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6,234 results for “phenotype”
Arabidopsis thaliana images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Arabidopsis thaliana</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Oryza sativa images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Oryza sativa</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Solanum lycopersicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Solanum lycopersicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Ocimum basilicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Ocimum basilicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Phenotype data for Sussex LHM Drosophila melanogaster reproductive fitness GWAS
<p>Input data, code, logs, graphs and output data for the Sussex LHM Drosophila melanogaster hemiclones.</p> <p>Aim is to generate single, standardised values of female and male reproductive fitness for each hemiclone genome, for using in genome-wide association test using Plink software.</p> <p>Notes on how to run are provided in the code.</p>
Data from: Chronic Rapamycin administration via drinking water mitigates the pathological phenotype in a Krabbe disease mouse model through autophagy activation.
<p>ABSTRACT </p><p>Krabbe disease (KD) is a rare disorder caused by a deficiency of the lysosomal enzyme galactosylceramidase (GALC), resulting in the accumulation of the cytotoxic metabolite psychosine (PSY) in the nervous system. This accumulation triggers demyelination and neurodegeneration. Despite ongoing research, the underlying pathogenic mechanisms remain incompletely understood, and there is currently no cure available.</p><p>Previous studies from our lab revealed the presence of autophagy dysfunctions in KD pathogenesis, as evidenced by the presence of p62-tagged protein aggregates in the brains of KD mice and increased p62 levels in the KD sciatic nerve. We also demonstrated that the autophagy inducer Rapamycin (RAPA) can partially restore the wild-type (WT) phenotype in KD primary cells by reducing the number of p62 aggregates.</p><p>In this study, we tested RAPA in the Twitcher (TWI) mouse, a spontaneous KD mouse model. We administered the drug ad libitum via drinking water (15 mg/L) starting from post-natal day (PND) 21-23. We longitudinally monitored the motor performance of the mice through grip strength and rotarod tests, along with various biochemical parameters related to KD pathogenesis (i.e. autophagy markers expression, myelination, astrogliosis, and PSY accumulation).</p><p>Our findings demonstrate that RAPA significantly enhances motor functions at specific treatment time points and reduces astrogliosis in TWI brain, spinal cord, and sciatic nerves. Using western blot and immunohistochemistry, we observed a decrease in p62 aggregates in TWI nervous tissues, which corroborates our earlier in-vitro results. Furthermore, RAPA treatment partially reduces PSY levels in the spinal cord.</p><p>In conclusion, our results support the consideration of RAPA as a supportive therapy for KD. Importantly, as RAPA is already available in pharmaceutical formulations for clinical use, its potential for KD treatment can be promptly evaluated in clinical trials.</p>
Predicting Phenotypic Traits Using a Massive RNA-seq Dataset
<h2><strong>Abstract</strong></h2><p>The included datasets are a conglomerate of all available <i>Arabidopsis thaliana</i> 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 <i>Arabidopsis thaliana.</i></p><h3><strong>Note to Researchers</strong></h3><p>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<br> </p><h3><strong>Note on File Compression</strong></h3><p>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.</p><h3><strong>Description All Datasets:</strong></h3><p><strong>Title:</strong> Gene Expression Count Data of all <i>Arabidopsis thaliana</i> data available from NCBI SRA as of November 2022<br><strong>Abstract: </strong>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. <strong>NCBI_Nov2022_SRR_runinfo.csv</strong> is the starting file from NCBI which reports SRR information for each sample. <strong>Note 1 to Researchers: </strong>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. <strong>Note 2 to Researchers:</strong> 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. <br><strong>Author:</strong> John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Count Data<br><strong>Organism:</strong> <i>Arabidopsis thaliana</i><br><strong>Files:</strong></p><p><strong>NCBI_Nov2022_SRR_runinfo.csv - </strong>Arabidopsis RNA-seq SRA RunInfo Retrieved from NCBI November 2022. This is the unprocessed data.<br><strong>Dataset_54547_NoFilter_raw.pkl </strong>- Raw File Before thresholding (".pkl" format). Same as NoNo normalization without thresholding.<br><strong>Dataset_54547_NoFilter_raw.tsv </strong>- Raw File Before thresholding (".tsv" format). Same as NoNo normalization without thresholding.