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103 results for “phenotype prediction”

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zenodo48/100

Prediction and analysis of phenotypes in the Arabidopsis clock mutant prr7prr9 using the Framework Model v2 (FMv2)

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 prr7prr9 double mutant compared to the Col wild type plants, with controls in lsf1 and prr7 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>. 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 pgm, lhy cca1, and in one case, toc1 and gi. 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. 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.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Predicting Phenotypic Traits Using a Massive RNA-seq Dataset

<h2>Abstract</h2>The included datasets are a conglomerate of all available Arabidopsis thaliana RNA-seq data available from NCBI as of November 2022 processed to count data. In addition, the associated annotation files from NCBI BioProject database and processed versions of this data is included. Data has been processed according to the "Data Description Methods" in the manuscript titled "Predicting Phenotypic Traits Using a Massive RNA-seq Dataset" (in publication). The associated Methods can be found at this repository:<a href="https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics"> https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics</a>. These datasets can be used for exploring machine learning methods for predicting both continuous (Age) and categorical (Tissue) phenotypic traits using gene expression. Additionally, the gene expression data can be used on its own for the investigation of gene expression in Arabidopsis thaliana.<h3>Note to Researchers</h3>This repository contains all of the datasets and information necessary to recreate the experiments in our paper. However, if may be that you are interested in our dataset for testing your own hypotheses/programs. If this is the case, we predict that you are looking for one or more of the the following 5 datasets  <h3>Note on File Compression</h3>All files in this repository are compressed using bzip2 to conserve space and allow for easier file transfer. The unzip command on linux systems is `bzip2 -d FILE_NAME`. For other computer systems (Windows and Apple) please consult your user manual.<h3>Description All Datasets:</h3>Title: Gene Expression Count Data of all Arabidopsis thaliana data available from NCBI SRA as of November 2022 Abstract: Gene Expression Count data was created using the workflow GEMmaker. The resulting Gene Expression Matrix (GEM) was then normalized and thresholded. The following 4 files are normalizations of the same data for Trimmed Mean of M values (TMM), Median Ratios Normalization (MRN), Transcripts Per kilobase Million (TPM), and No Normalization (NoNo) respectively. Additionally, Each file is included as a tsv and a python pickle. The tsv file is human readable, whereas the pickle file can be read into memory substantially faster. Format for tsv is each row represents a sample and each column represents a gene. NCBI_Nov2022_SRR_runinfo.csv is the starting file from NCBI which reports SRR information for each sample. Note 1 to Researchers: MRN normalization performed the best in our experiments and is likely what you want to use if you are doing additional expermentation with this dataset. Otherwise start with NoNo and perform your own normalizations. Note 2 to Researchers: the 54547 dataset will need to be thresholded prior to use. We include it in addition to the 32432 datasets in case you wish to try a different thresholding to the one outlined in our manuscript.  Author: John Anthony Hadish Data Type: Gene Expression Count Data Organism: Arabidopsis thaliana Files:NCBI_Nov2022_SRR_runinfo.csv - Arabidopsis RNA-seq SRA RunInfo Retrieved from NCBI November 2022. This is the unprocessed data. Dataset_54547_NoFilter_raw.pkl - Raw File Before thresholding (".pkl" format). Same as NoNo normalization without thresholding. Dataset_54547_NoFilter_raw.tsv - Raw File Before thresholding (".tsv" format). Same as NoNo normalization without thresholding. Dataset_32432_MRN.pkl - MRN normalized (".pkl" format) Dataset_32432_MRN.tsv - MRN normalized (".tsv" format) Dataset_32432_NoNo.pkl - NoNo normalized (".pkl" format) Dataset_32432_NoNo.tsv - NoNo