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Analysis of DNA strand-specific differential expression with high density tiling microarrays

ABSTRACT: DNA microarray technology allows the analysis of genome structure and dynamics at genome-wide scale. Expression microarrays (EMA) contain probes for annotated open reading frames (ORF) and are widely used for the analysis of differential gene expression. By contrast, tiling microarrays (TMA) have a much higher probe density and provide unbiased genome-wide coverage. The purpose of this study was to develop a protocol that would take advantage of the high resolution of TMAs for quantitative measurement of DNA strand-specific differential expression of annotated and non-annotated transcripts. We extensively filtered probes present in Affymetrix Genechip Yeast Genome 2.0 expression and GeneChip S. pombe 1.0FR tiling microarrays to generate custom Chip Description Files (CDF) in order to compare the efficiency of both platforms. We experimentally tested the potential of our approach by measuring the differential expression of 4904 genes in the yeast Schizosaccharomyces pombe growing under conditions of oxidative stress. The results afforded a Pearson correlation coefficient of 0.943, indicating that TMAs are as reliable as EMAs for quantitative expression analysis. A significant advantage of TMAs over EMAs is the possibility of detecting non-annotated transcripts generated only under specific physiological conditions. We combined this property with a target-labelling protocol that preserves the original polarity of the transcripts with a view to determining the differential expression of non-annotated transcriptionally active regions.

ORGANISM(S): Schizosaccharomyces pombe 972h- Schizosaccharomyces pombe

PROVIDER: GSE19020 | GEO | 2010/02/23

SECONDARY ACCESSION(S): PRJNA120613

REPOSITORIES: GEO

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Analysis of DNA strand-specific differential expression with high density tiling microarrays

Project description:DNA microarray technology allows the analysis of genome structure and dynamics at genome-wide scale. Expression microarrays (EMA) contain probes for annotated open reading frames (ORF) and are widely used for the analysis of differential gene expression. By contrast, tiling microarrays (TMA) have a much higher probe density and provide unbiased genome-wide coverage. The purpose of this study was to develop a protocol that would take advantage of the high resolution of TMAs for quantitative measurement of DNA strand-specific differential expression of annotated and non-annotated transcripts. We extensively filtered probes present in Affymetrix Genechip Yeast Genome 2.0 expression and GeneChip S. pombe 1.0FR tiling microarrays to generate custom Chip Description Files (CDF) in order to compare the efficiency of both platforms. We experimentally tested the potential of our approach by measuring the differential expression of 4904 genes in the yeast Schizosaccharomyces pombe growing under conditions of oxidative stress. The results afforded a Pearson correlation coefficient of 0.943, indicating that TMAs are as reliable as EMAs for quantitative expression analysis. A significant advantage of TMAs over EMAs is the possibility of detecting non-annotated transcripts generated only under specific physiological conditions. We combined this property with a target-labelling protocol that preserves the original polarity of the transcripts with a view to determining the differential expression of non-annotated transcriptionally active regions. We have generated custom Chip Description Files (CDF) from Affymetrix Genechip Yeast Genome 2.0 expression and GeneChip S. pombe 1.0FR tiling microarrays to compare the efficiency of both platforms. We have experimentally tested the potential of our approach by measuring differential expression of the yeast Schizosaccharomyces pombe growing under conditions of oxidative stress.

2010-05-14 | E-GEOD-19020 | biostudies-arrayexpress

Design and validation of 8X60k gene-expression microarray for chickpea (Cicer arietinum) based on the transcriptome data

