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Fig. 3. Phylogenetic relationships among Euphorbia flavicoma, E. montenegrina, E. serpentini and E in High genetic and morphological diversification of the Euphorbia verrucosa alliance (Euphorbiaceae) in the Balkan and Iberian peninsulas
Fig. 3. Phylogenetic relationships among Euphorbia flavicoma, E. montenegrina, E. serpentini and E. verrucosa inferred by neighbour-joining analysis based on AFLP fingerprinting and presented in detail in supplementary Fig. S3. Terminals belonging to the same taxa/ploidy levels were collapsed; the heights of the triangles correspond to the number of individuals analysed. Numbers above branches are bootstrap values, and 2x and 4x indicate diploid and tetraploid ploidy levels, respectively. Within E. montenegrina, both ploidy levels appeared in several clusters.
Fig. 8 in High genetic and morphological diversification of the Euphorbia verrucosa alliance (Euphorbiaceae) in the Balkan and Iberian peninsulas
Fig. 8. Morphological differentiation revealed by discriminant analysis (DA) of 39 metric vegetative and cyathium characters and 16 ratios among: A, Euphorbia flavicoma (squares), E. montenegrina (triangles), E. serpentini (stars) and E. verrucosa (circles); B, E. montenegrina (triangles), E. serpentini (stars) and E. verrucosa (circles).
Merging metabolomics and genomics provides a catalog of genetic factors that infuence molecular phenotypes in pigs linking relevant metabolic pathways
<h3>Content</h3> <p>Metabolites included in the study. Summary statistics of metabolite levels for the Large White and Duroc pig populations are provided.</p>
Supplementary table for "Deciphering the Relationship Between Circulating Metabolites and Osteoarthritis: A Comprehensive Genetic Correlation and Mendelian Randomization Studies"
Open the record for dataset details and reuse information.
Data from: Genetic and functional variation across regional and local scales is associated with climate in a foundational prairie grass
<ul> <li>Global change forecasts in ecosystems require knowledge of within species diversity, particularly of dominant species within communities. We assessed site-level diversity and capacity for adaptation of the dominant species of the shortgrass steppe biome of the Central US, Bouteloua gracilis.</li> <li>We quantified genetic diversity from 17 sites across regional scales, north-south from New Mexico to South Dakota, and local scales in Northern Colorado. We also quantified phenotype and plasticity within and among sites and determined the extent to which phenotypic diversity in B. gracilis was related to climate.</li> <li>Genome sequencing indicated pronounced population structure at the regional scale, and local differences indicated gene flow and/or dispersal may also be limited. Within a common environment, we found evidence for genetic divergence in biomass-related phenotypes, plasticity, and phenotypic variance, indicating functional divergence and different adaptive potential. Phenotypes differentiated according to climate, chiefly median Palmer Hydrological Drought Index and other aridity metrics.</li> <li>Our results indicate conclusive differences in genetic variation, phenotype, and plasticity in this species and suggest a mechanism explaining variation in shortgrass steppe community responses to global change. This analysis of B. gracilis intraspecific diversity across spatial scales will improve conservation and management of the shortgrass steppe ecosystem moving forward.</li> </ul>
Data from: Dispersal in a house sparrow metapopulation: an integrative case study of genetic assignment calibrated with ecological data and pedigree information
<p class="western">Dispersal has a crucial role determining eco-evolutionary dynamics through both gene flow and population size regulation. However, to study dispersal and its consequences, one must distinguish immigrants from residents. Dispersers can be identified using telemetry, capture-mark-recapture (CMR) methods, or genetic assignment methods. All of these methods have disadvantages, such as, high costs and substantial field efforts needed for telemetry and CMR surveys, and adequate genetic distance required in genetic assignment. In this study, we used genome-wide 200K Single Nucleotide Polymorphism data and two different genetic assignment approaches (GSI_SIM, Bayesian framework; BONE, network-based estimation) to identify the dispersers