normalized (".tsv" format) Dataset_32432_TMM.pkl - TMM normalized (".pkl" format) Dataset_32432_TMM.tsv - TMM normalized (".tsv" format) Dataset_32432_TPM.pkl - TPM normalized (".pkl" format) Dataset_32432_TPM.tsv - TPM normalized (".tsv" format) Title: Meta Data Arabidopsis Age and Tissue Abstract: Meta Data for Age and Tissue after processing. In our experiment this was used as response variable to gene expression. Shared columns are "bio_sample", "bioproject_name", "experiment". In addition to these processed datasets, NCBI_Nov2022_BioSample_data.tsv is the unprocessed starting material for these two data frames. Author: John Anthony Hadish Data Type: Metadata on phenotypes. ".tsv" format  Organism: Arabidopsis thaliana Files:  NCBI_Nov2022_BioSample_data.tsv - Arabidopsis BioSample data retrieved from NCBI November 2022. This is the unprocessed data. df_metadata_tissue.tsv - Tissue Annotations for 24876 samples df_metadata_age.tsv - Age Annotations for 16078 samples. In addition to shared columns includes "days_age"(how many days old the sample is converted to days) and "annotation_age" (how the annotation was reported for this sample in the raw data file-- i.e. "days", "weeks" etc.) Title: Machine Learning Dataset for Arabidopsis thaliana Age Abstract: The dataset used for Machine learning on the phenotype Age that is a combination of the Gene Expression Matrix and the Annotation Matrix. Consists of a list of 4 for the train and test splits. Author: John Anthony Hadish Data Type: Gene Expression Matrix and Annotations Combined, split into train and test  Organism: Arabidopsis thaliana Files: Dataset_Age_TrainTestSplits_mrn.pkl - MRN normalized Dataset_Age_TrainTestSplits_NoNo.pkl - NoNo normalized Dataset_Age_TrainTestSplits_tmm.pkl - TMM normalized Dataset_Age_TrainTestSplits_tpm.pkl - TPM normalized Title: Machine Learning Dataset for Arabidopsis thaliana Tissue Abstract: The dataset used for Machine learning on the phenotype Tissue that is a combination of the Gene Expression Matrix and the Annotation Matrix. Consists of a list of 4 for the train and test splits. Saved as python ".pkl" files. Author: John Anthony Hadish Data Type: Gene Expression Matrix and Annotations Combined, split into train and test. Saved as python ".pkl" files. Organism: Arabidopsis thaliana Files: Dataset_Tissue_TrainTestSplits_mrn.pkl - MRN normalized Dataset_Tissue_TrainTestSplits_NoNo.pkl - NoNo normalized Dataset_Tissue_TrainTestSplits_tmm.pkl - TMM normalized Dataset_Tissue_TrainTestSplits_tpm.pkl - TPM normalized Dataset_Tissue_TrainTestSplits_mrn_4category.pkl - MRN for the tissue-4 dataset Title: BioProject Names Abstract: Three Column File With BioProject Name, BioSample Name, and Experiment Name Author: John Anthony Hadish Data Type: ".tsv" Organism: Arabidopsis thaliana Files: BioProject_Names_All.tsv Title: Manuscript Supplemental Material Abstract: Supplemental tables and figures described in the manuscript (included with manuscript and here for convenience). Please see manuscript for additional information. Author: John Anthony Hadish Data Type: ".tsv", ".png".pdf" Organism: Arabidopsis thaliana Files: Supplemental_Figures.zip - Supplemental figures from the manuscript. Includes description of each figure. Supplemental_Tables.zip - Supplemental tables from the manuscript. Includes description of each table.  Title: Splits of data for 3 experiments Abstract: 2 column tsv files. The first column is the experiment (sample) name, and the second column is if it is included in the train or test data. Included here to make sure pkl files are reproducible in case the pkl package breaks in the future. Not used by scripts, included to prevent future potential loss of data. Author: John Anthony Hadish Data Type: ".tsv" Organism: Arabidopsis thaliana Files: Dataset_Tissue_TrainTestSplits_4category_namesOnly.tsv Dataset_Tissue_TrainTestSplits_namesOnly.tsv Dataset_Age_TrainTestSplits_namesOnly.tsv Title: Git Code Repository Abstract: A tar bz2 compression of the git repository containing all of the code created for this manuscript. The same code found in this file is also avalible on GitLab at the link: https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics Author: John Anthony Hadish Data Type: Git Repository, python code Files: modeling-with-transcriptomics-main.tar.bz2 - Compressed Git repository of all code used in paper.

