SureTypeSC - accurate genotyping of single-cell SNP array data
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ABSTRACT:
PROVIDER: EGAS00001004621 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Data from human and e coli peptides that were analyzed with a single FAIMS compensation voltage (CV) from -20 to -120. Data was used to train machine learning models to predict the FAIMS transmission profile of other peptides
2020-07-09 | MSV000085707 | MassIVE
Project description:BackgroundChina has the richest local chicken breeding resources in the world and is the world's second largest producer of meat-type chickens. Development of a moderate-density SNP array for genetic analysis of chickens and breeding of meat-type chickens taking utility of those resources is urgently needed for conventional farms, breeding industry, and research areas.ResultsEight representative local breeds or commercial broiler lines with 3 pools of 48 individuals within each breed/line were sequenced and supplied the major SNPs resource. There were 7.09 million - 9.41 million SNPs detected in each breed/line. After filtering using multiple criteria such as preferred incorporation of trait-related SNPs and uniformity of distribution across the genome, 52.18 K SNPs were selected in the final array. It consists of: (i) 19.22 K SNPs from the genomes of yellow-feathered, cyan-shank partridge and white-feathered chickens; (ii) 5.98 K SNPs related to economic traits from the Illumina 60 K SNP Bead Chip, which were found as significant associated SNPs with 15 traits in a Beijing-You crossed Cobb F2 resource population by genome-wide association study analysis; (iii) 7.63 K SNPs from 861 candidate genes of economic traits; (iv) the 0.94 K SNPs related to residual feed intake; and (v) 18.41 K from chicken SNPdb. The polymorphisms of 9 extra local breeds and 3 commercial lines were examined with this array, and 40 K - 47 K SNPs were polymorphic (with minor allele frequency > 0.05) in those breeds. The MDS result showed that those breeds can be clearly distinguished by this newly developed genotyping array.ConclusionsWe successfully developed a 55K genotyping array by using SNPs segregated from typical local breeds and commercial lines. Compared to the existing Affy 600 K and Illumina 60 K arrays, there were 21,41 K new SNPs included on our Affy 55K array. The results of the 55K genotyping data can therefore be imputed to high-density SNPs genotyping data. The array offers a wide range of potential applications such as genomic selection breeding, GWAS of interested traits, and investigation of diversity of different chicken breeds.
| S-EPMC6532155 | biostudies-literature
Project description:BACKGROUND:In forest trees, genetic markers have been used to understand the genetic architecture of natural populations, identify quantitative trait loci, infer gene function, and enhance tree breeding. Recently, new, efficient technologies for genotyping thousands to millions of single nucleotide polymorphisms (SNPs) have finally made large-scale use of genetic markers widely available. These methods will be exceedingly valuable for improving tree breeding and understanding the ecological genetics of Douglas-fir, one of the most economically and ecologically important trees in the world. RESULTS:We designed SNP assays for 55,766 potential SNPs that were discovered from previous transcriptome sequencing projects. We tested the array on ~ 2300 related and unrelated coastal Douglas-fir trees (Pseudotsuga menziesii var. menziesii) from Oregon and Washington, and 13 trees of interior Douglas-fir (P. menziesii var. glauca). As many as ~ 28 K SNPs were reliably genotyped and polymorphic, depending on the selected SNP call rate. To increase the number of SNPs and improve genome coverage, we developed protocols to 'rescue' SNPs that did not pass the default Affymetrix quality control criteria (e.g., 97% SNP call rate). Lowering the SNP call rate threshold from 97 to 60% increased the number of successful SNPs from 20,669 to 28,094. We used a subset of 395 unrelated trees to calculate SNP population genetic statistics for coastal Douglas-fir. Over a range of call rate thresholds (97 to 60%), the median call rate for SNPs in Hardy-Weinberg equilibrium ranged from 99.2 to 99.7%, and the median minor allele frequency ranged from 0.198 to 0.233. The successful SNPs also worked well on interior Douglas-fir. CONCLUSIONS:Based on the original transcriptome assemblies and comparisons to version 1.0 of the Douglas-fir reference genome, we conclude that these SNPs can be used to genotype about 10 K to 15 K loci. The Axiom genotyping array will serve as an excellent foundation for studying the population genomics of Douglas-fir and for implementing genomic selection. We are currently using the array to construct a linkage map and test genomic selection in a three-generation breeding program for coastal Douglas-fir.
