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1,155 results for “loci”
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: discovery cohort meta data and parsed TCR repertoire data
<p>Meta data corresponding the the discovery cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include: </p> <p>(1) a file mapping the SNP data subject IDs to the TCR repertoire data subject IDs (gwas_id_mapping.tsv)<br> (2) a file including the PCAir PCs, self-reported ancestry, and genomic ancestry for each subject (all_pc_air.txt)<br> (3) a file including the PCAir variance explained by each PC (all_pc_air_variance.txt)<br> (3) a file including the SNP ID, chromosome, hg19 position, allele, rsid, and quality control metrics for each SNP in the SNP array (emerson_snp_rs_data.tsv)<br> (4) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (5) a file including predicted TRBD2 allele genotypes for each subject (emerson_trbd2_alleles.tsv)<br> (6) Parsed TCRB repertoire data. These raw data were first published in Emerson et. al, <em>Nature Genetics </em>2017. (emerson_parsed_tcrb.tgz)</p> <p><strong>Corresponding discovery cohort raw TCR repertoire data is available here: </strong>https: //doi.org/10.21417/B7001Z (ImmuneACCESS database)<br> <strong>Corresponding discovery cohort SNP data is available here:</strong> https: //www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001918.v1.p1 (The database of Genotypes and Phenotypes, accession number: phs001918)<br> <br> <strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits
<p>Full summary statistics for the publication "Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits". </p> <p>The files, bmi.UKBBsummary.txt and height.UKBBsummary.txt, contain tissue specific associations with body mass index (BMI) and height respectively. The suffix UKBB450k indicates results from all ~450,000 European ancestry individuals in UK Biobank. The suffix UKBB50k corresponds to results from a subset of 50,000 Europeans in the UK Biobank. The suffix PAGE corresponds to results from ~50,000 individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. </p> <p>The file PAGE_PrediXcan_associations.txt includes trait~tissue specific GReX associations for 25 traits. The first field specifies the tissue.trait.gene of the association results.</p>
Local adaptation and archaic introgression shape global diversity at human structural variant loci
<p>Supporting data associated with the manuscript "Local adaptation and archaic introgression shape global diversity at human structural variant loci". These include:</p> <ul> <li>structural variant genotypes (Paragraph; <a href="https://github.com/Illumina/paragraph">https://github.com/Illumina/paragraph</a>)</li> <li>eQTL mapping results (fastqtl permutation pass; see <a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a> for column descriptions)</li> <li>eQTL fine-mapping results (CAVIAR; see <a href="http://genetics.cs.ucla.edu/caviar/index.html">http://genetics.cs.ucla.edu/caviar/index.html</a>)</li> <li>structural variant selection scan results (Ohana; <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</li> </ul> <p>Description of files in this directory:</p> <p><strong>Structural variant genotypes</strong></p> <p><code>SVs_paragraphFormat.vcf.gz</code> - merged long-read structural variant calls</p> <p><code>SVs_1KGP_pgGTs.vcf.gz</code> - genotypes for 1000 Genomes samples in VCF format</p> <p><strong>eQTL mapping results</strong></p> <p><code>fastqtl_out.txt</code> - results from fastQTL permutation pass; see <a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a> for column descriptions</p> <p><code>caviar_out.txt</code> - results from fine-mapping SNPs and SVs at significant SV eQTL loci with CAVIAR. Description of columns:</p> <ul> <li>query_sv: SV that was a significant eQTL and underwent fine-mapping</li> <li>gene_id: gene exhibiting an expression association with the query_sv</li> <li>var_id: variant (SNV or SV) that was tested for expression association with the above gene in the fine-mapping analysis</li> <li>var_in_credible_causal_set: Boolean variable denoting whether the above variant is in the 95% credible causal set</li> <li>prob_in_pcausal_set: the amount that this variant contributes to 95% credible causal set</li> <li>causal_post_prob: the posterior probability that the variant is causal in the expression association</li> </ul> <p><strong>Structural variant selection scan results</strong></p> <p><code>chr21_pruned_50_Q.matrix</code> - admixture proportion matrix (generated by Ohana; <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</p> <p><code>chr21_pruned_50_F.matrix</code> - matrix of inferred ancestral allele frequencies (generated by Ohana)</p> <p><code>chr21_pruned_50_C.matrix</code> - matrix of ancestry component covariances (generated by Ohana) Entries of the matrix can be modified to produce "selection hypothesis" matrices where allele frequencies are allowed to vary in one ancestry component (<a href="https://github.com/jade-cheng/ohana/wiki/Population-or-ancestry-specific-selection-scan">https://github.com/jade-cheng/ohana/wiki/Population-or-ancestry-specific-selection-scan</a>).