in a house sparrow (<i>Passer domesticus</i>) metapopulation sampled over 16 years. Our results showed higher assignment accuracy with BONE. Hence, we proceeded to diagnose potential sources of errors in the assignment results from the BONE method due to variation in levels of inter-population genetic differentiation, intra-population genetic variation and sample size. We show that assignment accuracy is high even at low levels of genetic differentiation and that it increases with the proportion of a population that has been sampled. Finally, we highlight that dispersal studies integrating both ecological and genetic data provide robust assessments of the dispersal patterns in natural populations.</p>
Bauder Et Al. - Landscape features fail to explain spatial genetic structure
<p> RMarkdown script and data in an Excel sheet to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We used genetic data from 619 individual deer from 24 counties across Ohio tested at 11 microsatellites and haplotypes from a 547-bp fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data.</p> <p>v2 (BauderEtAl_Files_for_archiving2.zip) includes additional and updated files not in v1. </p>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data2
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and houses' data</li> <li>Houses_Input_Output_JSONs_and_Statistics - Zip containing the input and output files from the proposed system, as well as their corresponding schedule statistics</li> </ul>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and other house data</li> <li>Houses_Input_JSONs - Zip containing the input files, from each house, for the proposed system</li> </ul>
Population genetics characteristics of a 90 locus panel of microhaplotypes
<p>Microhaplotype genotype data for 556 individuals (with anonymized identifiers) from 16 population samples.</p> <p> </p>
Supplementary data for: Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression
<p><strong>Supplementary Note 1:</strong></p> <ul> <li>S1 Text: Oncogenomic comparisons between SB candidate Trunk driver genes and their direct orthologs in human Cancer Gene Census; Pyrosequencing analysis of SB-driven keratinocyte cancer models; References.</li> </ul> <p><strong>Supplementary Figures 1-11:</strong></p> <ul> <li>S1 Fig: Overview of genetic crosses to generate SB|Trp53|Onc3 mouse model.</li> <li>S2 Fig: SB insertion patterns in activated and inactivated drivers.</li> <li>S3 Fig: Evaluating the reproducibility of SBCapSeq results from bulk cuSCC and normal skin specimens.</li> <li>S4 Fig. Hierarchical two-dimensional clustering of recurrent events in cuKA and cuSCC.</li> <li>S5 Fig. Curated biological pathways and processes enriched within SB-induced cuSCC.</li> <li>S7 Fig: ZMIZ1 metagene within the TCGA Head & Neck Squamous Cell Carcinoma (hnSCC) RNA-seq dataset.</li> <li>S8 Fig: Clonally selected SB insertions affect trunk driver proto-oncogene expression in SB-cuSCC genomes.</li> <li>S9 Fig: Clonally selected SB insertions affect trunk driver genes by inactivating expression in SB-cuSCC genomes.</li> <li>S10 Fig: CREBBP knockdown does not alter proliferation rate in cuSCC cell lines.</li> <li>S11 Fig: Gross photographs of cuSCC xenograft masses collected at necropsy showing robust TurboGFP expression.</li> <li>S12 Fig: SB T2/Onc3 TG.12740 allele donor position mapping and exclusion for SB Driver Analysis.</li> </ul> <p><strong>Supplementary Tables 1-20:</strong></p> <ul> <li>S1 Table: Tumor incidence and subgroup classifications by cohort.</li> <li>S2 Table: Specimen metafile data for projects sequenced using SBCapSeq protocol with Ion Torrent Proton sequencer.</li> <li>S3 Table: Discovery and progression SB Driver Analysis for cuSCC60_SBC.</li> <li>S4 Table: Trunk SB Driver Analysis for cuSCC60_SBC.</li> <li>S5 Table: Discovery and progression SB Driver Analysis for cuKA11_SBC.</li> <li>S6 Table: Trunk SB Driver Analysis for cuKA11_SBC.</li> <li>S7 Table: Discovery and progression SB Driver Analysis for cuSK32_SBC.</li> <li>S8 Table: SBCapSeq read depth and analysis for 4 cuSCC genomes selected for multi-region resequencing because they had intermixing of cuSCC and cuKA histologies.</li> <li>S9 Table: Enrichr gene set pathway enrichment analysis of cuSCC drivers.</li> <li>S10 Table: Summary of 7 cuSCC transcriptomes selected for whole transcriptome RNAseq analysis.</li> <li>S11 Table: BED file of SBfusion insertions in 7 cuSCC genomes by whole transcriptome RNAseq analysis.