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321 results for “genetic markers”
Supplementary dataset to publication: Oxford nanopore technologies - a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella, Thomas et al 2023
<p>Bacteria of the genus <em>Salmonella</em> pose a major risk to livestock, the food economy, and public health. <em>Salmonella</em> infections are one of the leading causes of food poisoning. The identification of serovars of <em>Salmonella</em> achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by <em>in silico</em> serotyping has been established as an alternative method for serotyping and the detection of genetic markers for <em>Salmonella</em>. Until now, WGS data generated with Illumina sequencing are used to validate <em>in silico</em> serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 <em>Salmonella</em> strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the <em>in silico</em> serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, <em>in silico</em> serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for <em>Salmonella in silico</em> serotyping and genetic marker detection.</p>
Supplementary data: Agro-morphological and molecular characterization reveal deep insights in promising genetic diversity and marker-trait associations in Fagopyrum esculentum and F. tataricum
<p>Our study focuses on the global/European buckwheat germplasm collected as part of the ECOBREDD project. The potential of this highly diverse collection for organic buckwheat breeding was evaluated at two complementary levels: phenotypic and genetic. Here, we characterized the phenotypic and genetic diversity of a global collection of the two cultivated buckwheat species <em>Fagopyrum esculentum</em> and <em>F. tataricum</em> (190 and 51 accessions, respectively) using 37 agro-morphological traits and 24 SSR markers (Simple Sequence Repeats) (see publication and info sheet of the data).</p>
LA1141 × OH8245 inbred backcross (IBC) single nucleotide polymorphism (SNP) markers for genetic studies
<p>The LA1141 × OH8245 157 polymorphic SNP markers from an optimized tomato panel Sim et al., 2012 were used for linkage map construction in the BC<sub>2</sub>S<sub>3</sub> IBC and composite interval mapping QTL analysis. Genetic map position and physical position corresponding to Sl4.0 (Hosmani et al., 2019), and flanking sequences are provided.</p>
Three systems of molecular markers reveal genetic differences between varieties sabina and balkanensis in the Juniperus sabina L. range
<p>Genotypes of 94 Juniperus sabina samples from 14 populations at SNP (Jsabina_SNPs.txt) and SilicoDArT (Jsabina_SilicoDArTs.txt) loci investigated using the DArTseq technology developed by Diversity Array Technology Pty Ltd (DArT, Canberra, ACT, Australia)</p>
Genetic diversity, population structure, and linkage disequilibrium among tropical quality protein maize (QPM) lines assessed with high-density SNP markers
<p>The study of genetic diversity (GD), population structure, and linkage disequilibrium (LD) provides a better understanding of the genetic relationships between individuals in a population which can be utilized in crop research and improvement. Genotyping-by-sequencing (GBS) was used to detect and genotype single nucleotide polymorphisms (SNPs) in a collection of 74 quality protein maize (QPM) lines and further to characterize their genetic diversity, population structure, and linkage disequilibrium. A total of 235,214 high-quality SNPs were used for different genetic analyses except for structure analysis where 11,950 SNPs were used. Analysis of molecular variance (AMOVA) based on these SNPs revealed high genetic heterozygosity among the five populations with 1% of the total genetic variation present among the subpopulations and 99% of the variation among individuals within the populations. Population structure analysis using Bayesian-based clustering revealed that the 74 lines could be clustered into four groups. However, neighbor-joining trees indicate the lines are grouped into three major clusters. Further analysis using principal component analyses (PCA) clustered the genotypes into five groups which are concordant with the groups based on pedigree information. Higher genetic diversity was detected in population 1 with a GD value of 0.484 and the lowest in population 5 (0.396) and overall, with a mean of 0.434. The LD pattern in the quality protein maize was investigated and we observed a relatively rapid LD decay of 3.53kb and 10.66kb at r<sup>2</sup> =0.2 and r<sup>2</sup>= 0.1, respectively. Our findings provide important information for future Linkage mapping studies, genome-wide association analyses, and marker-assisted selective breeding of maize as well as genomic prediction-based selection in tropical germplasm.</p>
New genetic markers for Sapotaceae phylogenomics: more than 600 nuclear genes applicable from family to population levels
<p>Some tropical plant families, such as the Sapotaceae, have a complex taxonomy, which can be resolved using Next Generation Sequencing (NGS). For most groups however, methodological protocols are still missing. Here we identified 531 monocopy genes and 227 Short tandem repeats (STR) markers and tested them on Sapotaceae using target capture and NGS. The probes were designed using two genome skimming samples from<em>Capurodendron delphinense</em> and <em>Bemangidia lowryi</em>, both from the Tseboneae tribe, as well as the published <em>Manilkara zapota</em> transcriptome from the Sapotoideae tribe. We combined our probes with 261 additional ones previously published and designed for the entire angiosperm group. On a total of 792 low-copy genes, 638 showed no signs of paralogy and were used to build a phylogeny of the family with 231 individuals from all main lineages. A highly supported topology was obtained at high taxonomic ranks but also at the species level. This phylogeny revealed the existence of more than 20 putative new species. Single nucleotide polymorphisms (SNPs) extracted from the 638 genes were able to distinguish lineages within a species complex and to highlight geographical structuration. STR were recovered efficiently for the species used as reference (<em>C. delphinense</em>) but the recovery rate decreased dramatically with the phylogenetic distance to the focal species. All together, the new loci will help reaching a sound taxonomic understanding of the family Sapotaceae for which many circumscriptions and relationships are still debated, at the species, genus and tribe levels.</p>
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).
