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Figure 3 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 3. Published values of percent divergence between cetacean subspecies (black bars), species (white bars), and taxa of uncertain taxonomic status (gray bars). Values are based on mtDNA control region sequence data. Not all values represent net sequence divergence. See Table 1 for list of papers corresponding to each value. Since completing this work, Sousa species have been supported ((Mendez et al. 2013) and Inia subspecies changed.
Figure 1 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 1. Sample sizes used in publications of molecular genetic studies of marine mammals at different taxonomic levels. Graphs present the proportion of studies at each taxonomic level that fall into each sample size category. (A) minimum total sample size per focal taxon; (B) maximum sample size per single sampling locality. Papers were categorized as examining taxonomic questions at: species = subspecies/species boundary; subspecies = population/subspecies boundary; uncertain = taxonomic boundary uncertain (see text).
Figure 2 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 2. Types of molecular genetic data used in published studies examining questions at the species-level, subspecies-level, or undefined taxonomic level for marine mammals. Note that studies may have used more than one data type. Mitochondrial DNA sequence data (MtDNASeq), nuclear DNA sequence data (NuSeq), microsatellites (Msats), morphological data (Morph).
Figure 3 in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data
Figure 3. Flow diagram for subspecies delineation using combined quantitative and qualitative standards. The threshold values assume the user is evaluating a case relying on mtDNA control region data. Percent Diagnosable (PD) is the smallest strata-specific correct classification score in a given comparison (e.g., PD50 in two-strata comparisons in Archer et al. 2017). The second box in the second row (other evidence to meet subspecies definition) allows for subspecies delineation when both conditions are not met using mtDNA. This box could be used either for the case when one condition is met and one unmet or when both just barely miss meeting the standards. For example, consider the case with PD <95% and dA> 0.004. Diagnosability could be achieved with morphological data or nuclear data that are sufficient for subspecies but not for full species.
Figure 2. A in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data
Figure 2. A comparison of the pairs of populations (red triangles), subspecies (green squares) and species (blue circles) estimated by Rosel et al. (2017a). Net nucleotide divergence (dA) is shown on a natural log scale to better illustrate differences between the pairwise comparisons at low levels of divergence. Bars show the central 95th-pecentile of the estimate distributions. The solid vertical line at dA = 0.020 delimits all but one species and correctly excludes all subspecies pairs. The vertical dashed line at dA = 0.004 delimits all populations from the higher taxonomic levels and correctly delimits seven of eleven subspecies. The horizontal dashed lines are two potential thresholds for percent diagnosable (80% and 95%) that are discussed in the text.
Tilapia genetic diversity across the Lake Victoria Basin
<p>Genotypes for <em>Oreochromis niloticus</em>, <em>O. leucosticus,</em> <em>O. variabilis</em>, <em>O. esculentus</em>, <em>Coptodon rendalli</em>, <em>C. zillii </em>collected in water bodies in Kenya, Tanzania, and Uganda within the Lake Victoria Basin. </p> <p> </p>
High genome plasticity and frequent genetic exchange in Leishmania tropica isolates from Afghanistan, Iran and Syria
<p>Fastqs files for paper: "High genome plasticity and frequent genetic exchange in Leishmania tropica isolates from Afghanistan, Iran and Syria"</p>
Cast Away in the Adriatic: Low Degree of Parallel Genetic Differentiation in Three-Spined Sticklebacks
<p>This repository incorporates data and scripts associated with the paper "Cast Away in the Adriatic: Low Degree of Parallel Genetic Divergence in Three-Spined Sticklebacks".</p> <p>Included in the repository are the following compressed folders: </p> <ul> <li>Moments_models&data.zip: two-population demographic models and SFS to run in <em>moments.</em></li> <li>Stairwayplot.zip: blueprint files for multiple mutation rates and results for StairwayPlots.</li> <li>TreeMix.zip: pipeline to infer population splits and mixture events from allele frequency data using TreeMix, including input files.</li> <li>VCF.zip: a filtered VCF file for all three-spined stickleback samples.</li> </ul> <p>The scripts for TreeMix and demographic analyses in <em>moments</em> are also available on <a href="https://github.com/carolindahms">GitHub</a>.</p>
Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations
<p>Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. We hypothesized that this variability could be partially explained by genetic polymorphism in neuromodulatory genes. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in Catechol-O-methyltransferase (COMT) Val<sup>158</sup>Met and brain-derived neurotrophic factor (BDNF) Val<sup>66</sup>Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in neuronal oscillation dynamics. Comparison of these results to computational modeling of near-critical synchronization dynamics further suggested that COMT and BDNF polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework.</p>
Summary statistics from "Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease"
<p>GWAMA summary statistics of four steroid hormone levels using fixed-effect model and GWAS summary statistics of four other steroid hormones.</p> <p>When using this data, please cite: Pott J, Bae YJ, Horn K, et al.. Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease. <em>J Clin Endocrinol Metab</em> <strong>2019</strong> Nov 1;104(11):5008-5023. doi: 10.1210/jc.2019-00757</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>effect_allele</li> <li>other_allele</li> <li>effect_allele_freq</li> <li>min_info (minimal info score across all used studies)</li> <li>n (sample size per SNP)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>CochransQ (only in GWAMA; SNP heterogeneity across studies)</li> <li>pCochransQ (only in GWAMA; p-value of Cochrans Q value)</li> </ul>
Summary statistics for association tests between human and Plasmodium falciparum genetic variants in 3,346 severe malaria cases from The Gambia and Kenya
<p>This dataset contains summary statistics for association tests between human and<br> <em>Plasmodium falciparum</em> malaria parasite genetic variants, using data from 3,346 severe malaria cases from The Gambia and Kenya. These results underlie the analysis described in our paper:</p> <p><strong>"Malaria protection due to sickle haemoglobin depends on parasite genotype"</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia M. Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>:: <a href="https://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a><br> <br> The genotype data underlying these summary statistics has also been deposited on Zenodo<br> (<a href="https://zenodo.org/record/4973477">doi:10.5281/zenodo.4973477</a>). The <a href="https://www.well.ox.ac.uk/~gav/hptest)">HPTEST software</a> used to generate these results has also been deposited (<a href="https://doi.org/10.5281/zenodo.5685580">doi:10.5281/zenodo.5685580</a>). Please see the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a> for a full list of datasets which have been released with this manuscript.</p> <p><strong>Data contents.</strong></p> <p>The dataset consists of a single <a href="http://sqlite.org">sqlite database file</a> containing the results, and an accompanying README file in markdown and html format. Please see the README file for full details of the data contents.</p> <p> </p>
Fig. 1 in Morphological and Genetic Characterization of the First Species of Thalassodrilides (Annelida: Clitellata: Naididae: Limnodriloidinae) from Japan
Fig. 1. Thalassodrilides cf. briani from Ehime Prefecture, Japan (NSMT-An 507), drawings from whole mounts. A, ventral chaetae from anterior segments; B, reproductive system in segments X–XII; C, detail of male reproductive structures from a live specimen; D, detail of spermatheca from a live pre-copulatory specimen. Scale bars: 50 µm.
