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Figure 3. A in Expression analysis of phosphate induced genes in contrasting maize genotypes for phosphorus use efficiency
Figure 3. A – Northern blot analysis of phosphate starvation-induced ZmPTs genes in maize genotypes. B – Expression of ZmPTs in a plant grown in different phosphorus concentrations. C – Suppression of the ZmPTs expression by Pi resupply. D – Expression of ZmPT genes using RNA isolated from different root parts. E – Expression of ZmPTs in different maize plants under Pi starvation. F – Effect of duration of phosphate starvation on ZmPTs genes expression in maize genotypes. G – Expression of ZmPT homologs in roots of two sorghum genotypes. Total RNA isolated from different times of hydroponically grown plants supplied with half-strength modified Hoagland's solution containing 250 µM phosphate (+) or no phosphate (-) for different days or different concentrations as indicated. All the blots were probed with 32P labeled ZmPTs. The panel below the Northern blots is the ethidium bromide-stained gel prior to blotting showing the RNA integrity and uniformity of loading.
Data From: Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping
<p>This is the data set from all figures and tables from the manuscript "Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping", which is posted to the ChemRxiv preprint server (10.26434/chemrxiv-2024-5bqm7) and currently in consideration for peer-reviewed publication elsewhere.</p>
Java tool for PCR, in silico PCR and genotyping
<p>We performed in silico PCR analysis of several complete plant genomes using a list of primers corresponding to an inverted repeat sequence of Hordeum-Triticum Athos miniature inverted-repeat transposable element (MITE) sequences. MITE nonautonomous members of Class II element families are derived by internal deletion of autonomous elements, and they are short (70-300 bp in length) and have conserved terminal repeats.</p> <p>For example, Athos, one of the MITE families described in grasses. Athos element sequences were collected from the genome of Hordeum vulgare, of which there are about 205 per complete genome. The Athos element sequences are highly truncated, including partial loss of terminal inverted repeats in the barley genome. Sequences of terminal inverted repeats contain multiple point mutations, insertions or deletions, which creates a difficulty for the selection of universal primers that would cover all whole copies of this element. Therefore, we selected all unique sequence variants for terminal inverted repeats and used them as primers to identify and obtain complete MITE elements for genomes of other cereals and as a negative control, we used the genome of human and long-horned nomad bee (Nomada hirtipes). Since the sequences of terminal inverted repeats for Athos element were different and quite degenerate, we used all 46 unique variants simultaneously as Forward primer in the analysis. The same primer will act as Forward and also Reverse. The length of the primers was 15 nucleotides, which localise to the furthest region of the terminal inverted repeat at the Athos element. The size for the amplicon in this case could be 30 to 200 nucleotides, including truncated elements with a central part. We used search conditions with control options: type=primer number3errors=0; minlen=30; maxlen=200. The results of this analysis are represented in Table 2. In the genome of Hordeum vulgare we identified 768 Athos and related elements, which is much more than was detected by blast analysis (205 copies for GCF_904849725.1, Blast: RefSeq Genome Database). This is because we detected not only Athos elements but also related MITE elements with overlapping end repeats. </p> <p>In our analysis, we could only detect whole Athos and related elements that contained both repeats, whereas the central part could vary. For the Hordeum bulbosum genome, we detected a 1620 record number of complete Athos and related elements compared to other species of the Hordeum family. This corresponds to the doubled genome size of this species compared to other species of the Hordeum family. For wheat genomes (Aegilops tauschii, Triticum dicoccoides), being the most similar to species of the Hordeum family, numerous copies of the related Athos and related elements were detected, with this MITE occurring much more frequently in the wheat genome than in the genome of Hordeum vulgare. It is well observed that the copy number of Athos and related elements directly depends on the genome size; the larger the genome, the greater the copy number of this element detected.</p>
Fig. 1 in Genotypic diversity and epidemiology of Trichomonas gallinae in Columbidae: Insights from a comprehensive analysis
Fig. 1. Systematic phylogenetic tree based on Trichomonas.sp ITS1/5.8S/ITS2 gene typing. The length of ITS1/5.8S/ITS2 was 223bp. This tree was generated using MEGA-X software and calculated using the Tamamura 3-parameter (T92) + G model and the neighbor-joining method with 2,000 bootstrap replicates. Bootstrap values <70% were not shown.
Fig. 2 in Genotypic diversity and epidemiology of Trichomonas gallinae in Columbidae: Insights from a comprehensive analysis
Fig. 2. Time-driven diagram of the epidemic characteristics of T. gallinae. The red font in the genotype box indicated T. gallinae genotypes with high prevalence rates. The other red font denoted highly susceptible animals, whereas the thick blue arrow indicated the factors influencing the differences in T. gallinae positivity rates. The content inside the orange box highlighted the main factors affecting the differences in T. gallinae positivity rates. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Linked collectors and determiners for: Fungi and fungi-like organisms - 4EU+ Summer School From Fungal Morphology to Genotype, Hajnowka 2021.
