic is YPD media.</p> <p> </p> <p> </p> <p>o File 4 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Coulter_Counter_Raw_data_heterozygous_contructions.xlsx?versionId=e1fa6610-3f88-414c-b07a-03cc11b913e4">Coulter_Counter_Raw_data_heterozygous_contructions</a></p> <ul> <ul> <li>File 4 description: Size distributions of the heterozygote construct knockout (ACE2/<em>ace</em>2Δ) and heterozygous missense construct (ACE2/<em>ace2Δ</em>) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: Coulter Coulter data runs at 24h</p> <p> Column 1: Volumen (um3)</p> <p> Column 2: Diameter (um2)</p> <p>Column 3: ACE2/ace2Δ missense_1_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 4: ACE2/ace2Δ missense_1_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 5: ACE2/ace2Δ missense_2_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 6: ACE2/ace2Δ missense_2_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 7: ACE2/ace2Δ missense_3_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 8: ACE2/ace2Δ missense_3_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 9: ACE2/ace2Δ knockout_1_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 10: ACE2/ace2Δ knockout _1_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 11: ACE2/ace2Δ knockout _2_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 12: ACE2/ace2Δ knockout _2_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 13: ACE2/ace2Δ knockout _3_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 14: ACE2/ace2Δ knockout _3_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate three is YPD media.</p> <ul> <li>FlowCam image data:</li> <ul> <li>File 5 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/FlowCamPicturesData.zip?versionId=1e80636d-3022-4feb-828e-bc9552fef091">FlowCamPicturesData.zip</a></li> <li>File 5 description: Pictures generated by the FlowCam.</li> <li>File 6 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Flow_Cam_Raw%20data.xls?versionId=efd469dc-66f9-4504-8de3-f96c74f07f38">Flow_Cam_Raw data.xls</a></li> <li>File 6 desciption: FlowCam data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: FlowCam data runs at 24 hours of Y55 strain.</p> <p> Column 1:Particle ID</p> <p> Column 2: Class. Classification as; single cells and mother-daughter(s).</p> <p> Column 3: Area ABD</p> <p> Column 4: Aspect Ratio (Width/Length)</p> <p> Column 5: Circle Fit</p> <p> Column 6: Area base Diameter (ABD)</p> <p> Column 7: Equivalent Spherical Diameter (ESD)</p> <p> Column 8: Elongation</p> <p> Column 9: Perimeter</p> <p> Column 10: Roughness</p> <p> Column 11: Volume ESD-based</p> <p> Column 12: Width</p> <p> Column 13: Source. Name of the sample.</p> <p> Column 14: Strain Y55.</p> <p> Column 15: media. Values: YPD and SD</p> <p> Column 16: clone. Isolate.</p> <p>§ Page 2: FlowCam data runs at 24 hours of C1W8.1 multicellular strain.</p> <p> Column 1: Particle ID</p> <p> Column 2: Area ABD</p> <p> Column 3: Aspect Ratio (Width/LEngth)</p> <p> Column 4: Circle Fit</p> <p> Column 5: Area base Diameter (ABD)</p> <p> Column 6: Equivalent Spherical Diameter (ESD)</p> <p> Column 7: Elongation</p> <p> Column 8: Perimeter</p> <p> Column 9: Roughness</p> <p> Column 10: Volume ESD-based</p> <p> Column 11: Width</p> <p> Column 12: Source. Name of the sample.</p> <p> Column 13: Strain C1W8.1.</p> <p> Column 14: media. Values: YPD and SD</p> <p> Column 15: clone. Isolate.</p> <p>§ Page 3: FlowCam data runs at 24hours of C1W8.2 multicellular strain.</p> <p> Column 1: Particle ID</p> <p> Column 2: Area ABD</p> <p> Column 3: Aspect Ratio (Width/LEngth)</p> <p> Column 4: Circle Fit</p> <p> Column 5: Area base Diameter (ABD)</p> <p> Column 6: Equivalent Spherical Diameter (ESD)</p> <p> Column 7: Elongation</p> <p> Column 8: Perimeter</p> <p> Column 9: Roughness</p> <p> Column 10: Volume ESD-based</p> <p> Column 11: Width</p> <p> Column 12: Source. Name of the sample.</p> <p> Column 13: Strain C1W8.2.</p> <p> Column 14: media. Values: YPD and SD</p> <p> Column 15: clone. Isolate.</p> <p> </p> <ul> <li>Data generated statistically:</li> <ul> <li>File 7 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_means.csv?versionId=48184a51-e3b5-407d-8ce1-ecdf5d7e1c99">bootstrapped_means.csv</a></li> <li>File 7 description: bootstrapped means from the Coulter Counter data to calculate the relative contributions to phenotypic variation.</li> </ul> </ul> <p>§ Page 1: Bootstrap mean values.</p> <p> Column 1: row number</p> <p> Column 2: sample. ID of the sample from whom the value was generated.</p> <p> Column 3: diameter_um. Mean diameter (um) values.