<br><strong>Dataset_32432_MRN.pkl</strong> - MRN normalized (".pkl" format)<br><strong>Dataset_32432_MRN.tsv - </strong>MRN normalized (".tsv" format)<br><strong>Dataset_32432_NoNo.pkl - </strong>NoNo normalized (".pkl" format)<br><strong>Dataset_32432_NoNo.tsv - </strong>NoNo normalized (".tsv" format)<br><strong>Dataset_32432_TMM.pkl - </strong>TMM normalized (".pkl" format)<br><strong>Dataset_32432_TMM.tsv - </strong>TMM normalized (".tsv" format)<br><strong>Dataset_32432_TPM.pkl - </strong>TPM normalized (".pkl" format)<br><strong>Dataset_32432_TPM.tsv - </strong>TPM normalized (".tsv" format)<br><br><br><strong>Title: </strong>Meta Data Arabidopsis Age and Tissue<br><strong>Abstract: </strong>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, <strong>NCBI_Nov2022_BioSample_data.tsv </strong>is the unprocessed starting material for these two data frames.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Metadata on phenotypes. ".tsv" format <br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files: </strong><br><strong>NCBI_Nov2022_BioSample_data.tsv - </strong>Arabidopsis BioSample data retrieved from NCBI November 2022. This is the unprocessed data.<br><strong>df_metadata_tissue.tsv</strong> - Tissue Annotations for 24876 samples<br><strong>df_metadata_age.tsv</strong><i><strong> - </strong></i>Age Annotations for 16078 samples. In addition to shared columns includes<i> "</i>days<i>_</i>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.)<br><br><br><strong>Title: </strong>Machine Learning Dataset for <i>Arabidopsis thaliana</i> <strong>Age</strong><br><strong>Abstract: </strong>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.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Matrix and Annotations Combined, split into train and test <br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Age_TrainTestSplits_mrn.pkl</strong> - MRN normalized<br><strong>Dataset_Age_TrainTestSplits_NoNo.pkl </strong>- NoNo normalized<br><strong>Dataset_Age_TrainTestSplits_tmm.pkl </strong>- TMM normalized<br><strong>Dataset_Age_TrainTestSplits_tpm.pkl - </strong>TPM normalized<br><br><br><strong>Title: </strong>Machine Learning Dataset for <i>Arabidopsis thaliana</i> <strong>Tissue</strong><br><strong>Abstract: </strong>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.<br><strong>Author:</strong> John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Matrix and Annotations Combined, split into train and test. Saved as python ".pkl" files.<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Tissue_TrainTestSplits_mrn.pkl </strong>- MRN normalized<br><strong>Dataset_Tissue_TrainTestSplits_NoNo.pkl </strong>- NoNo normalized<br><strong>Dataset_Tissue_TrainTestSplits_tmm.pkl </strong>- TMM normalized<br><strong>Dataset_Tissue_TrainTestSplits_tpm.pkl </strong>- TPM normalized<br><strong>Dataset_Tissue_TrainTestSplits_mrn_4category.pkl </strong>- MRN for the tissue-4 dataset<br><br><br><strong>Title: </strong>BioProject Names<br><strong>Abstract:</strong> Three Column File With BioProject Name, BioSample Name, and Experiment Name<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>BioProject_Names_All.tsv</strong><br><br><br><strong>Title:</strong> Manuscript Supplemental Material<br><strong>Abstract:</strong> Supplemental tables and figures described in the manuscript (included with manuscript and here for convenience). Please see manuscript for additional information.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv", ".png".pdf"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Supplemental_Figures.zip </strong>- Supplemental figures from the manuscript. Includes description of each figure.<br><strong>Supplemental_Tables.zip </strong>- Supplemental tables from the manuscript. Includes description of each table.<br><br> </p><p><strong>Title:</strong> Splits of data for 3 experiments<br><strong>Abstract:</strong> 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. <strong>Included here to make sure pkl files are reproducible in case the pkl package breaks in the future.</strong> Not used by scripts, included to prevent future potential loss of data.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Tissue_TrainTestSplits_4category_namesOnly.tsv</strong><br><strong>Dataset_Tissue_TrainTestSplits_namesOnly.tsv</strong><br><strong>Dataset_Age_TrainTestSplits_namesOnly.tsv</strong></p><p> </p><p><strong>Title:</strong> Git Code Repository<br><strong>Abstract:</strong> 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<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Git Repository, python code<br><strong>Files:</strong><br><strong>modeling-with-transcriptomics-main.tar.bz2</strong> - Compressed Git repository of all code used in paper.</p>
GWAS Summary Statistics for Publication: Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data
