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5,153 results for “Genetic data”
Fig. 3 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 3. Heatmap of pairwise genetic distances estimated from nucleotide sequences of the cytochrome b gene (479 nucleotides) of Haemoproteus spp. using the JukesCanter model.
Fig. 1 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 1. Localities of the raptors included in this study. There were 30 provinces where the raptors are found and submitted into the Kasetsart University Raptor Rehabilitation Unit. These provinces are divided into four groups base on the number of raptors. Bangkok is the most common locality of raptor (n> 100).
Supporting data and code for: Demographic and genetic impacts of powdery mildew in a young oak cohort
<p>This is a new release following the submission of the related PCI recommended manuscript to the <em>Annals of Forest Science</em> journal. It contains the necessary scripts to produce most of the analyses and figures of the manuscript. Apart from minor modifications following the recommendation in '<em>PCI Forest and Wood Sciences</em>', the main change is the addition of an extra dataset "Data_S2.txt" to the additional datasets. This dataset was previously included as a table in the 'supplementary material' file.</p>
Replication Data For: Spiking patterns in the globus pallidus highlight convergent neural dynamics across diverse genetic dystonia syndromes
<div><strong>Human Globus Pallidum Single-Unit Activity Dataset in Genetic Dystonia Patients</strong></div> <div> </div> <div>This dataset consists of tabular data encompassing diverse neural features extracted from spiking trains of stable single-unit activity. These units were isolated from raw microelectrode recordings obtained from the globus pallidum of genetic dystonia patients who underwent globus pallidus internal (GPi) deep brain stimulation (DBS) surgery. The dataset includes anonymized patient IDs, details about the patient's genetic dystonia mutation, as well as information on the hemisphere and depth of microelectrode recordings (MER). Additionally, it features neural properties such as firing rate, spiking regularity, neural bursts, oscillations, and pause characteristics of isolated single-unit activities (SUAs).</div> <div> </div> <div>To process the raw MER, we applied a semi-parametric offline spike sorting algorithm to isolate SUAs. The SUAs were analyzed both in the temporal and frequency domains to derive a comprehensive set of features related to spiking patterns.</div> <div> </div> <div>For those interested in replicating or understanding the feature extraction process, the MATLAB source code is available in the <a href="github.com/ahmetofficial/Spike-Feature-Generator">Github repository</a>.</div>
Linked collectors and determiners for: A revision of the West African freshwater crab genus Afrithelphusa Bott, 1969 (Brachyura: Deckeniidae: Deckeniinae) based on new morphological and genetic data.
Natural history specimen data linked to collectors and determiners held within, "A revision of the West African freshwater crab genus Afrithelphusa Bott, 1969 (Brachyura: Deckeniidae: Deckeniinae) based on new morphological and genetic data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1cba0b87-5819-44c5-a905-a5fbdf5ab7b7">https://bionomia.net/dataset/1cba0b87-5819-44c5-a905-a5fbdf5ab7b7</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1cba0b87-5819-44c5-a905-a5fbdf5ab7b7">https://gbif.org/dataset/1cba0b87-5819-44c5-a905-a5fbdf5ab7b7</a>. Formatted as a Frictionless Data package.
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>
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>
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>
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>
Data: Genetic diversity of Colletotrichum lupini and its virulence on white and Andean lupin
<p>DATA</p> <p>Lupin cultivation worldwide is threatened by anthracnose, a destructive disease caused by the seed- and air-borne fungal pathogen <em>Colletotrichum lupini</em>. In this study we explored the intraspecific diversity of 39 <em>C. lupini</em> isolates collected from different lupin cultivating regions around the world, and representative isolates were screened for their pathogenicity and virulence on white and Andean lupin. Multi-locus phylogeny and morphological characterizations showed intraspecific diversity to be greater than previously shown, distinguishing a total of six genetic groups and ten distinct morphotypes. Highest diversity was found across South America, indicating it as the center of origin of <em>C. lupini</em>. The isolates that correspond to the current pandemic belong to a genetic and morphological uniform group, were globally widespread, and showed high virulence on tested white and Andean lupin accessions. Isolates belonging to the other five genetic groups were mostly found locally and showed distinct virulence patterns. Two highly virulent strains were shown to overcome resistance of advanced white lupin breeding material. This stresses the need to be careful with international seed transports in order to prevent spread of currently confined but potentially highly virulent strains. This study improves our understanding of the diversity, phylogeography and pathogenicity of a member of one of the world’s top 10 plant pathogen genera, providing valuable information for breeding programs and future disease management.</p>
Data from: Genetic and ecogeographic controls on species cohesion in Australia's most diverse lizard radiation
<p>Species vary extensively in geographic range size and climatic niche breadth. If range limits are primarily determined by climatic factors, species with broad climatic tolerances and those that track geographically widespread climates should have large ranges. However, large ranges might increase the probability of population fragmentation and adaptive divergence, potentially decoupling climatic niche breadth and range size. Conversely, ecological generalism in widespread species might lead to higher gene flow across climatic transitions, increasing species' cohesion and thus decreasing genetic isolation-by-distance (IBD). Focusing on Australia's iconic <em>Ctenotus</em> lizard radiation, we ask whether species range size scales with climatic niche breadth and the degree of population isolation. To this end, we infer independently evolving operational taxonomic units (OTUs), their geographic and climatic ranges, and the strength of IBD within OTUs based on genome-wide loci from 722 individuals spanning 75 taxa. Large-ranged OTUs were common and had broader climatic niches than small-ranged OTUs; thus, large ranges do not simply result from passive tracking of widespread climatic zones. OTUs with larger ranges and broader climatic niches showed relatively weaker IBD, suggesting that large-ranged species might possess intrinsic attributes that facilitate genetic cohesion across large distances and varied climates. By influencing population divergence and persistence, traits that affect species cohesion may play a central role in large-scale patterns of diversification and species richness.</p>
Fig. 8 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 8. Photos in Chemnitz (1784) referred by Röding (1798) firstly named the giant clam species "noae" (no. 494), "maxima" (no. 495), and Tridacna derasa (no. 497).
Fig. 6 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 6. Shell morphology of Tridacna maxima from Hongchia with prominent rib scales on right valve (A–F) and Tridacna noae from Naliao (G–L). R: rib; S: scale.
Fig. 4 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 4. Neighbour joining tree of Tridacninae using Kimura 2-parameter model based on 18S rRNA gene sequence. Bootstrap values: 1,000; outgroup: Corculum cardissa.
Fig. 2 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 2. Neighbour joining tree of Tridacninae using Kimura 2-parameter model based on Cytochrome c oxidase subunit 1(COI) gene sequence. Bootstrap values: 1,000; outgroup: Corculum cardissa.
Fig. 3 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 3. Neighbour joining tree of Tridacninae using Kimura 2-parameter model based on 16S rRNA gene sequence. Bootstrap values: 1,000; outgroup: Corculum cardissa.
Fig. 5 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 5. Neighbour joining tree of Tridacninae using Kimura 2-parameter model based on 28S rRNA gene sequence. Bootstrap values: 1,000; outgroup: Corculum cardissa.
Fig. 7 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 7. Mantle colour pattern and hyaline organs of Tridacna maxima (A, B) and Tridacna noae (C, D). E, Enlarged hyaline organs of T. maxima; F, Enlarged hyaline organs of T. noae. →: Hyaline organs.
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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.