Project description:The Affymetrix Human Exon 1.0 ST array was used to measure differential splicing patterns in archived RNA isolated from 26 of 80 children (11 Rejectors and 15 Non-Rejectors). The exon-level probe summaries reported in this series were computed using the Affymetrix Power Tools (APT) software and 'rma-sketch' normalization method. Keywords: Affymetrix 1.0 ST exon array; exon-level analysis
Project description:The Affymetrix Human Exon 1.0 ST array was used to measure differential splicing patterns in archived RNA isolated from 26 of 80 children (11 Rejectors and 15 Non-Rejectors). The gene-level probe summaries reported in this series were computed using the Affymetrix Power Tools (APT) software and 'rma-sketch' normalization method. Keywords: Affymetrix 1.0 ST exon array; gene-level analysis
Project description:The aim of the experiment was to evaluate the performance of the Affymetrix Brassica Exon 1.0 ST array. Root and leaf samples from Brassica rapa line R-O-18 were compared. The same RNA samples were hybridised to the Agilent Brassica array, to compare the performance of the two arrays.
Project description:This experiment accompanies the main analysis using a custom MHC array to define the first high-resolution, strand-specific transcriptional map of the MHC, defining differences in gene expression for three common haplotypes associated with autoimmune disease. Unstimulated samples for each haplotype were hybridised to Affymetrix Human Exon 1.0 ST arrays as well the custom MHC array. Exon array data were used to assess the concordance of signal obtained from the two platforms and to investigate the extent of alternative splicing in the MHC, and how it compares to the rest of the genome.
Project description:The aim of the experiment was to evaluate the performance of the Affymetrix Brassica Exon 1.0 ST array. Root and leaf samples from Brassica rapa line R-O-18 were compared. The same RNA samples were hybridised to the Agilent Brassica array, to compare the performance of the two arrays. 6 samples were hybridised to each array. Triplicate samples of 11-day-old roots and 2 semi-expanded leaves from 23-day-old Brassica rapoa R-O-18 plants.
Project description:This experiment accompanies the main analysis using a custom MHC array to define the first high-resolution, strand-specific transcriptional map of the MHC, defining differences in gene expression for three common haplotypes associated with autoimmune disease. Unstimulated samples for each haplotype were hybridised to Affymetrix Human Exon 1.0 ST arrays as well the custom MHC array. Exon array data were used to assess the concordance of signal obtained from the two platforms and to investigate the extent of alternative splicing in the MHC, and how it compares to the rest of the genome. Lymphoblastoid cell lines carrying three common autoimmunity haplotypes (COX, PGF, QBL) were analysed in triplicate using the Affymetrix Human Exon 1.0 ST Array.
Project description:Human genomic variations are associated with several phenotypic traits, such as facial features or hereditary diseases. These variations can be, for example, single nucleotide polymorphisms (SNPs) or copy number variations (CNVs). Several genome-wide studies detecting the correlations between genomic variants and gene expression levels have been performed. In this study, we have studied human embryonic stem cells and human induced pluripotent stem cells and computationally identified the associations between SNPs and expression levels of exons, transcripts and genes as well as associations between CNVs and gene expression levels. We have identified SNP genotypes and gene copy numbers with genome wide Affymetrix SNP arrays and expression levels are measured with Affymetrix Exon arrays. For identifying SNPs that reliably correlate with expression levels, we filtered out the values that may cause variability to the expression values, such as the values measured with probes locating in SNP-areas. Additionally, we perform downstream analyses such as transcription factor binding site analysis and enrichment analysis. Further, we detected the genes that could be associated both with CNVs and SNPs and as expected according to earlier studies, we identify a few of this kind of genes. Overall, we could find several CNVs that correlated with gene expression levels while only few cases of SNPs that correlated with expression levels could be found as the sample size was small. However, as stem cells are hoped to be used in personalized disease treatments, our findings are important and provide a useful test set for experimental laboratory studies. In addition, our results open an interesting future direction to study how our findings correlate with the diversity of stem cell lines such as the variation in their differentiation potential. In the study, data from GEO Series GSE15097 and Series GSE26173 was also used.