Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Silk Road genotyping

ABSTRACT: Genotyping of individuals from 20 isolated communities along the Silk Road produced after the Marco Polo scientific expedition

INSTRUMENT(S): -

ORGANISM(S): Homo Sapiens

SUBMITTER: University of Trieste

PROVIDER: PRJEB60906 | EVA | 2023-03-27

REPOSITORIES: EVA

ACCESS DATA

Json Xml

Similar Datasets

Genotyping in Neuroblastoma Primary tumors

Project description:We performed genotyping of Neuroblastoma Primary tumors using Illumina HumanHap 550 - v1,v3,v3duo and 610 Quad genotyping beadchips.

2011-09-15 | GSE19385 | GEO

Whole-genome genotyping of human islets using Illumina infinium chips

Project description:Genomic DNA was extracted from human islets using Dneasy Blood & Tissue Kit (QIAGEN) with RNase A treatment. 200-500ng DNA was genotyped using InfiniumOmni2-5-8v Genotyping BeadChips (Illumina).DNA was isolated from human islet cells from various donors. DNA was genotyped using Illumina Infinium whole-genome genotyping array. Genotypes were called with GenomeStudio (v.2.0.4) using default settings. Genotypes that passed quality filters (missing<0.05, minor allele frequency (MAF>0.01), non-ambiguous alleles defined by AT/GC variants with MAF>40%) were exported.

2023-02-07 | GSE170763 | GEO

Genotyping human lymphoblastoid cell lines

Project description:Genotyping FATO and SATO3 human lymphoblastoid cell lines

2010-08-27 | GSE23813 | GEO

Genotyping of 4 female and 4 male Caucasians

Project description:Genotyping of eight human individuals which was used alongside RNA-seq data of the same individuals (SRA062051)

2013-11-26 | E-MTAB-1450 | biostudies-arrayexpress

Genotyping SAX1 Mouse Human Hybrid

Project description:Genotyping SAX1 Mouse Human Hybrid to identify haplotypes of chromosomes 7 and X

2010-08-27 | GSE23816 | GEO

A robust machine learning approach for missing persons cases with high genotyping errors

Project description:Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a robust machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.

2022-07-27 | GSE209804 | GEO

Genotyping of Coxiella burnetii strains collection

Project description:Genotyping based on genomic comparative hybridization of different isolates of coxiella burnetii compared to NMI reference strain

2011-12-31 | GSE31543 | GEO

Genotyping of 22 Great Danes with Ichthyosis

Project description:Genotyping of 22 Great Danes on the Illumina high density BeadChip

2015-11-03 | GSE73400 | GEO

Development of cucumber (Cucumis sativus L.) genotyping array

Project description:Genotyping arrays are tools for high throughput genotyping, which is required in genome-wide association studies (GWAS). Since the first cucumber genome draft was reported, genetic maps were constructed mainly based on simple-sequence repeats (SSRs) or on combinations of SSRs and other sequence-related amplified polymorphism (SRAP). In this study we developed the first cucumber genotyping array which consisted of 32,864 single nucleotide polymorphisms (SNPs). These markers cover the cucumber genome every 2.1Kb and have parents/F1 hybridizations as a training set. The training set was validated with Fludigm technology and had 98% concordance. The application of the genotyping array was illustrated by constructed a genetic map of 600 cM in length based on recombinant inbred lines (RIL) population of a 9930XGy14 cross of which compromise of 11564 SNPs. The markers collinearity between the genetic map and genome references of the two parents estimated as R2=0.97. Moreover, this comparison supports a translocation in the beginning of chromosome 5 that occurred in the lineage of 9930 and Gy14 as well as local variation in the recombination rate. We also used the array to investigate the local allele frequencies along the cucumber genome and found specific region with segregation distortions. We believe that the genotyping array together with the training set would be a powerful tool in applications such as quantitative-trait loci (QTL) analysis and GWAS.

2015-05-01 | GSE57294 | GEO

Development of the Restriction Site Tiling Analysis (RSTA) method: Accurate discovery and quantitative genotyping of genome-wide polymorphisms using nucleotide arrays

Project description:Here we developed a new high-throughput polymorphism detection and genotyping method based on identifying restriction cut site polymorphisms using a microarray platform. We compared the genomes of 20 individual urchins; 10 from the northern part of the species range (Boiler Bay, OR) and 10 from the southern part of the range (San Diego, CA).

2010-03-13 | GSE20857 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data