Fig. 2 in Morphological and Genetic Characterization of the First Species of Thalassodrilides (Annelida: Clitellata: Naididae: Limnodriloidinae) from Japan
Fig. 2. Thalassodrilides cf. briani from Ehime Prefecture, Japan, microscopic photographs from live specimens. A, ventral chaetae from anterior segments; B, male reproductive structures; C, pre-copulatory spermatheca; D, mature (post-copulatory) spermatheca. Scale bars: 50 µm.
Fig. 1. Collectionsitesfor 91 specimensfrom 29 locationsof Apodemusagrarius, usedin thisstudy. ThespecimennumberforeachlocationarelistedinTable 1 in Comparative Genetics Of Apodemus Agrarius (Rodentia: Mammalia) From Insular And Continental Eurasian Populations: Cytochrome B Sequence Analyses
Fig. 1. Collectionsitesfor 91 specimensfrom 29 locationsof Apodemusagrarius, usedin thisstudy. ThespecimennumberforeachlocationarelistedinTable 1, andminimum Jukes-Cantordistancesbetween 17 haplotypesfrommainlandKoreaandother 49 haplo- typesfromsevenregions (Taiwan, Jeju, northeasternChina, easternChina, centralChina, Far-easternRussia, andEurope) basedonthecytochrome b completesequencesaregiven
Fig. 1 MapshowingtherangeofthestudypopulationofEasternImperialEaglesinHungaryandthelocationofsampledandnotsampledterritoriesin 2003 in High Turnover Rate Revealed By Non-Invasive Genetic Analyses In An Expanding Eastern Imperial Eagle Population
Fig. 1 MapshowingtherangeofthestudypopulationofEasternImperialEaglesinHungaryandthelocationofsampledandnotsampledterritoriesin 2003 (35 ofthe 61 nesting
Fig. 2 in High Turnover Rate Revealed By Non-Invasive Genetic Analyses In An Expanding Eastern Imperial Eagle Population
Fig. 2. Firstidentification (1999, territorycodeBS-02) andre-identification (2003, BS-03) of afemale. Theterritorieswereapproximately 10 kmawayfromeachotherandtheoriginal BS-02 territorywasvacantin 2001-2002, butitwasoccupiedbyapairwithanewfemale in 2003; differentmarkingsrepresentdifferentgeneticallytaggedfemales, blackmarkings representthenestsfromtheBS-02 territory, greymarkingsrepresentnestsfromtheBS-03 territory; yearsinitalic (nestsmarkedbycircles) representnestingsiteswithoutsamples.
Fig. 2 in Allozyme-Based Genetic Variability Of The Daphnia Atkinsoni-Bolivari Species Complex (Cladocera: Daphniidae) In The Hungarian Great Plain
Fig. 2. UPGMAclusteringofNei'soriginalgeneticdistancesbetweenpopulationsof Daphniaatkinsoni and D. bolivari. Themorphological D. bolivari populationsaremarkedwith
Fig. 6 in Pattern Of Genetic And Morphometric Differentiation In Maculinea Nausithous (Lepidoptera: Lycaenidae) In The Carpathian Basin
Fig. 6. The results of AMOVA computed on the genetic data and hierarchical ANOVA of the morphometric data. A = AMOVA of both regions together. B = AMOVA of the two regions separately. C = Hierarchical ANOVA of both regions together. D = Hierarchical ANOVA of the two regions separately. The patterns of the columns are consistent in all charts. BR: between region component of variance (dark grey); BP: variation among the populations within the regions (black); BS: variation among the samples/generations within the populations (white); WS: within sample compo-
Fig. 4 in Pattern Of Genetic And Morphometric Differentiation In Maculinea Nausithous (Lepidoptera: Lycaenidae) In The Carpathian Basin
Fig. 4. UPGMA dendrogram constructed using CAVALLI-SFORSA & EDWARDS chord distances with a Maculinea teleius sample (3tKv) as out group. Bootstrap values were obtained using 2000 replicates
Fig. 2 in Pattern Of Genetic And Morphometric Differentiation In Maculinea Nausithous (Lepidoptera: Lycaenidae) In The Carpathian Basin
Fig. 2. Measured traits on the wings of Maculinea nausithous. Forewing: anal length (a), length of the outer margin (b), apical angle (β). Hindwing: anal length (c), costal length (d), basal angle (γ), widths
ScienceDex guides
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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.