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2,785 results for “genotyping”
Data for: Akhund-Zade et al The effect of environmental enrichment on behavioral variability depends on genotype, behavior, and type of enrichment
<p>Data and analysis scripts for the manuscript entitled "The effect of environmental enrichment on behavioral variability depends on genotype, behavior, and type of enrichment" by Jamilla Akhund-Zade et al. </p>
Recla genotypes
<p>36-state genotypes for genomewide markers. Created with R/qtl2</p>
Congenic genotypes
<p> “Congenic genotypes” This table shows sequenom generated SNP genotypes across the genomes of SHR-A3 and the SHR-A3(<em>IgH</em>-B2) congenic lines. There are two columns of genotypes for two individual SHR-A3(<em>IgH</em>-B2) congenics. These are both progeny of the same founders. All their genotypes outside of the <em>IgH</em>locus are identical to SHR-A3. The list includes two genotypes targeting the proximal and distal ends of the <em>IgH</em>locus. These were typed by PCR and gel analysis. Both are SHR-B2 alleles.</p>
Creating a 7,000 strains genotype-phenotype dataset of E. coli and antimicrobial resistance phenotypes
<h1>Description</h1> <p>This Zenodo repository contains the data (except for the input fastq files available on SRA and intermediary files generated during the variant calling process) and code to recapitulate the study from <a href="https://doi.org/10.57844/arcadia-d2cf-ebe5">https://doi.org/10.57844/arcadia-d2cf-ebe5</a> and the associated GitHub repository, where the code, pipelines, and analysis are described in more detail.</p> <p> </p> <h1>Work summary</h1> <p>In this work, we established a framework for compiling large genotype-phenotype datasets and produced a large-scale dataset of more than 7,000 <em>E. coli</em> strains and antimicrobial resistance phenotypes.</p> <p>We leveraged the genetic information and antimicrobial resistance (AMR) phenotype data available for the bacterium <em>Escherichia coli</em> to construct our dataset and took advantage of the existing knowledge about genetic variations and AMR phenotypes to validate our approach and dataset. We performed variant calling and compiled a genotype-phenotype dataset for more than 7,000 <em>E. coli</em> strains. Briefly, variant calling consists of identifying all genetic variations and their associated genotypes in a population compared to a reference genome. This is performed by aligning sequencing reads for each strain of the population against a reference genome, then identifying polymorphic regions in the population, and finally characterizing variants and their genotypes at each of these polymorphic regions.</p> <p>We have generated a dataset that successfully revealed significant genetic diversity and identified 2.4 million variants. By focusing on non-silent variants within genes associated with AMR, we confirmed the dataset's accuracy. </p> <p>We hope this study is a foundational resource for conducting large-scale genotype-phenotype studies that will offer valuable insights for genetics investigations, informing the development of treatments and prevention strategies for AMR. This resource is invaluable for microbiologists and epidemiologists seeking to understand AMR mechanisms and improve genotype-phenotype predictions in pathogenic <em>E. coli</em> outbreaks. Additionally, it's of particular interest to geneticists and evolutionary biologists, providing a dataset to develop strategies for studying genetic interactions and broader applications in phenotype-phenotype predictions and phylogenetic research.</p> <h1>Data organization</h1> <p>Data are organized in the compressed folder. Briefly, they’re divided into two main folders.</p> <p>The first folder, <strong>dataset_generation</strong>, includes the code and information necessary to build the genotype dataset and perform the variant calling. It covers major steps like the generation of the reference pangenome used for variant calling, the variant calling pipeline applied to each of the 7,000 strains, the filtering of false positive variants, and the annotation of the variants. </p> <p>The second section, <strong>dataset_analysis</strong>, includes the code and information used to process and analyze the dataset and generate figures for the Pub (https://doi.org/10.57844/arcadia-d2cf-ebe5). It includes the preliminary analysis of AMR phenotypes within the population and the analysis of variants regarding known AMR phenotypes.