Natural history specimen data linked to collectors and determiners held within, "Fungi and fungi-like organisms - 4EU+ Summer School From Fungal Morphology to Genotype, Hajnowka 2021". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/2634d165-1be7-43c7-8c5f-ac605ba2d0ec">https://bionomia.net/dataset/2634d165-1be7-43c7-8c5f-ac605ba2d0ec</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/2634d165-1be7-43c7-8c5f-ac605ba2d0ec">https://gbif.org/dataset/2634d165-1be7-43c7-8c5f-ac605ba2d0ec</a>. Formatted as a Frictionless Data package.
Knockdown resistance (kdr) genotypes and collection information for Aedes aegytpi from Iquitos, Peru (2000 - 2017)
<p>This study describes the evolution of <i>knockdown resistance (kdr)</i> haplotypes in <i>Aedes aegypti</i> in response to pyrethroid insecticide use over the course of 18 years in Iquitos, Peru. Based on the duration and intensiveness<span> of sampling (~10,000 samples), this is the most thorough study of kdr population genetics in <i>Ae. aegypti</i> to date within a city.</span> We provide evidence for the direct connection between programmatic citywide pyrethroid spraying and the increase in frequency of specific <i>kdr</i> haplotypes by identifying two evolutionary events in the population. The relatively high selection coefficients, even under infrequent insecticide pressure, emphasize how quickly <i>Ae. aegypti </i>populations can evolve. In our examination of the literature on mosquitoes and other insect pests, we could find no cases where a pest evolved so quickly to so few exposures to low or non-residual insecticide applications. <span>The observed rapid increase in frequency of resistance alleles might have been aided by the incomplete dominance of resistance-conferring alleles over corresponding susceptibility alleles.</span> In addition to dramatic temporal shifts, spatial suppression experiments reveal that genetic heterogeneity existed not only at the citywide scale, but also on a very fine scale within the city.</p>
Simultaneous genotyping of snails and infecting trematode parasites using high-throughput amplicon sequencing.
<p>Several methodological issues currently hamper the study of entire trematode communities within populations of their intermediate snail hosts. Here we develop a new workflow using high-throughput amplicon sequencing to simultaneously genotype snail hosts and their infecting trematode parasites. We designed primers to amplify 4 snail and 5 trematode markers in a single multiplex PCR. While also applicable to other genera, we focused on medically and economically important snail genera within the Superorder Hygrophila and targeted a broad taxonomic range of parasites within the Class Trematoda. We tested the workflow using 417 <i>Biomphalaria glabrata </i>specimens experimentally infected with <i>Schistosoma rodhaini</i>, two strains of<i> Schistosoma mansoni</i>,<i> </i>and combinations thereof. We evaluated the reliability of infection diagnostics, the robustness of the workflow, its specificity related to host and parasite identification, and the sensitivity to detect co-infections, immature infections, and changes of parasite biomass during the infection process. Finally, we investigated its applicability in wild-caught snails of other genera naturally infected with diverse trematode assemblages. After stringent quality control the workflow allows the identification of snails to species level, and of trematodes to taxonomic levels ranging from family to strain. It is sensitive to detect immature infections and changes in parasite biomass described in previous experimental studies. Co-infections were successfully identified, opening the possibility to examine parasite-parasite interactions such as interspecific competition. Altogether, these results demonstrate that our workflow provides a powerful tool to analyze the processes shaping trematode communities within natural snail populations.</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>
Fig. 4 in Among-Genotype Variation For Sediment Rejection In The Reef-Building Coral Diploastrea Heliopora (Lamarck, 1816)
Fig. 4. Percentage area cleared of sediment over the five-hour duration of the experiment calculated from 12 fragments (four replicates × three treatment) per genotype. Significantly more sediment was cleared under the 'low' sediment load compared to the 'medium' and 'high' loads (Table 2).
Fig. 5 in Among-Genotype Variation For Sediment Rejection In The Reef-Building Coral Diploastrea Heliopora (Lamarck, 1816)
Fig. 5. Percentage of sediment mass cleared after five hours (the duration of the experiment) calculated from 12 fragments (four replicates × three treatment) per genotype.
Fig. 2 in Among-Genotype Variation For Sediment Rejection In The Reef-Building Coral Diploastrea Heliopora (Lamarck, 1816)
Fig. 2. Profiles of natural sediments retrieved from sediment traps positioned at Pulau Hantu (solid line) and the artificial silicon carbide mixture (dotted line).
