Ontology highlight
ABSTRACT:
PROVIDER: PRJNA128055 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Microarray data was acquired from human primary monocyte-derived macrophages in order to test the validity of a graphical software package (z-score outlier detection (ZODET)) Complex human diseases can show significant heterogeneity between patients with the same phenotypic disorder. An outlier detection strategy was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance. Here we describe a graphical software package (z-score outlier detection (ZODET)) that enables identification and visualisation of gross abnormalities in gene expression (outliers) in individuals, using whole genome microarray data. Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual test subjects. We compared the potential of ZODET to detect outlier genes in gene expression datasets with a previously described statistical method, gene tissue index (GTI), using a simulated expression dataset and a publicly available monocyte-derived macrophage microarray dataset. Taken together, these results support ZODET as a novel approach to identify outlier genes of potential pathogenic relevance in complex human diseases. The algorithm is implemented using R packages and java.
2014-11-05 | GSE51256 | GEO
Project description:Microarray data was acquired from human primary monocyte-derived macrophages in order to test the validity of a graphical software package (z-score outlier detection (ZODET)) Complex human diseases can show significant heterogeneity between patients with the same phenotypic disorder. An outlier detection strategy was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance. Here we describe a graphical software package (z-score outlier detection (ZODET)) that enables identification and visualisation of gross abnormalities in gene expression (outliers) in individuals, using whole genome microarray data. Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual test subjects. We compared the potential of ZODET to detect outlier genes in gene expression datasets with a previously described statistical method, gene tissue index (GTI), using a simulated expression dataset and a publicly available monocyte-derived macrophage microarray dataset. Taken together, these results support ZODET as a novel approach to identify outlier genes of potential pathogenic relevance in complex human diseases. The algorithm is implemented using R packages and java. 40 volunteer were bled and primary monocyte derived macrophages were cultured. These samples were then randomised into two equal sized groups (control - A1-20 and experimental - B1-20) and run on the ZODET software.
2014-11-05 | E-GEOD-51256 | biostudies-arrayexpress
Project description:Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) has been instrumental to our current view of chromatin structure and function. It allows genome-wide mapping of histone marks, which demarcate biologically relevant domains. However, ChIP-seq is an ensemble measurement reporting the average occupancy of individual marks in a cell population. Consequently, our understanding of the combinatorial nature of chromatin states relies almost exclusively on correlation between the genomic distributions of individual marks. Here, we report the development of Combinatorial-iChIP to determine the genome-wide co-occurrence of histone marks at single nucleosome resolution. By comparing to null model, we show that certain combinations of overlapping marks (H3K36me3 and H3K79me3) co-occur more frequently than expected by chance, while others (H3K4me3 and H3K36me3) do not, reflecting differences in the underlying chromatin pathways. We further use combinatorial-iChIP to illuminate aspects of the Set2-RPD3S pathway. This approach promises to improve our understanding of the combinatorial complexity of chromatin. Combinatorial iChIP in yeast.
2016-08-02 | E-GEOD-84240 | biostudies-arrayexpress
Project description:DNA methylation alterations have similar patterns in normal aging tissue and in cancer. In this study, we investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. Outlier phenotype is identified by unsupervised anomaly detection algorithms and is defined by individuals who have normal tissue age-dependent DNA methylation levels that vary dramatically from the population mean. To identify age-dependent DNA methylation sites, we generated DNA methylation sequencing data for 29 purified normal adjacent human breast epithelia (age range 33-82 years old) using Digital Restriction Enzyme Analysis of Methylation (DREAM). Next, we validated the age-related sites in publicly available DNA methylation (450K array) of 97 normal adjacent TCGA samples. We found that hypermethylation in normal breast tissue is the best predictor of hypermethylation in cancer. Using unsupervised anomaly detection approaches, we found that about 10% of the individuals (39 /427) were outliers for DNA methylation from 6 publicly available DNA methylation datasets (GSE88883, GSE74214, GSE101961, GSE69914(normal), GSE69914(normal-adjacent), TCGA (Firehose Legacy)). We also found that there were significantly more outlier samples in normal-adjacent to cancer (24/139, 17.3%) then in normal samples (15/228, 5.2%). Additionally, we found significant differences between predicted ages based on DNA methylation and the chronological ages among outliers and not-outliers. Additionally, we found that accelerated outliers (older predicted age) were more frequent in normal-adjacent to cancer (14/17, 82%) compared to normal samples from individuals without cancer (3/17, 18%). Furthermore, in matched samples, the epigenome of the outliers in the pre-malignant tissue was as severely altered as in cancer.
