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1,337 results for “genetic variations”
Database of Pines from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown Under Two Watering Regimes: Implications for Management of Genetic Resources"
<p>Raw data from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown under Two Watering Regimes: Implications for Management of Genetic Resources" Forests <strong>2018</strong> <em>9</em>(2), 71. doi:<a href="http://dx.doi.org/10.3390/f9020071">10.3390/f9020071. </a></p> <p>The database correspond to seedlings of four Mexican pines: <em>P. oocarpa, P. patula</em> and <em>P. pseudostrobus</em>, that were submitted to two watering treatments: Field Capacity (FC) and Drought-Stress (DS), during 90 days. Growth and biomass, survival and ontogenetic score were measured.</p>
Link to Dataset related to article "Interpreting Non-coding Genetic Variation in Multiple Sclerosis Genome-Wide Associated Regions"
<p>Link to Dataset related to article "Interpreting Non-coding Genetic Variation in Multiple Sclerosis Genome-Wide Associated Regions"</p> <p>Multiple sclerosis (MS) is the most common neurological disorder in young adults. Despite extensive studies, only a fraction of MS heritability has been explained, with association studies focusing primarily on protein-coding genes, essentially for the difficulty of interpreting non-coding features. However, non-coding RNAs (ncRNAs) and functional elements, such as super-enhancers (SE), are crucial regulators of many pathways and cellular mechanisms, and they have been implicated in a growing number of diseases. In this work, we searched for possible enrichments in non-coding elements at MS genome-wide associated loci, with the aim to highlight their possible involvement in the susceptibility to the disease. We first reconstructed the linkage disequilibrium (LD) structure of the Italian population using data of 727,478 single-nucleotide polymorphisms (SNPs) from 1,668 healthy individuals. The genomic coordinates of the obtained LD blocks were intersected with those of the top hits identified in previously published MS genome-wide association studies (GWAS). By a bootstrapping approach, we hence demonstrated a striking enrichment of non-coding elements, especially of circular RNAs (circRNAs) mapping in the 73 LD blocks harboring MS-associated SNPs. In particular, we found a total of 482 circRNAs (annotated in publicly available databases) vs. a mean of 194 ± 65 in the random sets of LD blocks, using 1,000 iterations. As a proof of concept of a possible functional relevance of this observation, we experimentally verified that the expression levels of a circRNA derived from an MS-associated locus, i.e., hsa_circ_0043813 from the <em>STAT3</em> gene, can be modulated by the three genotypes at the disease-associated SNP. Finally, by evaluating RNA-seq data of two cell lines, SH-SY5Y and Jurkat cells, representing tissues relevant for MS, we identified 18 (two novel) circRNAs derived from MS-associated genes. In conclusion, this work showed for the first time that MS-GWAS top hits map in LD blocks enriched in circRNAs, suggesting circRNAs as possible novel contributors to the disease pathogenesis.</p> <p>GEO database</p> <p>URL: <a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a></p> <p>Numero di accesso del dataset: GSE110525</p>
Datasets for "Intraspecific interactions in the annual legume Medicago minima are shaped by both genetic variation for competitive ability and reduced competition among kin"
<p>Datasets for “Intraspecific interactions in the annual legume <em>Medicago minima</em> are shaped by both genetic variation for competitive ability and reduced competition among kin”</p> <p>Two datasets are provided.</p> <p>root_behavior_experiment_for_ms.csv: provides data relative to a root behaviour experiment where <em>Medicago minima</em> genotypes grew either with a kin or a non kin. Direction of root growth, root length and biomass were measured.</p> <p>Medicago_minima_biomass_dataMerge.csv: provides data relative to a minicommunity experiment where <em>Medicago minima </em>genotypes were grown surrounded by three kin genotypes, or three non-kin genotypes (i.e. stranger to the central plant but identical to each other) or three stranger genotypes (stranger to the central plant and to each other). For this second experiment above-ground growth and biomass were monitored.</p> <p>Detailed information on the dataset variables are provided in the metadata file.</p> <p>Code for data wrangling and analyses is included in the manuscript as an appendix.</p>
Figure 3 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP
Figure 3. Restriction patterns of HinfI inferred from D-loop digestion (M: Marker–100bp DNA Ladder, 1. Ordu, 2. Trabzon, 3. Rize, 4. Artvin, 5–6. Erzincan, 7–8. Kahramanmaraş, 9. Adıyaman, 10–11. Adana, 12. Muğla, 13. Burdur, 14. Konya, 15. Antalya, 16. Mersin, 17. Kastamonu, 18. Zonguldak, 19. Düzce, 20. Balıkesir, 21. İzmir, 22. Aydın, 23. A. uralensis, 24. A. witherbyi, 25. D-loop PCR products).