Fig. 14 in Congruent Patterns of Genetic and Morphological Variation in the Parthenogenetic Lizard Aspidoscelis tesselata (Squamata: Teiidae) and the Origins of Color Pattern Classes and Genotypic Clones in Eastern New Mexico
Fig. 14. Additive tree (phenogram), based on Mahalanobis D2 distances (table 13), depicting meristic resemblance among nine groups of Aspidoscelis tesselata. Distances (similarities) between groups are computed by adding lengths of nodes between groups of interest. Terminal nodes represent the nine groups, and internal nodes represent horizontal distances between clusters. As an interpretation example, the resemblance between Conchas 6CE and Conchas 1CE is 7.2 + 3.2 + 2.2 + 2.8 + 5.6 = 21.0, while the resemblance between Conchas 6CE and Macho EC is 7.2 + 1.0 + 1.9 + 3.1 = 13.2.
Fig. 3 in Congruent Patterns of Genetic and Morphological Variation in the Parthenogenetic Lizard Aspidoscelis tesselata (Squamata: Teiidae) and the Origins of Color Pattern Classes and Genotypic Clones in Eastern New Mexico
Fig. 3. Electrophoretic phenotypes of sACOH, a monomeric enzyme, from liver homogenates of nine specimens of A. tesselata of pattern class CE from Conchas Lake State Park, New Mexico. Letters below gel identify allozymes based on alleles present (table 3), and the genotype of each lizard is listed on the right. Lanes for individual lizards are labeled beside their patterns on the gel. Anode is to the right.
Fig. 11 in Congruent Patterns of Genetic and Morphological Variation in the Parthenogenetic Lizard Aspidoscelis tesselata (Squamata: Teiidae) and the Origins of Color Pattern Classes and Genotypic Clones in Eastern New Mexico
Fig. 11. Pattern of multivariate morphological variation among Aspidoscelis tesselata of pattern classes C (N = 44), E (N = 32), and New Mexico D (N = 5) from the vicinity of Sumner Lake State Park, De Baca County, New Mexico. Canonical variate scores were derived from a canonical variate analysis using meristic characters identified in table 10.
Fig. 2 in Congruent Patterns of Genetic and Morphological Variation in the Parthenogenetic Lizard Aspidoscelis tesselata (Squamata: Teiidae) and the Origins of Color Pattern Classes and Genotypic Clones in Eastern New Mexico
Fig. 2. Electrophoretic phenotypes of GPI, a dimeric enzyme, from erythrocyte hemolysates of six specimens of Aspidoscelis. Letters below gel identify allozymes based on alleles present (table 3), and the genotype of each lizard is listed on the right. Note the very slight difference in migration between the products of the ballele versus callele. Lanes for individual lizards are labeled beside their patterns on the gel as follows: TESC, A. tesselata of pattern class CE from Conchas Lake State Park, New Mexico; and TESE, A. tesselata of pattern class E from Sandoval County, New Mexico. Anode is to the right.
Fig. 1 in Congruent Patterns of Genetic and Morphological Variation in the Parthenogenetic Lizard Aspidoscelis tesselata (Squamata: Teiidae) and the Origins of Color Pattern Classes and Genotypic Clones in Eastern New Mexico
Fig. 1. Geographic relationships among four northern collecting localities of Aspidoscelis tesselata of color pattern classes C, New Mexico D, and E and convenience classes CE and EC. Color patterns found at the four sites are (1) Conchas Lake State Park: CE and New Mexico D; (2) Sumner Lake State Park: C, New Mexico D, and E; (3) Puerto de Luna: E; and (4) Arroyo del Macho: EC.
Fig. 7 in Congruent Patterns of Genetic and Morphological Variation in the Parthenogenetic Lizard Aspidoscelis tesselata (Squamata: Teiidae) and the Origins of Color Pattern Classes and Genotypic Clones in Eastern New Mexico
Fig. 7. Color pattern variation in Aspidoscelis tesselata of pattern class CE from the vicinity of Conchas Lake State Park, San Miguel County, New Mexico. Morphological subgroup 6CE: A (RU 0002, 93 mm SVL); B (RU 0029, 96 mm SVL); morphological subgroup 1CE: C (RU 0013, 95 mm SVL); D (RU 0030, 89 mm SVL); E (RU 0021, 95 mm SVL); morphological subgroup 8CE: F (RU 0027, 86 mm SVL).