</p> <p> </p> <h1>Files description</h1> <p>The following table provides a list and description of the different files and their locations.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Location</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>variant_calling_pipeline</td> <td>dataset_generation/scripts/</td> <td>Snakefile: performs variant calling from raw paired-end sequencing files and generate one vcf.gz file per sample</td> </tr> <tr> <td>snakemake_ECOR72_annotation</td> <td>Snakefile: performs Prokka annotation on inputs whole genome fastq files</td> </tr> <tr> <td>ECOR72_and_DP_threshold_analysis.Rmd</td> <td>R markdown: analyses the coverage of known present and absebt loci in the ECOR population</td> </tr> <tr> <td>average_coverage_41.csv</td> <td>dataset_generation/data/dp_threshold/</td> <td>Pangenome loci read coverage information for 40 ECOR strains</td> </tr> <tr> <td>average_coverage_last32.csv</td> <td>Pangenome loci read coverage information for 32 ECOR strains</td> </tr> <tr> <td>whole_pan_ecor_presence_absence.csv</td> <td>Reformated pangenome loci presence-absence in ECOR strains</td> </tr> <tr> <td>pangenome_genomes_SRA_GCA.csv</td> <td>Correspondance table between ECOR72 strains genome names and raw sequencing files SRA accession number</td> </tr> <tr> <td>index_loci_pangenome_good.txt</td> <td>List of indexed positions in the pangenome</td> </tr> <tr> <td>list_ecor_txtfiles.txt</td> <td>List of txt files (containing the DP information per nucleotide) to use - This corresponds to the files for each 72 ECOR strains</td> </tr> <tr> <td>ECOR72_SRA_and_assembly_accessions.csv</td> <td>dataset_generation/data/</td> <td>List of Genome accession number and the SRA accession number of the associated sequencing files for the 72 ECOR strains</td> </tr> <tr> <td>sample_list_SRA.csv</td> <td>List of SRA accession numbers of the E. coli strains used for variant calling</td> </tr> <tr> <td>gene_presence_absence.csv</td> <td>dataset_generation/results/pangenome_cds/</td> <td>Roary output of presence-absence of the pangenome cds loci in the ECOR72 strains</td> </tr> <tr> <td>genes.gff</td> <td>Annotation file of the pangenome cds sequences (Prokka output)</td> </tr> <tr> <td>pangenome_cds.fa</td> <td>Roary output cds_pangenome sequencing file</td> </tr> <tr> <td>summary_statistics.txt</td> <td>Roary statistics output of creations of the cds pangenome</td> </tr> <tr> <td>roary_output</td> <td>Roary output folder</td> </tr> <tr> <td>IGR_presence_absence.csv</td> <td>dataset_generation/results/pangenome_igr/</td> <td>Piggy output of presence-absence of the pangenome igr loci in the ECOR72 strains</td> </tr> <tr> <td>pangenome_igr.fasta</td> <td>Piggy output igr_pangenome sequencing file</td> </tr> <tr> <td>piggy_output</td> <td>Piggy output folder</td> </tr> <tr> <td>whole_pangenome.fasta</td> <td>dataset_generation/results/pangenome_whole/</td> <td>whole pangenome sequences</td> </tr> <tr> <td>annot_summary_filtered.html</td> <td>dataset_generation/results/vcf/</td> <td>Summary of snpEff annotations</td> </tr> <tr> <td>annotated_output.vcf.gz</td> <td>snpEff annotated vcf file</td> </tr> <tr> <td>annotated_output.vcf.gz.csi</td> <td>indexed annotated vcf file</td> </tr> <tr> <td>filtered_output.vcf.gz</td> <td>filtered vcf file (removed low coveraged and low quality variants)</td> </tr> <tr> <td>filtered_output.vcf.gz.csi</td> <td>indexed filtered vcf files</td> </tr> <tr> <td>output.non_silent.vcf.gz</td> <td>vcf file containing only the nonsilent variants in the pangenome cds loci</td> </tr> <tr> <td>merged_output_listN.vcg.gz</td> <td>intermediary vcf files of 1000 merged strains vcf - these intermediary merged files are numbered from 1 to 7</td> </tr> <tr> <td>merged_output_all.vcf.gz</td> <td>final vcf.gz files of all merged vcf files in this study</td> </tr> <tr> <td>List_N_merging.txt</td> <td>dataset_generation/data/vcf_merging/</td> <td>List of the 1000 vcf.gz files to be merged together. There are 7 lists, numbered from 1 to 7</td> </tr> <tr> <td>ecor72_array.txt</td> <td>dataset_generation/results/ecor72_DP/</td> <td>Consolidate DP information per nucleotide for each ECOR strain</td> </tr> <tr> <td>variants_pos.tsv</td> <td>dataset_analysis/data/variant_analysis/</td> <td>List of all the variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_freqs.txt</td> <td>Variant frequency informations</td> </tr> <tr> <td>variants_non_silent_pos.tsv</td> <td>List of all the non-silent variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_non_silent_freqs.txt</td> <td>Non-silent