Batch2_Genotypes_Raw
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ABSTRACT:
PROVIDER: EGAD00010002124 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Genotypes generated for Puno cohort Batch 1. Case (PRE) and control (PUN) families were recruited in hospital, and additional unrelated controls were recruited in university (UNA). Raw genotypes no QC.
| EGAD00010002126 | EGA
Project description:We performed quantitative proteomic profiling of 786 plasma samples from COVID-19 inpatients, treated at two different hospitals (Charité – Universitätsmedizin Berlin and University Hospital of Innsbruck). Sampling was performed at multiple time points throughout the course of the disease, to create a time-resolved map of COVID-19 progression. Full DIA-NN analysis reports are provided, as well as raw files for the QC runs.
2021-07-19 | PXD025752 | Pride
Project description:Data from the VLA lyssavirus genotyping microarray. The array platform for this data is GEO accession GPL8066, and consists of 624 oligos representing two viral families. The data set itself consists of 14 arrays, 7 hybridised with RNA from mice brains infected with 7 genotypes of lyssaviruses, 1 hybridised with RNA from normal mouse brain, and 6 hybridised with RNA from coded samples consisting of infected mouse brains or control mouse brains. Keywords: Lyssavirus genotyping microarray
2009-06-09 | GSE14705 | GEO
Project description:Fetal health is dependent upon the epigenetic-based regulation of gene expression in placenta. Genomic imprinting is an epigenetic phenomenon common to placenta and refers to the monoallelic expression of a gene in a parental-specific manner. We aimed to detect novel imprinted genes in human placenta by applying whole transcriptome RNA-sequencing and genotyping of coding variants. Ten family trios with healthy spontaneous single term pregnancy were recruited. Parental and child DNA genotypes were analysed using exome SNP genotyping microarrays, revealing the inheritance of parental alleles. Total RNA was extracted from placental tissue for whole transcriptome analysis. The imprinted genes showed consistent expression from either parental allele as demonstrated by the SNP content of sequenced transcripts. We found seven novel imprinted genes (ABP1, BCLAF1, IFI30, LGALS8, LGALS14, PAPPA2 and SPTLC3) and confirmed five known imprinted genes (AIM1, PEG10, RHOBTB3, ZFAT and ZFAT-AS1). The main functions of the proteins encoded by the imprinted genes can be grouped as being involved in: i) cellular apoptosis and tissue development; ii) regulating inflammation and modulating the immune system; iii) facilitating metabolic processes and iv) regulating the cell cycle. Ten family trios (mother, father, child) were analysed using SNP genotyping. Raw data contains additional two samples that were not used.
2014-09-01 | E-GEOD-56685 | biostudies-arrayexpress
Project description:Gene expression profiling of Monocytes (CD14+CD16-) from human peripheral blood mononuclear cells (PBMCs). PBMCs were isolated from healthy individuals from the Boston area. Over the course of 18 months, fresh blood samples from healthy participants were collected following a rigorous, standardized set of procedures (SOP). 15ml of blood was collected and used to isolate peripheral blood mononuclear cells (PBMCs). From the PBMCS, cell population of interest, CD14+CD16-, underwent a two-step sorting strategy in order to achieve cell purity >99%. mRNAs from these cells were profiled on Affymetrix GeneChip Human Gene ST 1.0 microarrays. Raw data CEL files were processed using the Robust Multichip Average (RMA) algorithm in Affymetrix PowerTools. To account for non-genetic factors such as batch effects, age, gender, and technical artifacts in gene expression data, we used Principal Component Analysis (PCA). PCs were estimated separately from the gene expression matrix for each population. The optimal numbers of PCs for association analysis were determined based on the PC that resulted in maximum number of cis-eQTLs. This procedure identified 20, 10, and 14 PCs in EU, EA and AA monocytes, respectively. GSE56034_GSM.ImmVarCD14.*.PC*.txt supplementary files: For each population EU, AA, and EA, individual PC-corrected matrices for samples that have passed both Expression QC and Genotyping QC. These are the same expression matrices that were used for the eQTL association analysis.