<p>This dataset contains summary statistics for genome-wide association studies (GWAS) conducted on genomic features derived from off-target reads in whole-exome sequencing (WES) data. The study utilized tools like Seeing Beyond the Target (SBT) and ImReP to construct novel phenotypic features from unmapped reads in ~50,000 participants in the UK Biobank. Features include mitochondrial DNA (mtDNA) copy number, ribosomal DNA (rDNA) copy number (5S, 18S, 28S), immune repertoire metrics (e.g., T-cell receptor alpha diversity), and microvial genome load (viral and fungal).</p> <p>Summary statistics can be used for replication studies, meta-analyses, or further exploration of these phenotypes.</p>
Complex basis of hybrid female sterility and Haldane's rule in Heliconius butterflies: Z-linkage and epistasis - RADseq and RNAseq reads, sterility phenotypes and pedigree
<p>RADseq and RNAseq reads (.fastq files), and sterility phenotypes and pedigree (.xlsx) using for QTL mapping of Heliconius pardalinus sterility crosses in Rosser, N., Edelman, N.B., Queste, L.M., Nelson, M., Seixas, F., Dasmahapatra, K.K. and Mallet, J., 2021. Complex basis of hybrid female sterility and Haldane’s rule in Heliconius butterflies: Z-linkage and epistasis, accepted for publication in Molecular Ecology. Queries to Neil Rosser (neil.rosser@york.ac.uk). </p> <p> </p>
Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system
<p>This dataset contains the raw and processed images from a low-cost high-throughput plant phenotyping (HTP) system, as well as the raw and processed images that were manually acquired for comparison. The HTP images were automatically and wirelessly acquired for entire benches of plants with a system composed of a Raspberry Pi and eight GoPro cameras. The entire file system of each GoPro camera was copied directly into a subfolder of finalGoProImages (numbered by camera). The raw HTP images were processed by correcting for lens distortion, computing the "greenness index" for each individual pixel, and filtering out extreme high and low values. These processed HTP images were then saved in the "greenness" subfolder of finalGoProImages. The manually acquired images in the finalDSLR folder each represent an individual plant from one of five time points during the same greenhouse experiment. The raw manually acquired images were processed in the same manner as the raw HTP images by computing the greenness index for each individual pixel and filtering out extreme high and low values. The two tab-delimited text files include the number of green pixels and mean greenness index for each HTP (greennessGoProTable2.txt) and manually acquired (greennessDSLRTable2.txt) image.</p>
Data for "Phenotypic responses to climate change are significantly dampened in big-brained birds"
<p>Anthropogenic climate change is rapidly altering local environments and threatening biodiversity throughout the world. Although many wildlife responses to this phenomenon appear largely idiosyncratic, a wealth of basic research on this topic is enabling the identification of general patterns across taxa. Here we expand those efforts by investigating how avian responses to climate change are affected by the ability to cope with ecological variation through behavioral flexibility (as measured by relative brain size). After accounting for the effects of phylogenetic uncertainty and interspecific variation in adaptive potential, we confirm that although climate warming is generally correlated with major body size reductions in North American migrants, these responses are significantly weaker in species with larger relative brain sizes. Our findings suggest that cognition can play an important role in organismal responses to global change by actively buffering individuals from the environmental effects of warming temperatures.</p>
Analysis scripts for the evaluation of a low-cost high-throughput plant phenotyping system
<p>Data analyses to complement "Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system" (DOI: 10.5281/zenodo.5725224). "README_SetupAndAnalyses.pdf" contains instructions for setting up the high-throughput phenotyping (HTP) system and analyzing the resulting image datasets. The analyses are split into two parts. First, the automatically acquired HTP and manually acquired (DSLR) images are processed using the Python script labeled "finalGreennessAnalyses.py". The csv file labeled "labelTable.csv" is used to rename the DSLR images in terms of the date acquired and experimental conditions and must be included for the Python script to process the DSLR images. The output of the Python script includes "greennessGoProTable.txt" containing tab-delimited data regarding foliar size and greenness for each HTP image and "greennessDSLRTable.txt" containing tab-delimited data regarding foliar size and greenness for each DSLR image. The second step of the analyses includes inferential statistics (e.g., correlations and linear mixed effects modeling) and is based on the R script labeled "ghGoProAndDSLR_toPublish2.R". The csv file labeled "parAllBenches.csv" includes average solar daily light integral (solar DLI) data that were used as part of the linear mixed effects models in R.</p>
Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover
<p>Red clover (<em>Trifolium pratense</em> L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses.</p> <p>A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by <em>Colletotrichum trifolii</em>, and clover rot caused by <em>Sclerotinia trifoliorum, </em>was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide, once validated, a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.</p>
Longitudinal characterization of circulating neutrophils uncovers distinct phenotypes associated with severity in hospitalized COVID-19 patients
<p>Code and data for the manuscript "Longitudinal characterization of circulating neutrophils uncovers distinct phenotypes associated with severity in hospitalized COVID-19 patients".</p> <p>Contains all code located at <a href="https://github.com/lasalletj/COVID_Neutrophils">https://github.com/lasalletj/COVID_Neutrophils</a> as well as additional data files needed to run the code.</p> <p>Three additional publicly available data objects are required to run the code from start to finish. The first, covid.combined_final.Robj, from the Sinha et al. Nature Medicine 2022 paper (<a href="https://doi.org/10.1038/s41591-021-01576-3">https://doi.org/10.1038/s41591-021-01576-3</a>), is downloadable from the following link: <a href="https://figshare.com/ndownloader/files/31562957">https://figshare.com/ndownloader/files/31562957</a>. The other two required objects, seurat_COVID19_Neutrophils_cohort2_rhapsody_jonas_FG_2020-08-18.rds and seurat_COVID19_freshWB-PBMC_cohort2_rhapsody_jonas_FG_2020-08-18.rds, are from the Schulte-Schrepping et al. Cell 2020 paper (<a href="https://doi.org/10.1016/j.cell.2020.08.001">https://doi.org/10.1016/j.cell.2020.08.001</a>), and can be downloaded from <a href="https://beta.fastgenomics.org/datasets/detail-dataset-ee4b1a0f339140ad82f861aea35076f1#Files">https://beta.fastgenomics.org/datasets/detail-dataset-ee4b1a0f339140ad82f861aea35076f1#Files</a> and <a href="https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files">https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files</a>, respectively.</p> <p>Any additional information required to reanalyze the data reported in this work paper is available from the Lead Contact, Moshe Sade-Feldman (msade-feldman@mgh.harvard.edu) upon request.</p>
Salmonella enterica serovar Derby isolated from eggs show genomic and phenotypic traits that may be linked to inability to produce human infection.
<p><em><span>Salmonella enterica</span></em><span> serovar Derby causes foodborne disease (FBD) outbreaks worldwide, mainly from contaminated pork but also from chickens. During a major epidemic of FBD in Uruguay due to <em>S</em>. Enteritidis from poultry, we conducted a large survey of commercially available eggs, where we isolated many <em>S.</em> Enteritidis strains but surprisingly also a much larger number (ratio 5:1) of <em>S</em>. Derby strains. No single case of <em>S</em>. Derby infection was detected in that period, suggesting that the <em>S</em>. Derby egg strains were impaired for human infection. We sequenced fourteen of these egg isolates, as well as fifteen isolates from pork or human infection that were isolated in Uruguay before and after that period, and all sequenced strains had the same sequence type <span>(ST40). Phylogenomic genomic analysis was conducted using more than 3500 genomes from the same sequence type (ST), revealing that Uruguayan isolates clustered into four distantly related lineages. Population structure analysis (BAPS) suggested the division of the analyzed genomes into nine different BAPS1 groups, with Uruguayan strains clustering within four of them. </span>All egg isolates clustered together as a monophyletic group and showed marked differences in gene content with the strains in the other clusters. <span>Differences included the absence of a C-terminal fragment of the <em>speF</em> gene, as well as variations in the composition of mobile genetic elements, such as plasmids, insertion sequences, transposons, and phages, between egg isolates and human/pork isolates.</span></span> <span>Egg isolates showed an acid susceptibility phenotype, reduced ability to reach the intestine after oral inoculation of mice, and reduced induction of SPI-2 <em>ssaG</em> gene, compared to human isolates from other monophyletic groups. Mice challenge experiments showed that mice infected intraperitoneally with human/pork isolates died between 1-7 days p.i., while all animals infected with the egg strain survived the challenge. Altogether, our results suggest that loss of gene functions and the absence of plasmids in egg isolates may explain why these <em>S</em>. Derby were not capable of producing human infection despite being at that time, the main serovar recovered from eggs countrywide.</span></p>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