Project description:Microarrays have increasingly become a powerful tool for high throughput gene-expression studies and discovery of novel biomarker genes. Developed for a large number of organisms, including plants, microarrays are commonly performed for species that have sequenced data, for performing gene expression analysis, miRNA profiling, comparative genomic hybridization (CGH), ChIP-on-chip and SNP analysis. Genomic resources are still very limited for chickpea, a very important food legume crop. Here, we report the design and comprehensive validation of Next Generation Sequencing transcriptome data for chickpea through microarray technology to develop a high-throughput resource for studying the expression of all the transcripts in different biological samples to help functional genomics and breeding programs. This microarray design was developed and validated jointly by Genotypic Technology Private Limited and National Institute of Plant Genome Research. First, we designed 400k probes using reads covering 35k assembled contigs and 100k singletons chickpea transcripts. The 400k chip was hybridized with DNA and RNA samples of chickpea and microarray analysis was carried out. A total of 73,922 probes were found to be specific to chickpea transcripts. Best probes were filtered from the analyzed data and a total of 61,659 probes were selected to develop the final microarray design in 60k gene-expression microarray format. The probes represented 51,444 unique transcripts. The probes were annotated based on their corresponding chickpea transcript and similarity with other plants species. Microarray results were concordant with previous results from the NGS studies. The design of custom oligonucleotide probes for microarrays have varied functional genomic applications and this approach represents a valuable resource for chickpea.

2017-01-05 | GSE76972 | GEO

Design and validation of microarray for chickpea (Cicer arietinum) based on the analysis of transcriptome data [gene expression]

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Design and validation of microarray for chickpea (Cicer arietinum) based on the analysis of transcriptome data [CGH]

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Egg White as a quality control in MALDI Mass Spectrometry Imaging

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2019-12-11 | PXD014310 | Pride

RNAseq and Lariat sequencing of different S. pombe growth conditions

Project description:We performed lariat sequencing to profile the diversity of spliced RNA lariats in S. pombe identify annotated and alternate introns. Three different growth conditions were used to grow S. pombe wt and S. pombe Δdbr1. Lariat sequencing of the Δdbr1 strains and RNAseq of the wt and Δdbr1 strains were done to profile intron lariats and exon-exon junctions in RNA transcripts.

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Joint Genome-Wide Gene Expression and GWAS Mapping in the MGS Dataset

Project description:<p>Schizophrenia is a common and severe psychotic disorder. While some common SNPs and rare copy number variants have been identified as being significantly associated with disease risk, the biological mechanisms remain undefined. To identify gene expression abnormalities in schizophrenia, we generated whole-genome gene expression profiles using microarrays on lymphoblastoid cell lines from a total of 413 cases and 446 controls. Regression analysis identified 95 transcripts differentially expressed by affection status at a genome-wide false discovery rate of 0.05, while simultaneously controlling for confounding effects. These transcripts represented 89 genes with functions such as neurotransmission, gene regulation, cell cycle progression, differentiation, apoptosis, and immunity. The observed differential expression of extended major histocompatibility complex region genes converges with the genetic evidence from schizophrenia genome-wide association studies, which find the same region to be the most significant schizophrenia susceptibility locus. Our analysis also provides novel candidate genes for further study to assess their potential contribution to schizophrenia.</p>

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Project description:Biomarkers of familial longevity may represent mechanisms underlying healthy aging. To identify gene expression profiles marking human familial longevity, an explorative genome-wide expression study was performed among 50 families from the Leiden Longevity Study who have a life-long survival advantage of 30%. Gene expression profiles were compared between 50 nonagenarians (mean age 93.4 years) and 50 controls (mean age 61.9 years) to investigate differential gene expression that may arise as a function of both chronological age and familial longevity. Differential expression was observed for 2953 probes (FDR≤0.05) and for 109 GO terms, which corresponded well with previously reported findings on gene expression changes associated with chronological age, such as ‘immune response’, ‘signal transduction’ and ‘regulation of gene transcription’. To explore which of the 2953 chronological age-related probes also marked familial longevity, we compared gene expression profiles of 50 offspring of the nonagenarians (mean age 60.8 years) with the same 50 controls. Since the average gene expression levels did not differ between offspring and controls, we tested for differential expression as a function of age (age range 43-79 years). We identified 360 probes (FDR≤0.1) and the ‘Rho protein signal transduction’ GO biological process (FWER = 0.079) whose expression signatures marked familial longevity already at middle-age. Of these probes, 236 were annotated and represent 244 known genes, including WRN and MYC. Interestingly, 51 genes are involved in the regulation of gene expression. Further investigation into the genes involved may be important for unraveling mechanisms underlying longevity.

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