variant frequency informations</td> </tr> <tr> <td>cds_eggNog.tsv</td> <td>eggNog output file of the pangenome annotation</td> </tr> <tr> <td>COG_functional_categories.csv</td> <td>Correspondance between COG functional categories and higher-order annotation</td> </tr> <tr> <td>BVBRC_genome_May31.csv</td> <td>dataset_analysis/data/dataset_analysis</td> <td>List of E. coli with available genomes as reported in BCBRV database</td> </tr> <tr> <td>BVBRC_genome_amr_May31.csv</td> <td>E. coli antimicrobial resistance information available in BCBRV database</td> </tr> <tr> <td>antibiotic_class.csv</td> <td>Antibiotic name and Antibiotic class information</td> </tr> <tr> <td>resistance_output.non_silent.vcf.gz</td> <td>dataset_analysis/data/antimicrobial_resistance_analysis</td> <td>vcf.gz file of the loci expected to be associated with antimicrobial resistance</td> </tr> <tr> <td>antibiotic_resistance_freq.csv</td> <td>Frequency information for the non-silent variant in the selected antimicrobial genes</td> </tr> <tr> <td>SRA_to_genome_name.csv</td> <td>correspondence between strain SRA accession number and genome name (as reported in BVBRC)</td> </tr> <tr> <td>Dataset_metainfo_AMR_analysis.Rmd</td> <td>dataset_analysis/scripts</td> <td>R markdown: conducts the characterization of the population and analysis of the AMR phenotype distribution</td> </tr> <tr> <td>Variant_population_analysis.Rmd</td> <td>R markdown: conducts the analysis and investigation of identified variants in the population</td> </tr> <tr> <td>Antimicrobial_resistance_investigation.Rmd</td> <td>R markdown: conducts the antimicrobial resistance investigation </td> </tr> </tbody> </table> <p> </p> <p> </p>
GTEx: DICOM converted whole slide hematoxylin and eosin stained images from the Genotype-Tissue Expression (GTEx) Project
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=gtex" target="_blank" rel="noopener">GTEx</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> <p>The<a href="https://commonfund.nih.gov/GTEx"> Genotype-Tissue Expression (GTEx) Project</a> established a data resource and tissue bank to study the relationship between genetic variants and gene expression in multiple human tissues and across individuals. The project included contributions from numerous groups with diverse expertise in biospecimen collection and processing, pathology review, molecular analysis, and data management. The contributors are collectively called the GTEx Consortium.</p> <p>GTEx collected a total of 26,468 unique tissue samples from 50+ different tissue types, from 956 healthy postmortem donors. The standardized biospecimen collection and analysis practices applied during the study served to minimize preanalytical variability associated with specimen-related factors and their potential impact on analytic endpoints. Each GTEx tissue was divided into two tissue blocks, one for histology and one for molecular analysis; both tissue blocks were preserved in PAXgene Tissue Fixative (Qiagen) solution for 6 to 24 hours, followed by PAXgene Tissue Stabilizer (Qiagen) as specified in the project-specific<a href="https://biospecimens.cancer.gov/resources/sops/library.asp"> standard operating procedures</a>. Tissue blocks were processed and embedded in paraffin at the GTEx central repository at the Van Andel Institute (MI) and hematoxylin and eosin–stained slides were generated from all GTEx donors. Digitally scanned whole slide images of PAXgene-fixed/stabilized, paraffin-embedded tissue sections were created using Aperio Scanscope software (Leica Biosystems). The digital images were then reviewed and annotated by one of four board-certified pathologists assigned to the GTEx study. There are a total of 25,503 digital histology images in the GTEx collection.</p> <p>GTEx was supported by the NIH Common Fund (2010 – 2019). Additional resources include the<a href="https://gtexportal.org/home/biobank"> GTEx Biobank</a>, the<a href="https://gtexportal.org/home/"> GTEx Portal</a>, and the full dataset at<a href="https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v9.p2"> dbGaP</a> (accession number phs000424).</p> <p>Please refer to the listed GTEx publications below for more details [2-7]. </p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>gtex-idc_v19-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>gtex-idc_v19-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>gtex-idc_v19-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <div>Please acknowledge the GTEx Consortium in any published work that includes the images. A sample statement for the acknowledgment of the Genotype-Tissue Expression (GTEx) Project dataset(s) follows.