</p> <p><code>selscan_50_k8_p*.txt.gz</code> - raw output of Ohana selscan (see <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</p> <p><code>selscan_res.txt.gz</code> - Ohana selection scan results. These results have been filtered to exclude SVs that have low genotyping rates (<50% of samples), violate Hardy-Weinberg equilibrium expectations (excess of heterozygotes) in more than half of populations, or have extreme global log likelihood estimate (LLE) values. Description of columns:</p> <ul> <li>ID: SV ID</li> <li>#CHROM: SV chromosome</li> <li>POS: SV start position</li> <li>SVLEN: SV length (negative for deletions)</li> <li>step: number of steps needed to interpolate between genome-wide and selection hypothesis models</li> <li>lle_ratio: likelihood ratio statistic (LRS) of the genome-wide vs. selection hypothesis model</li> <li>global-lle: log likelihood of the genome-wide model</li> <li>local-lle: log likelihood of the selection hypothesis model</li> <li>f-pop0: inferred allele frequency in ancestry component 0</li> <li>f-pop1: inferred allele frequency in ancestry component 1</li> <li>f-pop2: inferred allele frequency in ancestry component 2</li> <li>f-pop3: inferred allele frequency in ancestry component 3</li> <li>f-pop4: inferred allele frequency in ancestry component 4</li> <li>f-pop5: inferred allele frequency in ancestry component 5</li> <li>f-pop6: inferred allele frequency in ancestry component 6</li> <li>f-pop7: inferred allele frequency in ancestry component 7</li> <li>ancestry_component: ancestry component tested by the selection hypothesis model. Note that we have added 1 to the ancestry component numbers to match the terminology used in paper (which orders the components from 1-8 rather than 0-7 for interpretability)</li> <li>snp_perc: SV's percentile in the LRS distribution for frequency-matched SNPs</li> <li>p_nominal: nominal p-value calculated from the likelihood ratio</li> <li>p_adj: adjusted p-value calculated from the likelihood ratio</li> </ul> <p> </p>
Large effect loci mediate rapid adaptation of salmon body size after river regulation
<p>Understanding the potential of natural populations to adapt to altered environments is becoming increasingly relevant in evolutionary research. Currently, our understanding of adaptation to human alteration of the environment is hampered by lack of knowledge on the genetic basis of traits, lack of time series, and little or no information on changes in optimal trait values. Here we used time series data spanning nearly a century to investigate how body mass of Atlantic salmon (<em>Salmo salar</em>) adapts to river regulation. We found that the change in body mass followed the change in waterflow, both decreasing to ~1/3 of their original values. Allele frequency changes at two loci in the regions of <em>vgll3</em> and <em>six6 </em>predicted more than 80% of the observed body mass reduction. Modelling the adaptive dynamics revealed that the population mean lagged behind its optimum before catching up ~6 salmon generations after the initial waterflow reduction. Our results demonstrate rapid adaptation mediated by large effect loci and provide insight into the temporal dynamics of evolutionary rescue following human disturbance.</p>
13 STR polymorphic loci for Aulactinia stella and Cribrinopsis albopunctata
<p>Here is a dataset of *.fsa files (the Prism Genetic Analyzer 3100, Applied Biosystems) of pool DNA PCR product of 13 STR polymorphic loci for Aulactinia stella and Cribrinopsis albopunctata.</p>
Fig. 7 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 7. Simulated and observed values of Borges et al.'s (Borges et al. 2019) δ values for simulated trait distributions on the tree in Fig. 1 for sets of traits (A, m1: m2 length; B, M1: M2 length; C, M2: M3 length) that show phylogenetic signal (red triangles) and phylogenetic retention (gray circles).
Fig. 2 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 2. Illustration of measurements taken on lower (m1–m3, A–C) and upper (M1–M3, D–F) teeth in occlusal views to calculate potentially diagnostic traits. Measurements are illustrated on specimen UMZC H5101A, Procavia capensis. Abbreviations: LE, length; MW, width of the crown at metaloph; PW width of the crown at paraloph.
Fig. 6 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 6. Overlap in potentially diagnostic trait values for upper molar loci of three example hyracoid taxa (A1, B1, Procavia capensis; A2, B2, Saghatherium bowni; A3, B3, Thyrohyrax meyeri). A. Length vs. proportional frequency, showing how a single trait, length, would be modeled in a univariate discriminant analysis using observed means and variances. Colored sections of the distributions show length values that are within 95% confidence intervals of the means of more than one tooth locus, indicating regions of ambiguous lengths. B. Length vs. relative width, showing scatter plots overlaid on 2D density diagrams showing the distribution of values for individual teeth.
Fig. 8 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 8. Overlap in potentially diagnostic trait values for a case study of isolated molars of Meroehyrax kyongoi. In contrast to Fig. 4, molar locus identifications are based on occupation of space in this scatterplot. Question marks are overlaid over two specimens whose inferred tooth position conflicts with published diagnoses. In parentheses original identification in publication.