| S-EPMC6942338 | biostudies-literature
Project description:BACKGROUND: Chromosomal imbalances are a major cause of developmental defects as well as cancer and often constitute the key in identification of novel disease related genes. Classical cytogenetic methods are limited in resolution and dependent on highly skilled labour, while methods with higher resolution, based on molecular cytogenetics approaches such as matrix CGH, are not widely available. METHODS: We have developed and evaluated a method we term "molecular karyotyping", using readily available and easy to handle oligonucleotide arrays originally designed for parallel genomewide analysis of over 10,000 SNPs. We show that we can easily and reliably detect unbalanced chromosomal aberrations of various sizes from as little as 250 ng of DNA on a single microarray, based on fluorescence intensity information from clusters of SNPs. RESULTS: We determined the resolution of this method through analysis of 20 trios with 21 previously confirmed subtle aberrations sizing between 0.2 and 13 Mb. Duplications and deletions of at least 5 Mb in size were reliably detectable, but detection of smaller aberrations was dependent on the number of SNPs they contained, thus seven of 10 different deletions analysed, with sizes ranging from 0.2 to 3.7 Mb, were not detectable due to insufficient SNP densitiy in the respective region. CONCLUSIONS: Deduction of reliable cut off levels for array peaks in our series of well characterised patients allows the use of the GeneChip Mapping 10K SNP array for performing rapid molecular karyotyping from small amounts of DNA for the detection of even subtle deletions and duplications with high sensitivity and specificity.
| S-EPMC1735639 | biostudies-other
Project description:Maintaining genetic diversity is a crucial component in conserving threatened species. For the iconic Australian koala, there is little genetic information on wild populations that is not either skewed by biased sampling methods (e.g., sampling effort skewed toward urban areas) or of limited usefulness due to low numbers of microsatellites used. The ability to genotype DNA extracted from koala scats using next-generation sequencing technology will not only help resolve location sample bias but also improve the accuracy and scope of genetic analyses (e.g., neutral vs. adaptive genetic diversity, inbreeding, and effective population size). Here, we present the successful SNP genotyping (1272 SNP loci) of koala DNA extracted from scat, using a proprietary DArTseq™ protocol. We compare genotype results from two-day-old scat DNA and 14-day-old scat DNA to a blood DNA template, to test accuracy of scat genotyping. We find that DNA from fresher scat results in fewer loci with missing information than DNA from older scat; however, 14-day-old scat can still provide useful genetic information, depending on the research question. We also find that a subset of 209 conserved loci can accurately identify individual koalas, even from older scat samples. In addition, we find that DNA sequences identified from scat samples through the DArTseq™ process can provide genetic identification of koala diet species, bacterial and viral pathogens, and parasitic organisms.
| S-EPMC5869377 | biostudies-literature
Project description:Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96-98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ?20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses.
| S-EPMC4498655 | biostudies-literature
Project description:53 tumors used in the clinical training and validation of a CUP machine learning classifier using methylation profiling
2025-01-15 | GSE249711 | GEO
Project description:53 tumors used in the clinical training and validation of a CUP machine learning classifier using methylation profiling
2025-01-15 | GSE249688 | GEO
Project description:We here systematically studied the interaction network of small intestine crypt cells.We first created a reference of cell types based on single-cell mRNA sequencing, which we used to create virtual doublets and triplets. These virtual combinations were then used to train a machine learning algorithm (random forests) in order to infer the cell types present in the actual picked doublets or triplets. We then counted the number of interactions in between defferent cell types, and compared it to label permution simulation to identify interactions that happens more or less than expected by chance.
2018-04-16 | GSE103516 | GEO
Project description:BackgroundHigh density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species.ResultsWe report here the development of a 600 K Affymetrix® Axiom® HD genotyping array designed using SNPs segregating in a wide variety of chicken populations. In order to generate a large catalogue of segregating SNPs, we re-sequenced 243 chickens from 24 chicken lines derived from diverse sources (experimental, commercial broiler and layer lines) by pooling 10-15 samples within each line. About 139 million (M) putative SNPs were detected by mapping sequence reads to the new reference genome (Gallus_gallus_4.0) of which ~78 M appeared to be segregating in different lines. Using criteria such as high SNP-quality score, acceptable design scores predicting high conversion performance in the final array and uniformity of distribution across the genome, we selected ~1.8 M SNPs for validation through genotyping on an independent set of samples (n = 282). About 64% of the SNPs were polymorphic with high call rates (>98%), good cluster separation and stable Mendelian inheritance. Polymorphic SNPs were further analysed for their population characteristics and genomic effects. SNPs with extreme breach of Hardy-Weinberg equilibrium (P < 0.00001) were excluded from the panel. The final array, designed on the basis of these analyses, consists of 580,954 SNPs and includes 21,534 coding variants. SNPs were selected to achieve an essentially uniform distribution based on genetic map distance for both broiler and layer lines. Due to a lower extent of LD in broilers compared to layers, as reported in previous studies, the ratio of broiler and layer SNPs in the array was kept as 3:2. The final panel was shown to genotype a wide range of samples including broilers and layers with over 100 K to 450 K informative SNPs per line. A principal component analysis was used to demonstrate the ability of the array to detect the expected population structure which is an important pre-investigation step for many genome-wide analyses.ConclusionsThis Affymetrix® Axiom® array is the first SNP genotyping array for chicken that has been made commercially available to the public as a product. This array is expected to find widespread usage both in research and commercial application such as in genomic selection, genome-wide association studies, selection signature analyses, fine mapping of QTLs and detection of copy number variants.
| S-EPMC3598943 | biostudies-literature