</div> <p>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (<a href="http://commonfund.nih.gov/GTEx" target="_blank" rel="noopener">commonfund.nih.gov/GTEx</a>). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI/Leidos Biomedical Research, Inc. subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to the Broad Institute of MIT and Harvard. Biorepository operations were funded through a Leidos Biomedical Research, Inc. subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research, Inc. (HHSN261200800001E). The Brain Bank was supported with supplements to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941& MH101814), the University of Chicago (MH090951, MH090937, MH101825, & MH101820), the University of North Carolina - Chapel Hill (MH090936), North Carolina State University (MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University (MH101810), and to the University of Pennsylvania (MH101822).</p> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. <em>Radiographics</em> <strong>43,</strong> (2023).</p> <p>[2] Sobin, L., Barcus, M., Branton, P. A., Engel, K. B., Keen, J., Tabor, D., Ardlie, K. G., Greytak, S. R., Roche, N., Luke, B., Vaught, J., Guan, P. & Moore, H. M. Histologic and quality assessment of genotype-Tissue Expression (GTEx) research samples: A large postmortem tissue collection. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0467-OA">10.5858/arpa.2023-0467-OA</a></p> <p>[3] GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).</p> <p>[4] GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).</p> <p>[5] GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).</p> <p>[6] Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., Guan, P., Korzeniewski, G. E., Lockhart, N. C., Rabiner, C. A., Rao, A. K., Robinson, K. L., Roche, N. V., Sawyer, S. J., Segrè, A. V., Shive, C. E., Smith, A. M., Sobin, L. H., Undale, A. H., Valentino, K. M., Vaught, J., Young, T. R., Moore, H. M. & GTEx Consortium. A novel approach to high-quality postmortem tissue procurement: The GTEx project. Biopreserv. Biobank. 13, 311–319 (2015).</p> <p>[7] Branton, P. A., Sobin, L., Barcus, M., Engel, K. B., Greytak, S. R., Guan, P., Vaught, J. & Moore, H. M. Notable histologic findings in a ‘normal’ cohort: The National Institutes of Health Genotype-Tissue Expression (GTEx) project. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0468-OA">10.5858/arpa.2023-0468-OA</a></p>
Data for "Differential effects of Daphnia genotype composition on spatial environmental heterogeneity in experimental metacommunities"
Open the record for dataset details and reuse information.
Fig. 6 in Gene identification using RNA-seq in two sweetpotato genotypes and the use of mining to analyze carotenoid biosynthesis
Fig. 6. qRT-PCR analysis of the selected genes associated with carotenoid and terpenoid backbone biosynthesis pathways. Roots, stems and leaves were collected, and the total RNAs was extracted, respectively, pooled and used in qRT-PCR analysis of the genes FDPS, PSY, HMGCR, HMGCS1, CRTISO and HDR. Relative expression levels were calculated using the 2−ΔΔCt method, with actin as an internal control.
Fig. 4 in Gene identification using RNA-seq in two sweetpotato genotypes and the use of mining to analyze carotenoid biosynthesis
Fig. 4. Distribution and frequency of the EST-SSRs in the sweetpotato transcriptome. The y-axis shows the SSR motif, and the x-axis shows the number of the motif.
Fig. 2 in Gene identification using RNA-seq in two sweetpotato genotypes and the use of mining to analyze carotenoid biosynthesis
Fig. 2. KEGG classifications of the sweetpotato unigenes. The y-axis shows the KEGG metabolic pathway names, and the x-axis shows the number and proportion of genes associated with the pathways. The genes were divided into five branches according to involvement in the following KEGG pathways: Cellular Processes (A), Environmental Information Processing (B), Genetic Information Processing (C), Metabolism (D), and Organismal Systems (E).
Fig. 1. Sweetpotato cultivars Zhengshu 20 and Luoxushu 8 in Gene identification using RNA-seq in two sweetpotato genotypes and the use of mining to analyze carotenoid biosynthesis
Fig. 1. Sweetpotato cultivars Zhengshu 20 and Luoxushu 8. The tuberous roots were transected, and the carotene content of Zhengshu 20 and Luoxushu 8 is shown.
