Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,466
datasets available to search
ShareScore release 0.7.1
Dataset results
1,466 results for “genetic structure”
Genetic structure in patchy populations of a candidate foundation plant: a case study of Leymus chinensis using genetic and clonal diversity
<p><strong>PREMISE</strong>: The distribution of genetic diversity on the landscape has critical ecological and evolutionary implications. This may be especially the case on a local scale for foundation plant species since they create and define ecological communities, contributing disproportionately to ecosystem function.</p> <p><strong>METHODS</strong>: We examined the distribution of genetic diversity and clones, which we defined first as unique multilocus genotypes (MLG), and then by grouping similar MLGs into multilocus lineages (MLL). We used 186 markers from inter-simple sequence repeats (ISSR) across 358 ramets from 13 patches of the foundation grass <em>Leymus chinensis</em>. We examined the relationship between genetic and clonal diversities, their variation with patch-size, and the effect of the number of markers used to evaluate genetic diversity and structure in this species.</p> <p><strong>RESULTS</strong>: Every ramet had a unique MLG. Almost all patches consisted of individuals belonging to a single MLL. We confirmed this with a clustering algorithm to group related genotypes. The predominance of a single lineage within each patch could be the result of the accumulation of somatic mutations, limited dispersal, some sexual reproduction with partners mainly restricted to the same patch, or a combination of all three.</p> <p><strong>CONCLUSIONS</strong>: We found strong genetic structure among patches of <em>L. chinensis</em>. Consistent with previous work on the species, the clustering of similar genotypes within patches suggests that clonal reproduction combined with somatic mutation, limited dispersal, and some degree of sexual reproduction among neighbors causes individuals within a patch to be more closely related than among patches.</p>
Fig. 2 in Genetic Diversity And Place In The General Phylogeographic Structure Of Capercaillie,Tetrao Urogallus (Galliformes, Phasianidae), From Belarus
Fig. 2. Reconstruction of the phylogeny of the capercaillie according to the polymorphism of the control region of mtDNA. Red dots — sequences from Belarus (this study).
Fig. 3 in Genetic Diversity And Place In The General Phylogeographic Structure Of Capercaillie,Tetrao Urogallus (Galliformes, Phasianidae), From Belarus
Fig. 3. Network of capercaillie haplotypes according to the mtDNA control region. Balkans — the Balkan Peninsula, E_Europe — Eastern Europe, N_Europe — Northern Europe, W_Russia — Western Russia (up to Ural Mountains), W_Europe — Western Europe, NW_Russia — Northwest Russia, C_Europe — Central Europe.
Fig. 1 in Genetic Diversity And Place In The General Phylogeographic Structure Of Capercaillie,Tetrao Urogallus (Galliformes, Phasianidae), From Belarus
Fig. 1. Distribution of samples of the capercaillie. Black circles are samples obtained independently, black squares are mtDNA sequences (control region) downloaded from the GenBank database (see Appendix, table 1).
The genetic structure and connectivity in two sympatric rodent species with different life histories are similarly affected by land use disturbances
<p><strong>Microsatellite dataset of the wood mouse (<em>Apodemus sylvaticus)</em> and the bank vole (<em>Myodes glareolus).</em></strong></p> <p>The dataset of the wood mouse is constituted of 194 samples and 7 microsatellite markers: WM_194ind_7STRs.txt</p> <p>The dataset of the bank vole is constituted of 199 samples and 8 microsatellite markers: BV_199ind_8STRs.txt</p> <p>Each locus is encoded in the three-digit format (e.g., 126126) and each column corresponds to a locus specified in the order at the beginning of the file, following the GENEPOP format.</p> <p>Pop indicates the beginning of a new location.</p> <p> </p> <p><em><strong>Locus name in WM_194ind_7STRs.txt</strong></em></p> <p>Locus_1 AS-7-FAM<br> Locus_2 AS-12-PET<br> Locus_3 AS-20-NED<br> Locus_4 AS-34-FAM<br> Locus_5 GTTD9A-PET<br> Locus_6 AS-11-VIC<br> Locus_7 MS-AF-8-NED</p> <p> </p> <p><em><strong>Locus name in BV_199ind_8STRs.txt</strong></em></p> <p>Locus_1 Cg13B8-F_FAM<br> Locus_2 Cg6A1-F_VIC<br> Locus_3 Cg3F12-F_PET<br> Locus_4 Cg13H9-F_PET<br> Locus_5 Cg2E2-F_VIC<br> Locus_6 Cg3E10-F_FAM<br> Locus_7 Cg2A4-F_FAM<br> Locus_8 Cg3A8-F_NED</p>
Figure 2. The general structure of the proposed approach-Genetic Algorithms Principles Towards Hidden Markov Model
<p>The chromosome contains 8 genes, each is represented by the relation between two states<br> accompanied with a probability value. The genes should be formed in this way because this is<br> important in the crossover operation as to be explained later. The most important thing is that each<br> two genes has the probability summation of 1.0. For example Med-Med:02 and Med-High:08 have<br> the summation of 1.0. Similarly High-High:0.6 and High-Med:0.4 have the summation of 1.0. Each<br> two genes with summation of 1.0 should be neighbors.</p>
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
<p>Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (<em>Pinus pinaster</em> Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.</p>
Figure 2 in Genetic structure of Trypanosoma congolense "forest type" circulating in domestic animals and tsetse flies in the South-West region of Cameroon
Figure 2. NJ Tree based on Cavalli-Sforza and Edwards chord distance matrix of T. congolense "forest type" circulating in tsetse flies and domestic animals of Fontem.