</li> <li>S12 Table: Venn diagram for overlap of genes with SBfusion reads detected by whole transcriptome RNAseq analysis and cuSCC60_SBC discovery driver.</li> <li>S13 Table: Venn diagram for overlap of genes with SBfusion reads detected by whole transcriptome RNAseq analysis and all cuSCC drivers.</li> <li>S14 Table: Transcripts per million (TPM) normalized whole transcriptome RNAseq values per gene from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S15 Table: Fragments Per Kilobase of Transcripts per Million (FPKM) normalized whole transcriptome RNAseq values per gene transcript from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S16 Table: Normalized microarray values per gene from RNA isolated from cuSCC genomes with and without <em>Zmiz1</em> insertions.</li> <li>S17 Table: Normalized microarray values per probe from RNA isolated from cuSCC genomes with and without <em>Zmiz1</em> insertions.</li> <li>S18 Table: All 289 genes with differential expression analysis from microarray data from RNA isolated from cuSCC genomes with and without Zmiz1 insertions with P<0.0001 and q<0.05.</li> <li>S19 Table: Lentiviral vectors containing shRNAs used in this study.</li> <li>S20 Table: TaqMan probes used in this study.</li> </ul> <p><strong>Supplementary Datasets 1-5:</strong></p> <ul> <li>S1 Data: BED file of SB insertions for cuSCC60_SBC.</li> <li>S2 Data: BED file of SB insertions for cuKA11_SBC.</li> <li>S3 Data: BED file of SB insertions for cuSK32_SBC</li> <li>S4 Data: BED file of SB insertions for 4 cuSCC genomes selected for multi-region resequencing because they had intermixing of cuSCC and cuKA histologies.</li> <li>S5 Data: Numerical data for graphs pertaining to Figure Panels Fig1A; Fig5A–E; Fig6A–B,D; Fig7C–G; Fig8A–B,D–F; Fig9A–I in the paper on the publicly availble <em>PLOS Genetics</em> Web site.</li> </ul>
Data for: The genetic basis of floral mechanical isolation between two hummingbird-pollinated Neotropical understory herbs
<p>Floral divergence can contribute to reproductive isolation among plant lineages, and thus provides an opportunity to study the genetics of speciation, including the number, effect size, mode of action, and interactions of quantitative trait loci (QTL). Moreover, flowers represent suites of functionally interrelated traits, but it is unclear to what extent the phenotypic integration of the flower is underlain by a shared genetic architecture, which could facilitate or constrain correlated evolution of floral traits. Here, we examine the genetic architecture of floral morphological traits involved in an evolutionary switch from bill to forehead pollen placement between two species of hummingbird-pollinated Neotropical understory herbs that are reproductively isolated by these floral differences. For the majority of traits, we find multiple QTL of relatively small effect spread throughout the genome. We also find substantial colocalization and alignment of effects of QTL underlying different floral traits that function together to promote outcrossing and reduce heterospecific pollen transfer. Our results are consistent with adaptive pleiotropy or linkage of many coadapted genes, either of which could have facilitated a response to correlated selection and helped to stabilize divergent phenotypes in the face of low levels of hybridization. Moreover, our results indicate that floral mechanical isolation can be consistent with an infinitesimal model of adaptation.</p>
Figure 1 in Chromosomal and genetic characterization of four Caribbean Prioninae (Coleoptera: Cerambycidae) species with notes on biogeography
Figure 1. Schematic representation of the Lesser Antilles and photography of the insects under study.
Figure 10 in Chromosomal and genetic characterization of four Caribbean Prioninae (Coleoptera: Cerambycidae) species with notes on biogeography
Figure 10. Phylogenetic tree resulting from the Bayesian analysis, clustering the different haplotypes of Prioninae retrieved from the Genbank with those assessed of this study as compressed subtree (red triangle). Expansion of the subtree and phylogenetic relationships between the different haplotypes is also shown. The topology of the clusters was similar for the UPGMA and NJ trees. Numbers above branches of the major clusters correspond to posterior probabilities from the Bayesian analysis. TdeH and TdeB: Terre-de-Haut and Terre-de-Bas islands from Les Saintes.