2021-05-26 | GSE160233 | GEO
Project description:Passive transfer studies in humans clearly demonstrated the protective role of IgG antibodies against malaria. Identifying the precise parasite antigens that mediate immunity is essential for vaccine design, but has proved difficult. Completion of the Plasmodium falciparum genome revealed thousands of potential vaccine candidates, but a significant bottleneck remains in their validation and prioritization for further evaluation in clinical trials. Focusing initially on the Plasmodium falciparum merozoite proteome, we used peer-reviewed publications, multiple proteomic and bioinformatic approaches, to select and prioritize potential immune targets. We expressed 109 P. falciparum recombinant proteins, the majority of which were obtained using a mammalian expression system that has been shown to produce biologically functional extracellular proteins, and used them to create KILchip v1.0: a novel protein microarray to facilitate high throughput multiplexed antibody detection from individual samples. The microarray assay was highly specific; antibodies against P. falciparum proteins were detected exclusively in sera from malaria-exposed but not malaria-naïve individuals. The intensity of antibody reactivity varied as expected from strong to weak across well-studied antigens such as AMA1 and RH5 (Kruskal-Wallis H test for trend: p-value <0.0001). The inter-assay and intra-assay variability was minimal, with reproducible results obtained in re-assays using the same chip over duration of 3 months. Antibodies quantified using the multiplexed format in KILchip v1.0 were highly correlated with those measured in the gold-standard monoplex ELISA (median (range) Spearman’s R of 0.84 (0.65-0.95)). KILchip v1.0 is a robust, scalable and adaptable protein microarray that has broad applicability to studies of naturally acquired immunity against malaria by providing a standardized tool for the detection of antibody correlates of protection. It will facilitate rapid high-throughput validation and prioritization of potential Plasmodium falciparum merozoite-stage antigens paving the way for urgently needed clinical trials for the next-generation of malaria vaccines.
2021-02-12 | PXD011724 | Pride
Project description:<p>Despite improved diagnostics, pulmonary pathogens in immunocompromised children frequently evade detection, leading to significant mortality. In this study, we performed RNA and DNA-based metagenomic next generation sequencing (mNGS) on 41 lower respiratory samples collected from 34 children. We identified a rich cross-domain pulmonary microbiome containing bacteria, fungi, RNA viruses, and DNA viruses in each patient. Potentially pathogenic bacteria were ubiquitous among samples but could be distinguished as possible causes of disease by parsing for outlier organisms. Potential pathogens were detected in half of samples previously negative by clinical diagnostics. Ongoing investigation is needed to determine the pathogenic significance of outlier microbes in the lungs of immunocompromised children with pulmonary disease. Metatranscriptomic (RNA) sequencing libraries are reported in the manuscript and are included for this release.</p>
| phs001684 | dbGaP
Project description:Comparison of total RNA-seq and Affymetrix GeneChip(R) Human Transcriptome Array 2.0 analysis methods and Affymetrix GeneChip® WT PLUS Reagent and NuGEN Ovation® PICO WTA System V2 amplification methods for the detection of significant differentially expressed genes isolated from whole blood and brain RNA samples Affymetrix and NuGEN amplification methods are compared to determine which is most efficient, cost effective, and accurate in the detection of differentialy expressed transcript clusters on the HTA 2.0 microarray The optimum amplification microarray data is compared to total RNA-seq analysis of the same samples to determine which is the most efficient, cost effective, and accurate method of detecting differentially expressed genes
2020-09-30 | GSE120327 | GEO
Project description:Identifying Significant Temporal Variation in Time Course Microarray Data Without Replicates
| PRJNA113577 | ENA
Project description:KILchip v1.0: a novel protein microarray to facilitate high throughput multiplexed antibody detection from individual samples
2018-11-19 | MSV000083144 | MassIVE