</p> <ul> <ul> <li>File 8 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_vars.csv?versionId=3ff5b6aa-6644-4264-8a6b-448c8a733689">bootstrapped_vars.csv</a></li> <li>File 8 description: bootstrapped variance from the Coulter Counter data to calculate the phenotypic noise.</li> </ul> </ul> <p>§ Page 1: Bootstrap variance values.</p> <p> Column 1: row number</p> <p> Column 2: sample. ID of the sample from whom the value was generated.</p> <p> Column 3: diameter_um. Variance in diameter (um) values.</p> <ul> <ul> <li>File 9 name: <code>overlapPairs_values.xlsx</code> </li> <li>File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data.</li> </ul> </ul> <p>§ Page 1: Overlapping indexes (η) of the KDE distributions by strain, media and isolate.</p> <p> Column 1: var1. Population 1.</p> <p> Column 2: var2. Population 2.</p> <p> Column 3: value. Overlap value.</p> <p>§ Page 2: Overlapping indexes (η) of the KDE distributions by strain and media.</p> <p> Column 1: var1. Strain 1.</p> <p> Column 2: var2. Strain 2.</p> <p> Column 3: value. Overlap value.</p> <p> Column 4: media.</p> <p> </p> <p> </p>
Zea mays biparental RIL population - growth chamber phenotyping data
<p>MIAPPE compliant ISA-tab files of the Zea mays B73xH99 RIL population. This includes final leaf size and whole shoot traits at the seedling stage, complemented with measurements capturing growth dynamics, and cellular measurements.</p>
Data from: A population genomic scan in Chorthippus grasshoppers unveils previously unknown phenotypic divergence
Understanding the genetics of speciation and the processes that drive it is a central goal of evolutionary biology. Grasshoppers of the Chorthippus species group differ strongly in calling song (and corresponding female preferences) but are exceedingly similar in other characteristics such as morphology. Here, we performed a population genomic scan on three Chorthippus species (Chorthippus biguttulus, C. mollis and C. brunneus) to gain insight into the genes and processes involved in divergence and speciation in this group. Using an RNA-seq approach, we examined functional variation between the species by calling SNPs for each of the three species pairs and using FST-based approaches to identify outliers. We found approximately 1% of SNPs in each comparison to be outliers. Between 37% and 40% of these outliers were nonsynonymous SNPs (as opposed to a global level of 17%) indicating that we recovered loci under selection. Among the outliers were several genes that may be involved in song production and hearing as well as genes involved in other traits such as food preferences and metabolism. Differences in food preferences between species were confirmed with a behavioural experiment. This indicates that multiple phenotypic differences implicating multiple evolutionary processes (sexual selection and natural selection) are present between the species.
Data from: Using the generalized index of dissimilarity to detect gene-gene interactions in multi-class phenotypes
To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genetic association studies. It commonly assumes a dichotomous classification of the binary phenotypes. Its usual approach to determining the genomic association is to construct a confusion matrix to estimate a classification error, where a binary risk status is determined and assigned to each genotypic multifactor class. While multi-class phenotypes are commonly observed, the current MDR approach does not handle these phenotypes appropriately because the thresholds for the risk statuses may not be clear. In this study, we suggest a new method for estimating gene-gene interactions for multi-class phenotypes. Our approach adopts the index of dissimilarity (IDS) as an evaluation measure. This is analytically equivalent to the common association measure of balanced accuracy (BA) for the binary traits, while it is not required to determine the risk status for the estimation. Moreover, it is easily expandable to the generalized index of dissimilarity (GIDS), which has an explicit form that can handle any number of categories. The performance of the proposed method was compared with those of other approaches via simulation studies in which fifteen genetic models were generated with three class outcomes. A consistently better performance was observed using the proposed method. The effect of a varying number of categories was examined. The proposed method was also illustrated using real genome-wide association studies (GWAS) data from the Korean Association Resource (KARE) project.