Fig. 5 in Gene identification using RNA-seq in two sweetpotato genotypes and the use of mining to analyze carotenoid biosynthesis
Fig. 5. KEGG pathway enrichment scatter plot of the up-regulated genes between Zhengshu 20 and Luoxushu 8. The y-axis shows the pathway name, and the x-axis shows the richness factor of the pathway. The colors indicate the q-values; the smaller the q-value, the redder the color and the sizes of the point represent differences in the expression of the gene number in each pathway.
Fig. 3 in Gene identification using RNA-seq in two sweetpotato genotypes and the use of mining to analyze carotenoid biosynthesis
Fig. 3. Gene ontology (GO) classification of the assembled in vitro tissue transcripts. The results of the following three main GO categories are summarized: biological process, cellular component, and molecular function. The y-axis shows the total number of genes in each category.
Data from: Independent and interactive effects of plant genotype and environment on plant traits and insect herbivore performance: a meta-analysis with Salicaceae
1. Ecological research has increasingly highlighted the importance of intraspecific variation in shaping the structure and function of communities and ecosystems. Indeed, the effects of intraspecific variation can match or exceed those of interspecific variation. Previous reviews of intraspecific variation in plant traits across heterogeneous environments have focused primarily on mean phenotypic effects. We propose that a richer and fuller understanding of the ecological causes and consequences of intraspecific variation would be provided by partitioning trait variance into its subcomponents (genetic, environment, genotype by environment interaction). 2. We used a meta-analysis of 352 sets of genetic, environment, and genotype by environment (GxE) variation estimates from 72 studies of Salicaceae to compare these sources of variation across plant traits (growth, foliar nitrogen, defense compounds), insect herbivore performance metrics (e.g., survival, growth, fecundity), and environmental conditions (e.g., soil nutrients, water, defoliation). 3. Our findings revealed that variation in levels of defense compounds (both condensed tannins and salicinoids) and insect herbivore performance were primarily genetically determined, while variation in plant growth and foliar nitrogen were more environmentally determined. 4. Plasticity in plant growth, foliar nitrogen levels, and insect herbivore performance varied substantially across different sites (year x location), and nutrient, water, and carbon dioxide environments. Plasticity was lowest for chemical defense traits and all traits in contrasting ozone and defoliation environments. 5. Our quantitative review also revealed several gaps in the literature, including a need for surveying more mature plants (>2 years-old), a wider variety of insect herbivore species (e.g., leaf-modifiers, specialist insects), and underrepresented environmental treatments (e.g., competition, defoliation, disease, light, water). This work will help to assess how the patterns within this meta-analysis may or may not be confined within particular parameters (e.g., plant maturity). 6. Findings from this analysis further highlight the importance of and patterns within intraspecific variation in shaping the evolvability and plasticity of traits and in governing plant-insect interactions.
Data from: Fine-scale temporal analysis of genotype-dependent mortality at settlement in the Pacific oyster Crassostrea gigas
Settlement and metamorphosis mark a critical transition in the life cycle of marine invertebrates, during which substantial mortality occurs in both field and laboratory settings. Previous pair-crossing experiments with the Pacific oyster Crassostrea gigas have revealed significant selective or genotype-dependent mortality around the metamorphic transition, but the fine-scale nature and timing of this mortality is not known, particularly whether it occurs before, during or after metamorphosis. In this laboratory study, microsatellite marker segregation ratios were followed daily throughout the settlement and metamorphosis of an F2 cross of the Pacific oyster to examine the fine-scale patterns of genotype dependent mortality at this transition and whether settlement timing (early vs. late) might be under genetic control and affect inference of genotype dependent mortality. Settlement occurred over nine days (day 18 to day 27 post-fertilization) with 68% of individuals settling either early (day 19) or late (day 24). Tracking the survival of spat for 40 days after initial settlement revealed almost no mortality and thus no appreciable genetic mortality. Temporal genetic analysis revealed that 3/11 loci exhibited genotype dependent mortality around the metamorphic transition, one of which (Cg205) was followed throughout settlement and metamorphosis. Alternative temporal patterns of strong selection against each homozygous genotype at Cg205 revealed possible defects in both the competency pathway (inability to initiate metamorphosis) and the morphogenesis pathway (mortality during the metamorphic transition). Quantitative trait locus (QTL) mapping of settlement timing identified three individual and one epistatic QTL with significant genetic effects on this trait (29% of the variance explained in total); however, two of these loci were linked to markers exhibiting selective mortality at metamorphosis, potentially confounding their apparent association with settlement timing. Overall, the results of this study highlight the complex nature of mortality and behavior during settlement and metamorphosis in oysters and suggest that endogenous sources of mortality at settlement may play an important role in the recruitment dynamics of oysters and possibly other broadcast spawning marine invertebrates.