opencc-by-4.0Nov 2023View details →

zenodo44/100

Ontology based text mining of gene-phenotype associations: application to candidate gene prediction

Gene-phenotype associations play an important role in understanding   the disease mechanisms which is a requirement for treatment   development. A portion of gene-phenotype associations are observed   mainly experimentally and made publicly available through several   standard resources such as MGI. However, there is still a vast   amount of gene--phenotype associations buried in the biomedical   literature. Given the large amount of literature data, we need   automated text mining tools to alleviate the burden in manual   curation of gene-phenotype associations and to develop   comprehensive resources. We developed an ontology based   approach in combination with statistical methods to text mine   gene-phenotype associations from literature. Our method achieved   AUC values of 0.90 and 0.75 in recovering known gene-phenotype   associations from HPO and MGI respectively. We posit that candidate   genes and their relevant diseases should be expressed with similar   phenotypes in publications. Thus, we demonstrate the utility of our   approach by predicting disease candidate genes based on the semantic   similarities of phenotypes associated with genes and diseases. We evaluated our disease candidate prediction model on   the gene-disease associations from MGI. Our model achieved AUC   values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of   gene-disease associations respectively. Our manual analysis on the   text mined data revealed that, our method can accurately extract   gene-phenotype associations which are not currently covered by the   existing public gene-phenotype resources. Overall, results indicate   that our method can precisely extract known as well as new   gene-phenotype associations from literature. This released dataset at Zenodo covers our gene-phenotype extracts from the literature. All the methods used to extract the data are available at https://github.com/bio-ontology-research-group/genepheno.

opencc-by-4.0Jan 2019View details →

zenodo40/100

# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.

opencc-by-4.0Jan 2022View details →

dryad40/100

Data and Scripts from: Bayesian prediction of multivariate ecology from phenotypic data yields novel insights into the diets of extant and extinct taxa

Morphology often reflects ecology, enabling the prediction of ecological roles for taxa that lack direct observations such as fossils. In comparative analyses, ecological traits, like diet, are often treated as categorical, which may aid prediction and simplify analyses but ignores the multivariate nature of ecological niches. Futhermore, methods for quantifying and predicting multivariate ecology remain rare. Here, we ranked the relative importance of 13 food items for a sample of 88 extant carnivoran mammals, and then used Bayesian multilevel modeling to assess whether those rankings could be predicted from dental morphology and body size. Traditional diet categories fail to capture the true multivariate nature of carnivoran diets, but Bayesian regression models derived from living taxa have good predictive accuracy for importance ranks. Using our models to predict the importance of individual food items, the multivariate dietary niche, and the nearest extant analogs for a set of data-deficient extant and extinct carnivoran species confirms long-standing ideas for some taxa, but yields new insights about the fundamental dietary niches of others. Our approach provides a promising alternative to traditional dietary classifications. Importantly, this approach need not be limited to diet, but serves as a general framework for predicting multivariate ecology from phenotypic traits.

opencc-zeroNov 2022View details →

zenodo40/100

Data and code of "Post-trauma behavioral phenotype predicts the degree of vulnerability to fear relapse after extinction in male rats"

