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10 results for “Genotype Clustering”
Model-based analysis of tuberculosis genotype clusters in the United States reveals high degree of heterogeneity in transmission, and state-level differences across California, Florida, New York, and Texas.
<p>Data and codes for the publication</p>
Diseasome - Finding disease association based on Phenotypic and Genotypic clustering
<p>The final processed phenotypic clustered data (output.zip) from Orphanet is also added along side the genotypic clustering data.</p>
Data from: Stepwise Threshold Clustering: a new method for genotyping MHC loci using next-generation sequencing technology
Genes of the vertebrate major histocompatibility complex (MHC) are of great interest to biologists because of their important role in immunity and disease, and their extremely high levels of genetic diversity. Next generation sequencing (NGS) technologies are quickly becoming the method of choice for high-throughput genotyping of multi-locus templates like MHC in non-model organisms. Previous approaches to genotyping MHC genes using NGS technologies suffer from two problems: 1) a "gray zone" where low frequency alleles and high frequency artifacts can be difficult to disentangle and 2) a similar sequence problem, where very similar alleles can be difficult to distinguish as two distinct alleles. Here were present a new method for genotyping MHC loci – Stepwise Threshold Clustering (STC) – that addresses these problems by taking full advantage of the increase in sequence data provided by NGS technologies. Unlike previous approaches for genotyping MHC with NGS data that attempt to classify individual sequences as alleles or artifacts, STC uses a quasi-Dirichlet clustering algorithm to cluster similar sequences at increasing levels of sequence similarity. By applying frequency and similarity based criteria to clusters rather than individual sequences, STC is able to successfully identify clusters of sequences that correspond to individual or similar alleles present in the genomes of individual samples. Furthermore, STC does not require duplicate runs of all samples, increasing the number of samples that can be genotyped in a given project. We show how the STC method works using a single sample library. We then apply STC to 295 threespine stickleback (Gasterosteus aculeatus) samples from four populations and show that neighboring populations differ significantly in MHC allele pools. We show that STC is a reliable, accurate, efficient, and flexible method for genotyping MHC that will be of use to biologists interested in a variety of downstream applications.
Microsatellite genotypes, cluster membership and metadata of Central European wolves (Canis lupus)
<p class="Normalny1">Local extinction and recolonization events can shape genetic structure of subdivided animal populations. The gray wolf (<i>Canis lupus</i>) was extirpated from most of Europe, but recently recolonized big part of its historical range. An exceptionally dynamic expansion of wolf population is observed in the western part of the Great European Plain. Nonetheless, genetic consequences of this process have not yet been fully understood. We aimed to assess genetic diversity of this recently established wolf population in Western Poland (WPL), determine its origin and provide novel data regarding the population genetic structure of the grey wolf in Central Europe. We utilized both spatially explicit and non-explicit Bayesian clustering approaches, as well as a model-independent, multivariate method DAPC, to infer genetic structure in large dataset of wolf microsatellite genotypes. To put the patterns observed in studied population into a broader biogeographic context we also analyzed a mtDNA control region fragment widely used in previous studies.</p> <p>In comparison to a source population, we found slightly reduced allelic richness and heterozygosity in the newly recolonized areas west of the Vistula river. We discovered relatively strong west-east structuring in lowland wolves, probably reflecting founder-flush and allele surfing during range expansion, resulting in clear distinction of WPL, eastern lowland and Carpathian genetic groups. Interestingly, wolves from recently recolonized mountainous areas (Sudetes Mts, SW Poland) clustered together with lowland, but not Carpathian wolf populations. We also identified an area in Central Poland that seems to be a melting pot of western, lowland eastern and Carpathian wolves. We conclude that the process of dynamic recolonization of Central European lowlands lead to the formation of a new, genetically distinct wolf population. Together with the settlement and establishment of packs in mountains by lowland wolves and vice versa, it suggests that demographic dynamics and possibly anthropogenic barriers rather than ecological factors (e.g. natal habitat-biased dispersal patterns) shape the current wolf gene<span>tic structure in Central Europe.</span></p>
Figure 4. Individual multilocus genotype clustering analysis for Podarcis carbonelli. A in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 4. Individual multilocus genotype clustering analysis for Podarcis carbonelli. A, inferred population structure from the number of clusters (K) = 2 to 5. These plots were obtained from the runs producing the highest values of Ln probability for each value of K, assuming correlated allele frequencies. In these plots, each individual is represented by a column divided into K segments, the size of each corresponding to the individual's estimated membership fraction in each of the K clusters. See Table 1 for locality name abbreviations. B, variation of the value of DK with the number of clusters, following Evanno et al. (2005). C, pie charts representing the mean proportion of membership for K = 4 (chosen by the previous method) for each locality.
Figure 3. Individual multilocus genotype clustering analysis for Podarcis bocagei. A in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 3. Individual multilocus genotype clustering analysis for Podarcis bocagei. A, inferred population structure from the number of clusters (K) = 2 to 5. These plots were obtained from the runs producing the highest values of Ln probability for each value of K, assuming correlated allele frequencies. In these plots, each individual is represented by a column divided into K segments, the size of each corresponding to the individual's estimated membership fraction in each of the K clusters. See Table 1 for locality name abbreviations. B, variation of the value of DK with the number of clusters, following Evanno et al. (2005). C, pie charts representing the mean proportion of membership for K = 3 and 5 (chosen by the previous method) for each locality.
Data from: Stepwise Threshold Clustering: a new method for genotyping MHC loci using next-generation sequencing technology
Open the record for dataset details and reuse information.
Microsatellite genotypes, cluster membership and metadata of Central European wolves (Canis lupus)
Open the record for dataset details and reuse information.
Single cell- Cluster-RNA seq and Spatial Transcriptomics from the trunk of mouse embryos of different genotypes (wild type, Pax2-GFP, Pax2-GFP; Gata3 knockout) and developmental stages (E8.75, E9.5, E
GEO Series GSE160137. Mus musculus. 4 samples. Type: Other; Expression profiling by high throughput sequencing.
Single cell- Cluster-RNA seq and Spatial Transcriptomics from the trunk of mouse embryos of different genotypes (wild type, Pax2-GFP, Pax2-GFP; Gata3 knockout) and developmental stages (E8.75, E9.5, E
GEO Series GSE160136. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
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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.