SNP genotyping of Lord Howe woodhen (Hypotaenidia sylvestris) from museum skins and contemporary blood samples
<p>These data are from a conservation genetics project investigating population structure, dispersal and genetic bottlenecks in the Lord Howe woodhen <i>Hypotaenidia sylvestris.</i> This species recovered from near extinction in the 1970s to approximately 250 individuals in 2017. We used single nucleotide polymorphisms (SNPs) to genotype samples of both the contemporary population and 100-year-old museum specimens. We discovered strong population structuring between mountain and lowland "subpopulations" in both the contemporary and historic populations. This is indicative of restricted dispersal at fine spatial scales associated with rugged topography. There was also a decline in genetic diversity over the past century. We recommend ongoing genetic monitoring and translocations to increase genetic diversity within the re-established lowland subpopulation which although numerically stronger is genetically depleted relative to the mountain subpopulation.</p>
Data from: A set of plastid loci for use in multiplex fragment length genotyping for intraspecific variation in Pinus (Pinaceae)
Premise of the study: Recently released Pinus plastome sequences support characterization of 15 plastid Simple Sequence Repeat (ptSSR) loci originally published for P. contorta and P. thunbergii. This allows selection of loci for single-tube PCR multiplexed genotyping in any subsection of the genus. Methods: Unique placement of primers and primer conservation across the genus were investigated, and a set of six loci were selected for single-tube multiplexing. We compare interspecific variation between ptSSRs and nucleotide sequences of ycf1 then test intraspecific variation for ptSSRs using 911 samples in the P. ponderosa species complex. Results: The ptSSR loci contain mononucleotide and complex repeats with additional length variation in flanking regions. They are not located in hypervariable regions and most primers are conserved across the genus. A single PCR per sample multiplexed for six loci yielded 45 alleles in 911 samples. Discussion: The protocol allows efficient genotyping of many samples. The ptSSR loci are too variable for Pinus phylogenies but are useful for the study of genetic structure within and among populations. The multiplex method could easily be extended to other plant groups by choosing primers for ptSSR loci in a plastome alignment for the target group.
Data from: Skin swabbing of amphibian larvae yields sufficient DNA for efficient sequencing and reliable microsatellite genotyping
Skin swabbing, a minimally invasive DNA sampling method recently developed on adult amphibians, was tested on larvae of fire salamanders (Salamandra salamandra). The quality and quantity of the sampled DNA was evaluated by (i) measuring DNA concentration in DNA extracts, (ii) sequencing part of the mtDNA cytochrome b gene (692 bp) and (iii) genotyping eight polymorphic nuclear microsatellite loci. The multiple-tubes approach was used for calculating allelic dropout (ADO) and false allele (FA) rates to evaluate the reliability of the genotypes. DNA extracts from tissue samples of road-killed individuals were included in the study as positive controls. Our results showed that skin swabs of fire salamander larvae can provide DNA in sufficient quantity and quality, as sequencing was successful and no allelic dropouts or false alleles were detected. This method, tested for the first time on amphibian larvae, has proven to be an efficient and reliable alternative to the controversial tail fin clipping procedure.
Data from: Genotyping-in-Thousands by sequencing (GT-seq) panel development and application to minimally-invasive DNA samples to support studies in molecular ecology
Minimally-invasive sampling (MIS) is widespread in wildlife studies; however, its utility for massively parallel DNA sequencing (MPS) is limited. Poor sample quality and contamination by exogenous DNA can make MIS challenging to use with modern genotyping-by-sequencing approaches, which have been traditionally developed for high-quality DNA sources. Given that MIS is often more appropriate in many contexts, there is a need to make such samples practical for harnessing MPS. Here, we test the ability for Genotyping-in-Thousands by sequencing (GT-seq), a multiplex amplicon sequencing approach, to effectively genotype minimally-invasive cloacal DNA samples collected from the Western Rattlesnake (Crotalus oreganus), a threatened species in British Columbia, Canada. As there was no previous genetic information for this species, an optimized panel of 362 SNPs was selected for use with GT-seq from a de novo restriction-site associated DNA sequencing (RADseq) assembly. Comparisons of genotypes generated within and among RADseq and GT-seq for the same individuals found low rates of genotyping error (GT-seq: 0.50%; RADseq: 0.80%) and discordance (2.57%), the latter likely due to the different genotype calling models employed. GT-seq mean genotype discordance between blood and cloacal swab samples collected from the same individuals was also minimal (1.37%). Estimates of population diversity parameters were similar across GT-seq and RADseq datasets, as were inferred patterns of population structure. Overall, GT-seq can be effectively applied to low quality DNA samples, minimizing the inefficiencies presented by exogenous DNA typically found in minimally-invasive samples and continuing the expansion of molecular ecology and conservation genetics in the genomics era.