Figures 6-9 in Chromosomal and genetic characterization of four Caribbean Prioninae (Coleoptera: Cerambycidae) species with notes on biogeography
Figures 6-9. Karyotypes of Solenoptera and Hovorodon species. 6) Giemsa stained karyotype of S. touroulti exhibiting three pairs (# 1, 2 and 4) of sub-metacentric autosomes. 7) Karyotype of a spermatocyte I at pachynema of S. touroulti, after Giemsa (left) and silver (right) stainings. Notice the lack of synapsis at the NOR locus (arrow) and the silver staining of nucleoli. 8) Giemsa stained karyotype of H. maxillosum exhibiting 10 pairs of sub-metacentric autosomes. 9) Karyotype of a spermatocyte I at pachynema of H. maxillosum, after Giemsa (left) and silver (right) stainings. Arrow: NOR on bivalent 12. Bars = 10μm.
Figures 2-5 in Chromosomal and genetic characterization of four Caribbean Prioninae (Coleoptera: Cerambycidae) species with notes on biogeography
Figures 2-5. Karyotypes of Solenoptera species. 2) Giemsa stained male karyotype of S. canaliculata. The arrows on chromosome 4 indicate an elongated region, which is the only region stained by C-banding. Bar = 10 μm, as in other figures. 3) Karyotype of a spermatocyte I at pachynema of S. canaliculata, after Giemsa (left) and silver (right) stainings. The NOR on bivalent 4 looks elongated, and is surrounded by nucleoli after silver staining. 4) Giemsa stained XYY karyotype of S. quadrilineata. Chromosome 4 is elongated at the same position as in S. canaliculata (arrow), which indicates the position of the NOR. 5) Giemsa stained metaphase I of the XYY male of S. quadrilineata exhibiting a parachute sex trivalent. Arrows point out the two Ys. The accidental presence of two Ys in male beetles was estimated to about 1 per cent (Dutrillaux and Dutrillaux 2011). Bars = 10μm.
Phenotype, genotype and fitness data related to genetic analysis of praziquantel response in schistosome parasites.
<p>These data are related to the study of the Genetic analysis of praziquantel response in schistosome parasites implicates a Transient Receptor Potential channel.</p> <p>Mass treatment with praziquantel (PZQ) monotherapy is the mainstay for schistosomiasis treatment. This drug shows imperfect cure rates in the field and parasites showing reduced PZQ response can be selected in the laboratory, but the extent of resistance in <em>Schistosoma mansoni</em> populations is unknown. We examined the genetic basis of variation in PZQ response in a <em>S. mansoni</em> population (SmLE-PZQ-R) selected with PZQ in the laboratory: 35% of these worms survive high dose (73 µg/mL) PZQ treatment. We used genome wide association to map loci underlying PZQ response. The major chr. 3 peak contains a transient receptor potential (Sm.TRPM_PZQ) channel (Smp_246790), activated by nanomolar concentrations of PZQ. PZQ response shows recessive inheritance and marker-assisted selection of parasites at a single Sm.TRPM_PZQ SNP enriched populations of PZQ-resistant (PZQ-ER) and sensitive (PZQ-ES) parasites showing >377 fold difference in PZQ response. The PZQ-ER parasites survived treatment in rodents better than PZQ-ES. Resistant parasites show 2.25-fold lower expression of Sm.TRPM_PZQ than sensitive parasites. Specific chemical blockers of Sm.TRPM_PZQ enhanced PZQ resistance, while Sm.TRPM_PZQ activators increased sensitivity. A single SNP in Sm.TRPM_PZQ differentiated PZQ-ER and PZQ-ES lines, but mutagenesis showed this was not involved in PZQ response, suggesting linked regulatory changes. We surveyed Sm.TRPM_PZQ sequence variation in 259 parasites from the New and Old World revealing one nonsense mutation that results in a truncated protein with no PZQ-binding site. Our results demonstrate that Sm.TRPM_PZQ underlies variation in PZQ response in <em>S. mansoni</em> and provides an approach for monitoring emerging PZQ-resistance alleles in schistosome elimination programs.</p> <p>This dataset is divided in 3 folders. Each folder has a readme detailing its content.