This dataset contains behavioral and transcriptomic data, and the original code related to the following article: Post-trauma behavioral phenotype predicts the degree of vulnerability to fear relapse after extinction in male rats. Fanny Demars, Ralitsa Todorova, Gabriel Makdah, Antonin Forestier, Marie-Odile Krebs, Bill P Godsil, Thérèse M Jay, Sidney I Wiener, & Marco N Pompili (2022) Current Biology 32. https://doi.org/10.1016/j.cub.2022.05.050

opencc-by-4.0Jun 2022View details →

zenodo40/100

Black Soldier Fly detection, phenotyping and sex prediction dataset

BSF_Larvae for training the detection and segmentation model consists of 321 images with over 5000 larvae instances.  Single_Larvae_data consists of 1608 single larvae images for the measurement of body weight, length and width of the individual larva.  The Larvae size  excel sheet contains the size and sex data corresponding to the individual larva.  The Larvae sex classification dataset includes images cropped from the Single_Larvae_data for sex classification.

opencc-by-4.0Aug 2024View details →

dryad40/100

Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments

A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from−0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.

opencc-zeroSep 2021View details →

dryad40/100

Data and Scripts from: Bayesian prediction of multivariate ecology from phenotypic data yields novel insights into the diets of extant and extinct taxa

Open the record for dataset details and reuse information.

publicApr 2023View details →

dryad40/100

Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments

Open the record for dataset details and reuse information.

publicSep 2021View details →

dryad36/100

Data from: Genetic growth potential, rather than phenotypic size, predicts migration phenotype in Atlantic salmon

Knowledge of the relative importance of genetic versus environmental determinants of major developmental transitions is pertinent to understanding phenotypic evolution. In salmonid fishes, a major developmental transition enables a risky seaward migration that provides access to feed resources. In Atlantic salmon, initiation of the migrant phenotype, and thus age of migrants, is presumably controlled via thresholds of a quantitative liability, approximated by body size expressed long before the migration. However, how well size approximates liability, both genetically and environmentally, remains uncertain. We studied 32 Atlantic salmon families in two temperatures and feeding regimes (fully fed, temporarily restricted) to completion of migration status at age 1 year. We detected a lower migrant probability in the cold (0.42) than the warm environment (0.76), but no effects of male maturation status or feed restriction. By contrast, body length in late summer predicted migrant probability and its control reduced migrant probability heritability by 50–70%. Furthermore, migrant probability and length showed high heritabilities and between-environment genetic correlations, and were phenotypically highly correlated with stronger genetic than environmental contributions. Altogether, quantitative estimates for the genetic and environmental effects predicting the migrant phenotype indicate, for a given temperature, a larger importance of genetic than environmental size effects.

opencc-zeroJul 2020View details →

dryad36/100

Trait-fitness associations do not predict within-species phenotypic evolution over 2 million years

Long-term patterns of phenotypic change are the cumulative results of tens of thousands to millions of years of evolution. Yet, empirical and theoretical studies of phenotypic selection are largely based on contemporary populations. The challenges in studying phenotypic evolution, in particular trait-fitness associations in the deep past, are barriers to linking micro- and macroevolution. Here, we capitalize on the unique opportunity offered by a marine colonial organism commonly preserved in the fossil record to investigate trait-fitness associations over 2 million years. We use the density of female polymorphs in colonies of Antartothoa tongima as a proxy for fecundity, a fitness component, and investigate multivariate signals of trait-fitness associations in six time intervals on the backdrop of Pleistocene climatic shifts. We detect negative trait-fitness associations for feeding polymorph (autozooid) sizes, positive associations for autozooid shape but no particular relationship between fitness and brood chamber size. In addition, we demonstrate that long-term trait patterns are explained by palaeoclimate and to a lesser extent by ecological interactions. Our analyses show that macroevolutionary outcomes of trait evolution are not a simple scaling-up from the trait-fitness associations.

opencc-zeroJan 2021View details →

dryad36/100

Data from: Phenotype-environment matching predicts both positive and negative effects of intraspecific variation