2014-05-02 | E-GEOD-56034 | biostudies-arrayexpress
Project description:In a study to elucidate the genetic defects in patients with X-linked intellectual disability (XLID) we performed X chromosome-specific BAC-array-CGH and identified 0.33 to 1.0 Mb nonrecurrent copy number gains at Xp11.22 in affected males of unrelated XLID families. All aberrations segregate with the disease in the families and the carrier mothers show a nonrandom X-inactivation. Affected males suffered from mild to moderate ID. Tiling Xp11.22 region-specific oligo-array (ChrX:52.50 - 54.50 Mb) revealed that all aberrations had different start and stop sites. The twofold copy number gain included up to 20 genes but only the HUWE1 gene is located in the minimal common region of overlap in these families. Moreover, expression analysis revealed about twofold increased HUWE1 mRNA levels in affected patients when compared to control individuals. Breakpoint analysis revealed Non-homologous end-joining (4 cases), serial replication slippage (1 case) and non-allelic homologous recmbination (one case) as the potential recombination mechanism.
2012-09-03 | GSE32945 | GEO
Project description:Gene expression profiling of CD4 T-Cells (CD4+CD62L+) from human peripheral blood mononuclear cells (PBMCs). PBMCs were isolated from healthy individuals from the Boston area. Over the course of 18 months, fresh blood samples from healthy participants were collected following a rigorous, standardized set of procedures (SOP). 15ml of blood was collected and used to isolate peripheral blood mononuclear cells (PBMCs). From the PBMCS, cell population of interest, CD4+CD62L+, underwent a two-step sorting strategy in order to achieve cell purity >99%. mRNAs from these cells were profiled on Affymetrix GeneChip Human Gene ST 1.0 microarrays. Raw data CEL files were processed using the Robust Multichip Average (RMA) algorithm in Affymetrix PowerTools. To account for non-genetic factors such as batch effects, age, gender, and technical artifacts in gene expression data, we used Principal Component Analysis (PCA). PCs were estimated separately from the gene expression matrix for each population and cell type. The optimal numbers of PCs for association analysis were determined based on the PC that resulted in maximum number of cis-eQTLs. This procedure identified 20, 12 and 12 PCs in EU, EA and AA CD4+ T-cells, respectively. GSE56033_GSM.ImmVarCD4.*.PC*.txt supplementary files: For each population EU, AA, and EA, individual PC-corrected matrices for samples that have passed both Expression QC and Genotyping QC. These are the same expression matrices that were used for the eQTL association analysis.
2014-05-02 | E-GEOD-56033 | biostudies-arrayexpress
Project description:To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.
2022-03-01 | MTBLS719 | MetaboLights
Project description:The presence of a coding variant affecting plasma high density lipoprotein cholesterol (HDLC) levels was evaluated in subjects with elevated plasma levels of HDLC. 18 DNA samples from subjects with high plasma HDLC levels were analyzed. The samples were analyzed using the exome chip in three separate phases, with different samples in each phase. The same genotyping platform was used for each of the phases of the experiment under the same experimental conditions. Because different samples were run in different phases, raw intensity data was collected separately for each phase and genotypes were called separately for each phase. Raw intensity data is provided thus separately for the subjects by phase of genotyping. The experimental phase of each subject is also provided.
2015-12-17 | E-GEOD-76065 | biostudies-arrayexpress
Project description:Data from the VLA lyssavirus genotyping microarray. The array platform for this data is GEO accession GPL8066, and consists of 624 oligos representing two viral families. The data set itself consists of 14 arrays, 7 hybridised with RNA from mice brains infected with 7 genotypes of lyssaviruses, 1 hybridised with RNA from normal mouse brain, and 6 hybridised with RNA from coded samples consisting of infected mouse brains or control mouse brains. Keywords: Lyssavirus genotyping microarray Data from the VLA lyssavirus genotyping microarray. The array platform for this data is GEO accession GPL8066, and consists of 624 oligos representing two viral families. The data set itself consists of 14 arrays, 7 hybridised with RNA from mice brains infected with 7 genotypes of lyssaviruses, 1 hybridised with RNA from normal mouse brain, and 6 hybridised with RNA from coded samples consisting of infected mouse brains or control mouse brains. Statistical analysis of the data was done with DetectiV software (Watson et al., 2007). The median and array methods of normalization were used in the statistical analysis of the results. In the median method, DetectiV software calculates the mean fluorescence for each set of probes and normalised against background fluorescence of all probes, assuming that most probes are not hybridized. The array method utilizes an entire control array, e.g. RNA from a known uninfected animal, as the negative control and all probe values are divided by their respective elements from the control array.
2010-05-18 | E-GEOD-14705 | biostudies-arrayexpress