Data from: Comparing evolutionary rates for different phenotypic traits on a phylogeny using likelihood
In recent years, likelihood-based approaches have been used with increasing frequency to evaluate macroevolutionary hypotheses of phenotypic evolution under distinct evolutionary processes in a phylogenetic context (e.g., Brownian motion, Ornstein-Uhlenbeck, etc.), and to compare one or more evolutionary rates for the same phenotypic trait along a phylogeny. It is also of interest to determine whether one trait evolves at a faster rate than another trait. However, to date no study has compared phylogenetic evolutionary rates between traits using likelihood, because a formal approach has not yet been proposed. In this article, I describe a new likelihood procedure for comparing evolutionary rates for two or more phenotypic traits on a phylogeny. This approach compares the likelihood of a model where each trait evolves at a distinct evolutionary rate to the likelihood of a model where all traits are constrained to evolve at a common evolutionary rate. The method can also account for within-species measurement error and within-species trait covariation if available. Simulations revealed that the method has appropriate Type I error rates and statistical power. Importantly, when compared with existing approaches based on phylogenetically independent contrasts and methods that compare confidence intervals for model parameters, the likelihood method displays preferable statistical properties for a wide range of simulated conditions. Thus, this likelihood-based method extends the phylogenetic comparative biology toolkit and provides evolutionary biologists with a more powerful means of determining when evolutionary rates differ between phenotypic traits. Finally, I provide an empirical example illustrating the approach by comparing rates of evolution for several phenotypic traits in Plethodon salamanders.
Data from: Detection of human disease conditions by single-cell morpho-rheological phenotyping of blood
Blood is arguably the most important bodily fluid and its analysis provides crucial health status information. A first routine measure to narrow down diagnosis in clinical practice is the differential blood count, determining the frequency of all major blood cells. What is lacking to advance initial blood diagnostics is an unbiased and quick functional assessment of blood that can narrow down the diagnosis and generate specific hypotheses. To address this need, we introduce the continuous, cell-by-cell morpho-rheological (MORE) analysis of diluted whole blood, without labeling, enrichment or separation, at rates of 1,000 cells/sec. In a drop of blood we can identify all major blood cells and characterize their pathological changes in several disease conditions in vitro and in patient samples. This approach takes previous results of mechanical studies on specifically isolated blood cells to the level of application directly in blood and adds a functional dimension to conventional blood analysis.
Data from: The effects of genotype × phenotype interactions on transcriptional response to silver nanoparticle toxicity in organotypic cultures of murine tracheal epithelial cells
The airway epithelium is critical for maintaining innate and adaptive immune responses, and occupational exposures that disrupt its immune homeostasis may initiate and amplify airway inflammation. In our previous study, we demonstrated that silver nanoparticles (AgNP), which are engineered nanomaterials used in multiple applications but primarily in the manufacturing of many antimicrobial products, induce toxicity in organotypic cultures derived from murine tracheal epithelial cells (MTEC), and those differentiated toward a "Type 2 [T2]-Skewed" phenotype experienced an increased sensitivity to AgNP toxicity, suggesting that asthmatics could be a sensitive population to AgNP exposures in occupational settings. However, the mechanistic basis for this genotype × phenotype interaction (G×P) has yet to be defined. In the present study, we conducted transcriptional profiling using RNA-sequencing (RNA-seq) to predict the enrichment of specific canonical pathways and upstream transcriptional regulators to assist in defining a mechanistic basis for G×P effects on AgNP toxicity. Organotypic cultures were derived from MTEC across two genetically inbred mouse strains (A/J and C57BL/6J mice), two phenotypes ("Normal" and "T2-Skewed"), and one AgNP exposure (an acute 24 h exposure) to characterize G×P effects on transcriptional response to AgNP toxicity. The "T2-Skewed" phenotype was marked by increased pro-inflammatory T17 responses to AgNP toxicity, which are significant predictors of neutrophilic/difficult-to-control asthma and suggests that asthmatics could be a sensitive population to AgNP exposures in occupational settings. This study highlights the importance of considering G×P effects when identifying these sensitive populations, whose underlying genetics or diseases could directly modify their response to AgNP exposures.
Data for "Hypotheses on the extended phenotype of the mitochondrion"
<p>Mitochondrial genome data associated with the manuscript "Hypotheses on the extended phenotype of the mitochondrion".</p> <p>This is simply a particular RefSeq mitochondrial data release. Note that NCBI does not appear to maintain an archive of past RefSeq mitochondrial data files.<br> </p>
Phenotypic Exploration of Autism Spectrum Disorders Retrospective and Prospective Data
ClinicalTrials.gov study NCT04923854. IPD Sharing: NO. Countries: 1. Publications: 0.
National Ophthalmic Genotyping and Phenotyping Network (eyeGENE (Registered Trademark)), Stage 3 - Expansion of DNA and Data Repositories for Rare Inherited Ophthalmic Diseases
ClinicalTrials.gov study NCT06491615. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Comparing evolutionary rates for different phenotypic traits on a phylogeny using likelihood
Open the record for dataset details and reuse information.
Data from: Phenotypic interactions between tree hosts and invasive forest pathogens in the light of globalization and climate change
Open the record for dataset details and reuse information.
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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.