Similar Datasets
Project description:Custom Microarray Developmental Disabilities Clinical Dataset
| PRJNA377195 | ENA
Project description:A method of stripping custom microarrays
| PRJNA108525 | ENA
Project description:Our genome wide analyses of microRNA expression profiles involve the hybridization of fluorescently labeled RNA samples to custom made, DNA microarrays based on the GAPSII coated slides. We describe a simple and effective method to regenerate such custom microarrays. Our protocol entails the use of a very low concentration of sodium hydroxide in a low salt buffer to strip RNA molecules from the arrays. The solution is also capable of removing DNA molecules hybridized to the slides, while preserving the slide coating and printed DNA probes. Slides can be stripped and reused at least twice without significantly sacrificing data quality. Keywords: expression study, new vs. stripped array comparison
2008-10-01 | GSE10305 | GEO
Project description:We developed a R-based script to select internal control genes based solely on read counts and gene sizes. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower covariance and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the custom selected genes were more stable. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. were more stable) than commonly used reference genes.
2019-08-21 | GSE136038 | GEO
Project description:Performing Chromatin IP of Klf2, Klf4, Klf5 and p53 in mouse embryonic stem cells with NimbleGen custom genomic tiling arrays, we sought to decipher Klf2, Klf4, Klf5-regulated genes. Keywords: genomic tiling arrays, ES cells
2008-02-11 | GSE9774 | GEO
Project description:A custom resequencing array for analysis of field isolates of plasmdium falciparum was created. Test of DNA with genotypes known at all loci genotyped by the microarray as well as test of accuracy correlation with amounts of DNA added to each array
2014-03-28 | GSE56305 | GEO
Project description:The aim of this study is to discover loss of specific miRNA loci in Wilms' tumors using array CGH. Custom arrays were designed based on the Agilent 2x105K Human Whole Genome Genomic microarray with Agilent’s eArray program (https://earray.chem.agilent.com/earray/), with additional probes that cover all miRNA regions (200 bps before, within and after each miRNA from miRBase v13, with each probe in triplicates to enhance the reliability). All custom-designed probes were designed in UCSC hg18. Probes from Agilent’s database were lifted-over from hg19 to hg18 (LiftOver tool: http://genome.ucsc.edu).
2012-03-31 | GSE28397 | GEO
Project description:To determine whether BCL6 binds to the certain locus we performed genomic localization studies by chromatin immunoprecipitations (ChIP) using a densely tiled custom oligonucleotide microarray covering the genomic loci of different genes. Keywords: ChIP-chip, Transcription Factor localization
2007-07-07 | GSE7673 | GEO
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description:Buffy coat PBMC RNA expression was tested using a custom Nanostring probe panel, to identify general immune and glucocorticoid receptor-associated immune genes. ClinicalTrials.gov Identifier: NCT00824941
2019-11-01 | GSE124549 | GEO