Microsatellite genotypes, cluster membership and metadata of Central European wolves (Canis lupus)
<p class="Normalny1">Local extinction and recolonization events can shape genetic structure of subdivided animal populations. The gray wolf (<i>Canis lupus</i>) was extirpated from most of Europe, but recently recolonized big part of its historical range. An exceptionally dynamic expansion of wolf population is observed in the western part of the Great European Plain. Nonetheless, genetic consequences of this process have not yet been fully understood. We aimed to assess genetic diversity of this recently established wolf population in Western Poland (WPL), determine its origin and provide novel data regarding the population genetic structure of the grey wolf in Central Europe. We utilized both spatially explicit and non-explicit Bayesian clustering approaches, as well as a model-independent, multivariate method DAPC, to infer genetic structure in large dataset of wolf microsatellite genotypes. To put the patterns observed in studied population into a broader biogeographic context we also analyzed a mtDNA control region fragment widely used in previous studies.</p> <p>In comparison to a source population, we found slightly reduced allelic richness and heterozygosity in the newly recolonized areas west of the Vistula river. We discovered relatively strong west-east structuring in lowland wolves, probably reflecting founder-flush and allele surfing during range expansion, resulting in clear distinction of WPL, eastern lowland and Carpathian genetic groups. Interestingly, wolves from recently recolonized mountainous areas (Sudetes Mts, SW Poland) clustered together with lowland, but not Carpathian wolf populations. We also identified an area in Central Poland that seems to be a melting pot of western, lowland eastern and Carpathian wolves. We conclude that the process of dynamic recolonization of Central European lowlands lead to the formation of a new, genetically distinct wolf population. Together with the settlement and establishment of packs in mountains by lowland wolves and vice versa, it suggests that demographic dynamics and possibly anthropogenic barriers rather than ecological factors (e.g. natal habitat-biased dispersal patterns) shape the current wolf gene<span>tic structure in Central Europe.</span></p>
Data from: Persistence of resident and transplanted genotypes of the undomesticated yeast, Saccharomyces paradoxus in forest soil
One might expect yeasts in soil to be highly dispersed via water or insects, forming ephemeral, genetically heterogeneous populations subject to competition and environmental stochasticity. Here, we report persistence of genotypes of the yeast Saccharomyces paradoxus in space and time. Within 1 km2 in a mixed hardwood forest on scales from centimeters to tens of meters, we detect persistence over 3 years of native genotypes, identified by SNPs genome-wide, of the wild yeast, Saccharomyces paradoxus around Quercus rubra and Q. alba. Yeasts were recovered by enrichment in ethanol-containing medium, which measures only presence or absence, not abundance. Additional transplantation experiments employed strains marked with spontaneous defects in the URA3 gene, which also confer resistance to 5-Fluoroorotic acid (5FOA). Plating soil suspensions from transplant sites on 5FOA medium permitted one-step quantification of yeast colony-forming units, with no interference from other unmarked yeasts or microorganisms. After an initial steep decrease in abundance, the yeast densities fluctuated over time, increasing in association with rainfall and decreasing in association with drought. After 18 months, the transplanted yeasts remain in place on the nine sites. In vitro transplantation experiments into non-sterile soil in petri dishes showed similar patterns of persistence and response to moisture and drought. To determine whether S. cerevisiae, not previously recovered from soils regionally, can persist in our cold-climate sites, we transplanted marked S. cerevisiae alone and in mixture with S. paradoxus in fall, 2017. Five months on, S. cerevisiae persist to the same extent as S. paradoxus.
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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.