Figure 2 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP
Figure 2. Restriction patterns of MboI, HaeIII, and RsaI inferred from cytb digestion (M: Marker–100bp DNA Ladder, 1. Ordu, 2. Trabzon, 3. Rize, 4. Artvin, 5. Erzincan, 6. Kahramanmaraş, 7. Adıyaman, 8. Adana, 9. Muğla, 10. Burdur, 11. Konya, 12. Antalya, 13. Mersin, 14. Kastamonu, 15. Zonguldak, 16. Düzce, 17. Balıkesir, 18. İzmir, 19. Aydın, 20. A. uralensis, 21. A. witherbyi, 22. Cytb PCR product).
Figure 5 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP
Figure 5. PCoA analysis of A. mystacinus clades. The scatter plot is of the scores of three principal eigenvalues inferred from NTSYS software. Each scatter point represents a specimen of A. mystacinus.
Figure 2 in Investigation of genetic variation among Turkish populations of Andricus lignicola using mitochondrial cytochrome b gene sequence data
Figure 2. Bayesian analysis tree. Posterior probability values are given on the branches. Outgroup haplotypes: Ac (Andricus caliciformis) and Ak (Andricus kollari).
Figure 1 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP
Figure 1. Sampling localities of A. mystacinus specimens. Table 2. Restriction enzymes and their digestion sites with reaction procedures.
Figure 2. A in Genetic differentiation of the Meriones tristrami (Mammalia: Rodentia) subpopulations in Turkey - inferring allozyme variations
Figure 2. A dendrogram summarizing the genetic relationships of M. tristrami subpopulations (NTSYSpc options: Coefficient: SM (SimQual), clustering method: UPGMA) (see Nei, 1978) (Mt1 = Gaziantep, Adana, Mt2 = Central Anatolia (Cihanbeyli/Konya, Sivrihisar/Eskişehir), Mt3 = Denizli, Mt4 = Şanlıurfa, Mt5 = Iğdır, Mt6 = Tosya/Kastamonu, Mt7 = Karadağ/Karaman, Mt8 = Turgutlu/Manisa).
Figure 1 in Genetic differentiation of the Meriones tristrami (Mammalia: Rodentia) subpopulations in Turkey - inferring allozyme variations
Figure 1. Map of the locations of the M. tristrami specimens in Turkey (Gaziantep, Adana: Mt1; Central Anatolia (Cihanbeyli/Konya, Sivrihisar/Eskişehir): Mt2; Denizli: Mt3; Şanlıurfa: Mt4; Iğdır: Mt5; Tosya/Kastamonu: Mt6; Karadağ/Karaman: Mt7; Turgutlu/ Manisa: Mt8).
Figure 3 in Occlusal surface variations in genetically-identified specimens of the genus Apodemus (Mammalia: Rodentia) distributed in the Northern Anatolia region and three Turkish islands: Gökçeada, Marmara Island, and Bozcaada
Figure 3. The variations observed in the left upper teeth and their locations. D1: Presence of a bis structure immediately adjacent to the t2 cusp in UM1, D2: Presence of the t12 cusp in UM1, D3: Presence of a spur structure extending posteriorly from the t3 cusp in UM1 but not merging with the t5 cusp, D4: Existence of a bridge between the t1 cusp and t5 cusp in UM1, D5: Connection through a bridge between the t4 cusp and t7 cusp in UM1, D6: Structural swelling and island-like condition of the t7 cusp in UM1, D7: Structural line-like condition of the t7 cusp in UM1, D8: Structural swelling and island-like condition of the t7 cusp in UM2, D9: Line-like structural condition of t7 in UM2, D10: Presence or absence of the t12 cusp in UM2, D11: Presence of a bridge-like structure between the t1 cusp and t5 cusp in UM2, D12: Presence of a bridge between the t4 cusp and t7 cusp in UM2, D13: Existence of a bridge structure between the t1 cusp and t5 cusp in UM3, D14: Presence of a connection between the t6 cusp and t8 cusp in UM3.