</p> <p><strong>1-Phenotyping_data</strong></p> <p>This folder includes the data tables related to the phenotyping of the worms performed during this study. The phenotype measured was the viability of worms following PZQ treatment (i.e., PZQ response). This viability was assessed microscopically or using worm lactate production released in culture media.</p> <p>The data correspond to the following experiments:</p> <ul> <li>PZQ response of single adult male worms from SmLE and SmLE-PZQ-R populations to different doses of PZQ. This data was used to determine the PZQ IC50 of each population.</li> <li>Lactate production from single SmLE-PZQ-R adult male worms and correlation with visual observation. This was a proof-of-principle that lactate production can be used to efficiently and unbiasedly phenotype schistosome adult male worms in response to PZQ drug.</li> <li>PZQ response of single SmLE-PZQ-R adult male worms. These worms were then divided in low and high producer in response to PZQ and used to perform a genome-wide association study.</li> <li>PZQ response of single adult male worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations to different doses of PZQ. This data was used to determine the PZQ IC50 of each population.</li> <li>PZQ response of single adult male worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations in presence of Sm.TRPM_PZQ blocker (MB2) and activator (MV1) with and without PZQ drug.</li> <li>In vivo PZQ response of schistosome worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations.</li> </ul> <p><strong>2-Genotyping_data</strong></p> <p>This folder includes the data tables related to the genotyping of the worms performed during this study. Worms were genotyping using PCR-RFLP (genotyping of single nucleotide polymorphisms (SNPs) on chr2 and chr3 QTLs) or using qPCR (genotyping of a copy number variation (CNV) on chr3 QTL).</p> <p>The data correspond to the following experiment:</p> <ul> <li>Association between PZQ response of single adult male worms from SmLE-PZQ-R population and their respective genotype on chromosome 2 (SNP) and chromosome 3 (SNP and CNV) loci.</li> </ul> <p><strong>3-Fitness_data</strong></p> <p>This folder includes the data tables related to the fitness of the parasite populations. We collected data regarding:</p> <ul> <li>The number of surviving and infected snails after exposure to SmLE-PZQ-ER or SmLE-PZQ-ES miracidia.</li> <li>The number of adult worms recovered from golden Syrian female hamsters exposed to SmLE-PZQ-ER or SmLE-PZQ-ES cercariae.</li> </ul> <p>All the data were collected during 12 generations of parasites and are used to evaluate a potential impact of PZQ resistance on the parasite fitness.</p>
Figure 3. A in Taxonomic re-examination of the two camptandriid crab species Deiratonotus japonicus (Sakai, 1934) and D. tondensis Sakai, 1983, and genetic differentiation among their local populations
Figure 3. A male specimen having morphological characteristics of Deiratonotus tondensis, collected in the Tonda River, Wakayama (OMNH-Ar 6885). Scale bar: 5 mm.
Figure 6 in Taxonomic re-examination of the two camptandriid crab species Deiratonotus japonicus (Sakai, 1934) and D. tondensis Sakai, 1983, and genetic differentiation among their local populations
Figure 6. Male and female specimens of Deiratonotus japonicus. (A–C) Male from the Isuzu River, Shizuoka (OMNH-Ar 6879); (D) male from the Tonda River, Wakayama (OMNH-Ar 6885); (E) female from the Isuzu River, Shizuoka (OMNH-Ar 6879). (A, D) Cheliped; (B, E) abdomen; (C) first pleopod. Scale bar: 1 mm.
Figure 5 in Taxonomic re-examination of the two camptandriid crab species Deiratonotus japonicus (Sakai, 1934) and D. tondensis Sakai, 1983, and genetic differentiation among their local populations
Figure 5. Relationship between geographic distance and FST/(12FST) in Deiratonotus populations. Solid circles represent locality combinations showing significant FST values (P,0.05) and open circles represent locality combinations showing insignificant FST values.
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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.