Natural populations can vary considerably in their genotypic and/or phenotypic diversity. Differences in this intraspecific diversity can have important consequences for contemporary ecological dynamics, but the direction and magnitude of these effects appear inconsistent across studies and systems. Here we proposed and tested the hypothesis that context-dependent ecological effects of altering phenotypic variance are predictable and arise from the relationship between a population's mean phenotype and the local environmental optimum. By factorially manipulating the mean and variance of a key host trait in environments with and without a lethal parasite, we demonstrate that increasing phenotypic variance can have beneficial effects for host populations (e.g. smaller disease epidemics), but only when the population's initial phenotype was poorly-matched to the local environment. When phenotypes were initially well-suited to environmental conditions, in contrast, greater phenotypic variance led to larger disease epidemics. Significant reductions in individual susceptibility occurred in both contexts over time, but the mechanisms leading to those reductions differed; strong selection was caused by either a 'suboptimal' trait mean and insufficient trait variance, or a 'near-optimal' trait mean and too much trait variance. Increasing intraspecific variation is clearly not always beneficial for populations, instead producing predictable ecological and evolutionary effects that depend on environmental context and biological interactions.

opencc-zeroDec 2018View details →

zenodo36/100

Data and software associated with PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources

Data and software associated with the paper: PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources

opencc-zeroJun 2015View details →

dryad36/100

Data from: Phenotypically plastic responses to environmental variation are more complex than life history theory predicts

For insects that exhibit wing polyphenic development, abiotic and biotic signals dictate the adult wing morphology of the insect in an adaptive manner such that in stressful environments the formation of a flight-capable morph is favored and in low stress environments a flightless morph is favored. While there is a relatively large amount known about the environmental cues that dictate morph formation in wing polyphenic hemipterans like planthoppers and aphids, whether those cues dictate the same morphs in non-hemipteran (i.e. cricket) wing polyphenic species has not been explicitly investigated. To experimentally test the generality of environmental cue determination of wing polyphenism across taxa with diverse life histories, in this study we tested the importance of food quantity, parasitic infection, and tactile cues on wing morph determination in the wing polyphenic sand field cricket, Gryllus firmus. Our results also show that certain stress cues, such as severe diet quantity limitation and parasitic infection, actually led to an increase in the production of flightless morph. Based on these findings, our results suggest that physiological and genetic constraints are important to an organism's ability to respond to environmental variation in an adaptive manner beyond simple life history trade-offs.

opencc-zeroJun 2024View details →

zenodo36/100

Predicting Phenotypic Traits Using Conglomerate RNA-seq Datasets -- Datasets

Phenotype Annotations and Corresponding Gene Expression Matrices created for the paper Hadish et al. [REF]. Raw Phenotypic Annotations were downloaded and processed from NCBI BioProject and raw RNA-seq data was downloaded from NCBI SRA. All annotations are contained within a single file for either Tissue or Age. Gene expression matrices are for each of the 12 species used in this project in their unproccessed form.  Scripts used in this project are located here:  GitLab Repository:  https://gitlab.com/ficklin-lab/predicting-phenotypic-traits-using-conglomerate-rna-seq-datasets Paper Citation:  [REF]

opencc-by-4.0Aug 2024View details →

dryad36/100

Data from: Combining niche-shift and population genetic analyses predicts rapid phenotypic evolution during invasion