Fig. 1 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 1. Phylogenetic tree and tooth size distribution in hyracoids (topology from Cooper et al. 2014). Taxa are time-scaled along the x-axis of the tree to reflect fossil occurrences based on the literature, with branches rescaled between these tip dates and a root age estimated at 70.1 million years. Taxa in bold text were included in analyses. Minimum monophyletic clade including taxa in bold indicates the range of the phylogenetic bracket applied for both length and width measures (base of clade indicated by black star). Minimum monophyletic clade for length measures from the literature is indicated by a white star at the base of the clade. Shapes to the right of tips indicate whether there is a significant fit with a model of ascending (increasing) tooth size down the molar row. Abbreviations: M, upper molars; m, lower molars.
Fig. 3 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 3. Distribution of values for a set of univariate, potentially locus-diagnostic traits (A, length vs. m1 length; B, trigonid width vs. talonic width) described in Fig. 2 in lower molars of a range of hyracoid species.
Fig. 5 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 5. Distribution of values for a set of univariate, potentially locus-diagnostic traits (A, length vs. M1 length; B, paraloph vs. metaloph; C, metaloph vs. length; D, paraloph vs. length) described in Fig. 2 in upper molars of a range of hyracoid species.
Fig. 4 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 4. Overlap in potentially diagnostic trait values for lower molar loci of three example hyracoid taxa (A1, B1, Procavia capensis; A2, B2, Saghatherium bowni; A3, B3, Thyrohyrax domorictus). A. Length vs. proportional frequency, showing how a single trait, length, would be modeled in a univariate discriminant analysis using observed means and variances. Colored sections of the distributions show length values that are within 95% confidence intervals of the means of more than one tooth locus, indicating regions of ambiguous lengths. B. Length vs. relative width, showing scatter plots overlaid on 2D density diagrams showing the distribution of values for individual teeth.
Fig 4 in Using mating-type loci to improve taxonomy of the Tuber indicum complex, and discovery of a new species, T. longispinosum
Fig 4. Phylogenetic relationships among Asian black truffles based on MAT1-1-1 and MAT1-2-1 sequences. The phylogram was obtained by maximum likelihood inference under the TN93 model. SH-aLRT values and Bayesian posterior probabilities are shown as ML/BPP. https://doi.org/10.1371/journal.pone.0193745.g004
Fig 3 in Using mating-type loci to improve taxonomy of the Tuber indicum complex, and discovery of a new species, T. longispinosum
Fig 3. Relative frequencies of asci with one to six ascospores per ascus for the studied taxa. https://doi.org/10.1371/journal.pone.0193745.g003
Fig 2 in Using mating-type loci to improve taxonomy of the Tuber indicum complex, and discovery of a new species, T. longispinosum
Fig 2. Ascospore spine height of four-spored asci (A) and width of spine bases (B), for the studied taxa. Different letters above boxes indicate significant differences between mean according to Tukey–Kramer honestly significant difference test (P <0.01). https://doi.org/10.1371/journal.pone.0193745.g002
Fig 6 in Using mating-type loci to improve taxonomy of the Tuber indicum complex, and discovery of a new species, T. longispinosum
Fig 6. Tuber longispinosum photographs (holotype, TFM: S17009). A. Dried ascomata (bar = 1 cm). B. Fruit bodies photographed in the field. C. Peridial warts, (bar = 3 mm). D. Asci and ascospores (bar = 30 μm). E. Ascospore (bar = 10 μm). F. Peridium in cross section (bar = 50 μm). https://doi.org/10.1371/journal.pone.0193745.g006
Fig 1 in Using mating-type loci to improve taxonomy of the Tuber indicum complex, and discovery of a new species, T. longispinosum
Fig 1. SEM images of ascospores for Asian black truffles, showing the details of ornamentation. A–C: Tuber longispinosum (A: K70, B: K209, C: K466), D–F: Tuber sp. 6 (D: K152, E: S4, F: S23), G–I: T. formosanum (G: HKAS48268 paratype, H: HKAS62628 holotype, I: HKAS79547), J–L: T. himalayense (J: HP1-3, K: MY5-1, L: SHD1-1- 1), M–O: T. indicum (M: YSH1-8, N: YR1-4, O: HD6-16). Bars = 10 μm. https://doi.org/10.1371/journal.pone.0193745.g001
Fig 7 in Using mating-type loci to improve taxonomy of the Tuber indicum complex, and discovery of a new species, T. longispinosum
Fig 7. Tuber himalayense photographs (TFM: S17015). A. Ascomata (bar = 1 cm). B. Peridial warts, (bar = 3 mm). C. Asci and ascospores (bar = 30 μm). D. Peridium and glebal tissue in cross section (bar = 50 μm). E. Ascospore (bar = 10 μm). https://doi.org/10.1371/journal.pone.0193745.g007
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).
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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)
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DANDI Archive for NWB datasets
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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.