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).
Fig. 1 in Grouping and genetic diversity of different watermelon ecotypes based on agro-morphological traits and ISSR marker
Fig. 1. Grouping the watermelon ecotypes based on agro-morphological traits using UPGMA method. The symbols for the ecotypes are presented in Table 1.
Figure 5 in Identification of SCAR markers for genetic authentication of Dendrobium nobile Lindl.
Figure 5. The cloned nucleotide information by Sanger-sequencing.A. The sequences of clone M9-21 with 514bp [The GenBank accession number: MZ417502]. B. The sequences of clone M19-45 with 795bp [The GenBank accession number: MZ484089]. C. The sequences of clone N1-12 with 761bp [The GenBank accession number: MZ417504]. D. The sequences of clone N2-11 with 718bp [The GenBank accession number: MZ417505].
Figure 4 in Identification of SCAR markers for genetic authentication of Dendrobium nobile Lindl.
Figure 4. Clone identification of clones M9-21 (A), M19-45 (B), N1-12 & N2-11 (C) by without (lanes 1) or with (lanes 2) EcoRI digestion. The black arrows represent desired PCR product or specific insert bands in different clones. Lane M indicates the DNA molecular weight marker DL2000 with the fragment size (bp).
Figure 3 in Identification of SCAR markers for genetic authentication of Dendrobium nobile Lindl.
Figure 3. Identification of positive clone M19-45, M9-21 (A), and N1-12, N2-11 (B) by PCR amplification with vector T7/sp6 primers.
Figure 2 in Identification of SCAR markers for genetic authentication of Dendrobium nobile Lindl.
Figure 2. Improved RAPD amplification from DNA samples of D.nobile (listed in Table 1) using different RAPD primers. A. primers M9, M9-21; B. primers M19, M19-45; C. primers N1, N1-12; D. primers N2, N2-11.
Figure 1 in Identification of SCAR markers for genetic authentication of Dendrobium nobile Lindl.
Figure 1. The localities of samples D. nobile cultivars from different regions in China. Spots in black indicate cities and lines in light blue indicate the Yellow River (up) and the Yangtze River (down).
Figure 6 in Identification of SCAR markers for genetic authentication of Dendrobium nobile Lindl.
Figure 6. Development of stable RAPD-SCAR markers for M19-45 (A), M9-21 (B), N1-12 (C), N2-11 (D). Lane 1-10 is D. nobile, 11-21 is D. officinale Kimura et Migo,Lycium barbarum L.,Litchi chinensis Sonn., Ginkgo biloba L., Canarium album (Lour.) Raeusch., Lonicera japonica Thunb., Mentha haplocalyx Briq., Penthorum chinense Pursh, Angelica sinensis, Gardenia jasminoides, Ganoderma lucidum (Leyss. ex Fr.) Karst.. Lane M indicates the DNA molecular weight marker DL2000.
Figure 4 in Analysis of the genetic diversity of Dragon fruit based on ISSR markers in Colombia
Figure 4. UPGMA dendrogram prepared from the genetic distances (Nei and Li, 1979) of the pitahaya populations.
Figure 3 in Analysis of the genetic diversity of Dragon fruit based on ISSR markers in Colombia
Figure 3. Population genetic structure of Pitahaya for each sample analyzed, estimated using the Structure software.
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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.