Rapid evolution of non-native species can facilitate invasion success, but recent reviews indicate that such microevolution rarely yields expansion of the climatic niche in the introduced habitats. However, because some invasions originate from a geographically restricted portion of the native species range and its climatic niche, it is possible that the frequency, direction and magnitude of phenotypic evolution during invasion has been underestimated. We explored the utility of niche-shift analyses in the red seaweed Gracilaria vermiculophylla, which expanded from the northeastern coastline of Japan to North America, Europe and northwestern Africa within the last 100 years. A genetically-informed climatic niche shift analysis indicates that native source populations occur in colder and highly seasonal habitats, while most non-native populations typically occur in warmer, less seasonal habitats. This climatic niche expansion predicts that non-native populations evolved greater tolerance for elevated heat conditions relative to native source populations. We assayed 935 field-collected and 325 common-garden thalli from 40 locations and as predicted, non-native populations had greater tolerance for ecologically-relevant extreme heat (40ºC) than did Japanese source populations. Non-native populations also had greater tolerance for cold and low-salinity stresses relative to source populations. The importance of local adaptation to warm temperatures during invasion was reinforced by evolution of parallel clines: populations from warmer, lower-latitude estuaries had greater heat tolerance than did populations from colder, higher-latitude estuaries in both Japan and eastern North America. We conclude that rapid evolution plays an important role in facilitating the invasion success of this and perhaps other non-native marine species. Genetically-informed ecological niche analyses readily generate clear predictions of phenotypic shifts during invasions, and may help to resolve debate over the frequency of niche conservatism versus rapid adaptation during invasion.

opencc-zeroDec 2016View details →

dryad36/100

Data from: Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing

Current climate change species response models usually do not include evolution. We integrated remote sensing with population genomics to improve phenotypic response prediction to drought stress in the key forest tree species European beech (Fagus sylvatica L.). We used whole-genome sequencing of pooled DNA from natural stands along an ecological gradient from humid-cold to warm-dry climate. We phenotyped stands for leaf area index (LAI) and moisture stress index (MSI) for the period 2016–2022. We predicted this data with matching meteorological data and a newly developed genomic population prediction score in a Generalised Linear Model. Model selection showed that the addition of genomic prediction decisively increased the explanatory power. We then predicted the response of beech to future climate change under evolutionary adaptation scenarios. A moderate climate change scenario would allow persistence of adapted beech forests, but not worst-case scenarios. Our approach can thus guide mitigation measures, such as allowing natural selection or proactive evolutionary management.

opencc-zeroAug 2023View details →

zenodo36/100

Harnessing genotype-phenotype nonlinearity to accelerate biological prediction

Description: We are sharing phenotypic data sets used in the pub <a href="http://doi.org/10.57844/arcadia-5953-995f">“Harnessing genotype-phenotype nonlinearity to accelerate biological prediction”</a>. Contained within are a set of publicly available empirical phenotype data sets, synthetic phenotypes we generated, and the output of an autoencoder model trained on them. Walkthroughs for all analyses using these data are available on <a href="http://doi.org/10.5281/zenodo.8371249">GitHub</a>. Files:  <ul> <li>“ail_cleaned_phenos.RDS”: .RDS file of mouse phenotypes from Bogue et al. 2015</li> <li>“all_autoencoder_phenotype_predictions.csv“: .csv file containing accuracy statistics for all phenotype-phenotype autoencoder models analyzed in the pub</li> <li>“arapheno_cleaned_phenos.RDS“: .RDS file of Arabidopsis phenotypes from Exposito-Alonso et al. 2019</li> <li>“autoencoder_phenos.zip“: directory of synthetic phenotype sets used to train the autoencoder models</li> <li>“dgrp_cleaned_phenos.RDS”: .RDS file of DGRP fruit fly phenotypes compiled from multiple sources, originally reported in Mackay et al. 2012</li> <li>“jax_cleaned_phenos.RDS”: .RDS file of mouse phenotypes from Gonzales et al. 2018</li> <li>“nematode_cleaned_phenos.RDS”: .RDS file of nematode phenotypes from Snoek et al. 2019</li> <li>“phen_pleio_int_01_0_1.pk”: pickle of all synthetic phenotypes analyzed in the pub</li> <li>“yeast_cleaned_phenos.RDS”: .RDS file of yeast phenotypes from Bloom et al. 2019</li> </ul> A full description of how the synthetic phenotypes and autoencoder predictions were generated is available in the associated <a href="http://doi.org/10.57844/arcadia-5953-995f">pub</a>.

opencc-by-4.0Aug 2023View details →

dryad36/100

Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data

Open the record for dataset details and reuse information.

publicNov 2023View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record