Fig. 4 in Congruent Patterns of Genetic and Morphological Variation in the Parthenogenetic Lizard Aspidoscelis tesselata (Squamata: Teiidae) and the Origins of Color Pattern Classes and Genotypic Clones in Eastern New Mexico
Fig. 4. Electrophoretic phenotypes of MPI, a monomeric enzyme, from liver homogenates of 11 specimens of Aspidoscelis. Letters below gel identify allozymes based on alleles present (table 3), and the genotype of each lizard is listed on the right. Lanes for individual lizards are labeled beside their patterns on the gel (with genotype) as follows: NEOTESA, B, and C, different pattern classes of the triploid A. neotesselata from Colorado; TESC and D, A. tesselata of pattern classes CE and D from Conchas Lake State Park, New Mexico; TESE, A. tesselata of pattern class EC from Arroyo del Macho, New Mexico; TESF, A. dixoni from New Mexico; TESF × PUN, triploid hybrid of A. dixoni × A. tigris punctilinealis from New Mexico; and TESG and H, A. dixoni of two pattern classes from Texas. Anode is to the right.
Data from: "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat"
<p><em><strong>Data and code used for the study : "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat".</strong></em></p> <p>The script "Manuscript_Analyses.R" contains all code for the statistical analysis presented in the manuscript (main text & supplementary information). This script uses files produced in the folder "Locus-by-locus analysis" as inputs, and "manhattan_custom.R" as a source function ("manhattan_custom.R" is used to highlight SNPs in a given interval and to write specified SNPs name on manhattan plots). The file "Traits_monocultures.csv" contains the 20 functional traits measured on the 179 monoculture plots (see Supplementary Methods for more information on trait measurement). This file is used as an input in the script "Manuscript_Analyses.R".</p> <p>The "Locus-by-locus analysis" folder contains all analyses conducted to test the effect of allelic richness on the four variables of interest: Grain Yield (GY, g/m²), Spike Number per m² (SNb, nb spikes/m²), Thousand Kernel Weight (TKW, g), and Septoria tritici blotch (STB) severity. The locus-by-locus analysis is performed with the script "Allelic_richness_locus_by_locus_analysis.R". This analysis generates a list of .csv files with one file per chromosome. Each file contains the pvalues and estimated effect sizes of the tested SNPs for the given chromosome. These output files are stored in folders named after the variables for which the effect of allelic richness was tested ("RAW_GY", "RAW_SNb", "RAW_TKW", and "RAW_severity"). The script "Allelic_richness_locus_by_locus_output_processing.R" combines all .csv files into a single dataframe and produces three diagnostic plots: Manahattan plots, histograms of p-value distributions, and p-value q-q plots. p-value thresholds were computed based on a Family-Wise Error Rate of 5% using the Galwey correction. This is done in the "pvalue_thresholds" folder with the "Meff_computation.R" script. "Meff_computation.R" uses the "Meff_function.R" as a source function and generates "GY_thresholds.csv" and "STB_thresholds.csv" as outputs (these files contains different thresholds computed according to different methods but we only retained the Galwey method (most recent) for the analyses. Since GY, SNb, and TKW were analyzed with the same number of SNPs (~19K), we used the same significance threshold for the three variables ("GY_thresholds.csv"), whereas we computed a different thresholds for STB ("STB_thresholds.csv") for which we could only include ~6K SNPs in the analysis. The "geno_pos.csv" file contains the physical positions of the SNPs.</p> <p>Upstream the locus-by-locus analysis, phenotypic and genotypic files are prepared in the "Phenoytpic file preparation" and "Genotypic file preparation" folders, respecively.</p> <p>The phenotypic file preparation includes the correction of yield-related variables (GY, SNb, and TKW) for spatial auto-correlation in the "Spatial_analyses_YLD_variables" folder, and the computation of plot-level variables from individual-level variables with the "Allelic_richness_phenotypic_file_prep.R" script. In this script, we compute both absolute plot values (termed "RAW_...) and relative plot values (termed "RYT_..., only for mixture plots). All phenotypic files have the same structure with the same first 6 columns: "focal" = identity of the focal genotype (the one for which the variable is measured, only relevant for variables measured at the individual-level), "neighbor" = identity of the neighbor genotype (the neighbor of the genotype for which the variable is measured, only relevant for variables measured at the individual-level), "pair" = identity of the genotypic pair (combines the identity of the focal and the neighbor genotypes), "assoc" = type of plot ("M" = monoculture or pure stand plot, "P" = mixture plot), "row" = position of the plot along the smallest dimension of the grid (see Figure 1), "column" = position of the plot along the largest dimension of the grid (see Figure 1).</p> <p>The genotypic file preparation is done with the "Allelic_richness_genotypic_file_prep.R" script and includes SNP filtering, computation of matrices of allelic richness, and computation of matrices of genetic similarity between genotypic pairs. The analysis is done separatly for yield-related variables and for STB severity since the two types of variable were not measured on the same set of plots.</p>
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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.