Demo code for "ESPRESSO: Spatiotemporal omics based on organelle phenotyping": https://doi.org/10.1101/2024.06.13.598932
<p>The files and code provided are sufficient to run end-to-end the ESPRESSO analysis, from the microscope data to the feature analysis.<br>In the following, we provide guidance for the installation and running of the program, the output for each of the scripts are also stored in the <br>"ESPRESSO - Demo\Output of scripts" folder and can be consulted directly.</p>
Phenotypes of beef-on-dairy calves
<p><strong>Phenotypes from three different cattle F1 crossbreds obtained during three fattening trials. </strong></p> <p>calfID: individual calf ID</p> <p>dnaID: individual calf ID of genomic data</p> <p>Trial: fattening trial</p> <p>RatLot: treatment</p> <p>Breed: breed of sire, Angus (AAN), Limousin (LIM), Simmental (SIM); breed of dam is always Brown Swiss</p> <p>Sex: bull (B), heifer (H), steer (S)</p>
Gold standard corpus, ontologies, and Entity-Quality ontology annotations for evolutionary phenotypes
<p>This data set includes a gold-standard corpus of evolutionary phenotype descriptions (in the form of character state descriptions pulled from a variety of phylogenetic systematics studies), and their corresponding expert-curated annotations with ontology terms in the form of Entity-Quality (EQ) statements. EQ annotatons allow machine-reasoning (through the semantics encoded in the requisite ontologies from which the ontology terms are drawn), and machine-reasoning in turn enables computing metrics for quantifying the semantic similarity between different phenotype descriptions as represented by their EQ annotations.</p> <p>Also included are the ontologies, and the human expert-generated and Semantic Charaparser (i.e., machine) generated EQ annotations used to assess Semantic Charaparser performance relative to inter-curator variation and to the effect of having access to external knowledge. The ontologies include those used as input, the "augmented" ontologies created by human curators in each experiment round, and the merged ontology used to maximize Semantic Charaparser's performance.</p> <p>The production of the gold standard corpus, annotation experiments, and evaluation of the results are described in detail in the following manuscript:</p> <blockquote> <p>Dahdul et al (2018) Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems. BioRxiv https://doi.org/10.1101/322156. Submitted to Database.</p> </blockquote> <p>The analysis code for evaluating the gold standard corpus (and the input data and ontologies for that) are available separately from the following:</p> <blockquote> <p>Manda et al (2018) Code and data for analysis of evolutionary phenotype ontology annotations and gold standard corpus. Zenodo. https://doi.org/10.5281/zenodo.1218010</p> </blockquote> <p>In comparison to the previous version (v1.0.0), this record includes a file of MD5 checksums of the Gold Standard data files. The data files themselves are unchanged.</p>
Ontology based text mining of gene-phenotype associations: application to candidate gene prediction
<p>Gene-phenotype associations play an important role in understanding<br> the disease mechanisms which is a requirement for treatment<br> development. A portion of gene-phenotype associations are observed<br> mainly experimentally and made publicly available through several<br> standard resources such as MGI. However, there is still a vast<br> amount of gene--phenotype associations buried in the biomedical<br> literature. Given the large amount of literature data, we need<br> automated text mining tools to alleviate the burden in manual<br> curation of gene-phenotype associations and to develop<br> comprehensive resources. We developed an ontology based<br> approach in combination with statistical methods to text mine<br> gene-phenotype associations from literature. Our method achieved<br> AUC values of 0.90 and 0.75 in recovering known gene-phenotype<br> associations from HPO and MGI respectively. We posit that candidate<br> genes and their relevant diseases should be expressed with similar<br> phenotypes in publications. Thus, we demonstrate the utility of our<br> approach by predicting disease candidate genes based on the semantic<br> similarities of phenotypes associated with genes and diseases. We evaluated our disease candidate prediction model on<br> the gene-disease associations from MGI. Our model achieved AUC<br> values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of<br> gene-disease associations respectively. Our manual analysis on the<br> text mined data revealed that, our method can accurately extract<br> gene-phenotype associations which are not currently covered by the<br> existing public gene-phenotype resources. Overall, results indicate<br> that our method can precisely extract known as well as new<br> 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.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.