Fig. 1 in The Dynamics Of Genetic Structure Of Round G O B Y N E O G O B I U S M E L A N O S T O M U S (Pa L L A S) Groupings In The Odessa Bay Of The Black Sea Utilizing Biochemical Marker Loci
Fig. 1. Frequencies of S-alleles by polymorphic locus Es2 in round goby groupings from different parts of the Odessa Bay. * – significant deviation of allele frequencies in round goby groupings from the south and the north part of the Odessa Bay (Р = 0,05); # – significant deviation of allele frequencies in round goby groupings in the south part of the Odessa Bay in 2015-2016 in comparison to 2013-2014 (Р = 0,05).
Fig. 2 in The Dynamics Of Genetic Structure Of Round G O B Y N E O G O B I U S M E L A N O S T O M U S (Pa L L A S) Groupings In The Odessa Bay Of The Black Sea Utilizing Biochemical Marker Loci
Fig. 2. Frequencies of S-alleles by polymorphic locus of myogene 3 in round goby groupings from different parts of the Odessa Bay * – significant deviation of allele frequencies in round goby groupings from the south and the north part of the Odessa Bay in 2013 and 2014 (Р = 0,05); # – significant deviation of allele frequencies in round goby groupings from the south part of the Odessa Bay in 2013-2014 in comparison to 2015 (Р = 0,05).
FIGURE 2 in Extinction risk or lack of sampling in a threatened species: Genetic structure and environmental suitability of the neotropical frog Pristimantis penelopus (Anura: Craugastoridae)
FIGURE 2: (Left) Maximum clade credibility tree depicting the phylogenetic position of Pristimantis penelopus within the P. ridens series. Numbers on nodes indicate posterior probabilities. Numbers below nodes represent nodal support using the ultrafast bootstrap (see methods). Asterisks indicate nodal support above 95% in both Bayesian and ML methods. (Right) Haplotype network based on 460 bp of the COI region. Numbers of mutational steps are shown on the lines connecting haplotypes. Colors refer to geographic locations shown in Figure 1.
FIGURE 4 in Extinction risk or lack of sampling in a threatened species: Genetic structure and environmental suitability of the neotropical frog Pristimantis penelopus (Anura: Craugastoridae)
FIGURE 4: Potential distribution of Pristimantis penelopus based on ecological niche modeling (red). Yellow dots represents occurrence localities used to calibrate the model. See main text for details.
FIGURE 1 in Extinction risk or lack of sampling in a threatened species: Genetic structure and environmental suitability of the neotropical frog Pristimantis penelopus (Anura: Craugastoridae)
FIGURE 1: Geographic sampling of Pristimantis penelopus. Colored circles indicate sequenced specimens. Different colors represent the populations used in the genetic analysis (see Figure 2 for color codes).
FIGURE 3 in Extinction risk or lack of sampling in a threatened species: Genetic structure and environmental suitability of the neotropical frog Pristimantis penelopus (Anura: Craugastoridae)
FIGURE 3: Phenotypic variation of Pristimantis penelopus across its distribution. Localitites are shown in Appendix 1.
Figure 3 in Infraspecific genetic variation and population structure of Salvia nemorosa L. (Lamiaceae) in Iran
Figure 3. PCoA plot of the studied populations based on ISSR data (population numbers are according to Table 1).
Figure 2 in Infraspecific genetic variation and population structure of Salvia nemorosa L. (Lamiaceae) in Iran
Figure 2. MDS plot of the studied populations based on ISSR data (population numbers are according to Table 1).
Figure 4 in Infraspecific genetic variation and population structure of Salvia nemorosa L. (Lamiaceae) in Iran
Figure 4. NJ tree of S. nemorosa populations based on ISSR results (population numbers are according to Table 1).
Figure 8 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 8. TCS network of the studied S. limbata and their individuals (numbers indicated the populations based on Table 1).
Figure 6 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 6. STRUCTURE analysis of the studied populations, which revealed the best number of K=7 (numbers indicated the populations based on Table 1).
Figure 5 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 5. NJ tree of the evaluated populations and their individuals based on ISSR data (numbers indicated the populations based on Table 1).
ScienceDex guides
Understand access before you commit
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.