Figure 4 in Occlusal surface variations in genetically-identified specimens of the genus Apodemus (Mammalia: Rodentia) distributed in the Northern Anatolia region and three Turkish islands: Gökçeada, Marmara Island, and Bozcaada
Figure 4. The intra- and interspecies schematic diagram of left lower dental variations. Diagram A: refers to the island population of A. sylvaticus, while B: refers to the mainland population of A. sylvaticus. Numbers represent species; 1: A. flavicollis, 2: A. witherbyi, 3: A. sylvaticus, 4: A. uralensis, 5: A. mystacinus. Lowercase letters have been selected as variation characteristics; a: number of cingula in LM1, b: tma in LM1, c: central distoconid in LM1, d: aneroconid complex in LM1, e: number of cingula in LM2, f: central distoconid in LM2.
Figure 1 in Occlusal surface variations in genetically-identified specimens of the genus Apodemus (Mammalia: Rodentia) distributed in the Northern Anatolia region and three Turkish islands: Gökçeada, Marmara Island, and Bozcaada
Figure 1. Map of the locations where the samples were collected and main locations (modified from Çolak et al., 2013) A: The Dardanelles, B: The Bosphorus, C: Kızılırmak, D: Melet River, E: Çoruh River, a: Marmara Adası, b: Gökçeada, c: Bozcaada, 1-Ardahan, 2-Artvin, 3-Rize, 4-Trabzon, 5-Giresun, 6-Ordu, 7-Tokat, 8-Samsun, 9-Sinop, 10-Çorum, 11-Kastamonu, 12-Zonguldak, 13-Bolu, 14-Düzce, 15-Kocaeli, 16-İstanbul, 17-Bursa, 18-Tekirdağ, 19-Kırklareli, 20-Balıkesir, 21-Edirne, 22-Çanakkale.
Fig. 4 in Pattern Of Genetic Variation Of Bottlenose Dolphins In Chinese Waters
Fig. 4. Plylogenetic reconstruction of Turisops mitochondrial control region haplotypes and some haplotypes of striped dolphin Stenella coeruleoalba and common dolphin Delphinus delphis, reconstructed using the neighbor joining algorithm with short-finned pilot whale Globicephala macrorhynchu as outgroup. Bootstrap values from 500 iterations are indicated near branches. Haplotype codes correspond to the codes in figure 1.
Fig. 1 in Pattern Of Genetic Variation Of Bottlenose Dolphins In Chinese Waters
Fig. 1. Locations where bottlenose dolphins were sampled. Numerals within the square and circle symbols represent the sample size for truncatus-type and aduncus-type, respectively. QD, Qingdao, LYG, Lianyungang, ZS, Zhoushan, XM, Xiamen, DS, Dongshan, TS, Taiwan Strait, BH, Beihai
Fig. 7 in Genetic and morphological variation of metacercariae of Microphallus piriformes (Trematoda, Microphallidae): Effects of paraxenia and geographic location
Fig. 7. Variability of metacercarial body shape within hemipopulations and infrapopulations of M. piriformes. A: Absolute and relative morphological disparity (MD) of metacercariae within hosts of the same species. B: Distribution of morphological disparity (MD) within individual snails grouped by host species and sampling location.
Fig. 4 in Genetic and morphological variation of metacercariae of Microphallus piriformes (Trematoda, Microphallidae): Effects of paraxenia and geographic location
Fig. 4. Haplotype networks, COI sequence (369 bp); TCS algorithm; dashes correspond to mutations. A: color reflects sampling location. B: color reflects host species.
Fig. 5 in Genetic and morphological variation of metacercariae of Microphallus piriformes (Trematoda, Microphallidae): Effects of paraxenia and geographic location
Fig. 5. PCA-ordination of individual M. piriformes metacercariae body shapes grouped by host species. PC1 can be interpreted as a deepness of a "waist" between locomotory and generative body parts; PC2 can be interpreted as a width of locomotory body part. B: Pairwise post-hoc comparison; significant value are shown as bold (considering Holmes correction for multiple comparison); host species: sax – L. saxatilis; obt – L. obtusata; sampling site: Kib - Barents Sea, Kiberg; Kor – White Sea, Korga-Islet; Zel – Barents Sea, Dalnie Zelentsy.
Fig. 1 in Genetic and morphological variation of metacercariae of Microphallus piriformes (Trematoda, Microphallidae): Effects of paraxenia and geographic location
Fig. 1. The map of the study region (image: TerraMetrics, map data: Google). Sample collection sites (Tromsø city, Kiberg settlement, Dalnie Zelentsy settlement, Sredny Island) are shown.
Fig. 8 in Genetic and morphological variation of metacercariae of Microphallus piriformes (Trematoda, Microphallidae): Effects of paraxenia and geographic location
Fig. 8. Body size of M. piriformes metacercariae from different host species and sampling locations. Mean centroid size and 95% confidence interval obtained via bootstrap.
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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.