Human Exon 1.0 ST Affymetrix array data from SHSY-5Y cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE75465 | GEO | 2015/12/01
SECONDARY ACCESSION(S): PRJNA304374
REPOSITORIES: GEO
Similar Datasets
Project description:The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge owing to the vast amounts of data and the large variety of preprocessing and filtering steps used before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results, it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip®Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow, we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip®Human Exon 1.0 ST Array.
| S-EPMC4103539 | biostudies-literature
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
2008-09-15 | GSE11360 | GEO
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
2008-09-15 | GSE11359 | GEO
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.
2010-09-10 | GSE23141 | GEO
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.
2011-05-23 | GSE22454 | GEO
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.
2010-09-10 | E-GEOD-23141 | biostudies-arrayexpress
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.
2015-11-28 | GSE42625 | GEO
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.
2011-05-23 | E-GEOD-22454 | biostudies-arrayexpress
Project description:Affymetrix exon array and iCLIP analysis of SAFB1 function
| PRJNA304375 | ENA
Project description:BackgroundIt is known from recent studies that more than 90% of human multi-exon genes are subject to Alternative Splicing (AS), a key molecular mechanism in which multiple transcripts may be generated from a single gene. It is widely recognized that a breakdown in AS mechanisms plays an important role in cellular differentiation and pathologies. Polymerase Chain Reactions, microarrays and sequencing technologies have been applied to the study of transcript diversity arising from alternative expression. Last generation Affymetrix GeneChip Human Exon 1.0 ST Arrays offer a more detailed view of the gene expression profile providing information on the AS patterns. The exon array technology, with more than five million data points, can detect approximately one million exons, and it allows performing analyses at both gene and exon level. In this paper we describe BEAT, an integrated user-friendly bioinformatics framework to store, analyze and visualize exon arrays datasets. It combines a data warehouse approach with some rigorous statistical methods for assessing the AS of genes involved in diseases. Meta statistics are proposed as a novel approach to explore the analysis results. BEAT is available at http://beat.ba.itb.cnr.it.ResultsBEAT is a web tool which allows uploading and analyzing exon array datasets using standard statistical methods and an easy-to-use graphical web front-end. BEAT has been tested on a dataset with 173 samples and tuned using new datasets of exon array experiments from 28 colorectal cancer and 26 renal cell cancer samples produced at the Medical Genetics Unit of IRCCS Casa Sollievo della Sofferenza.To highlight all possible AS events, alternative names, accession Ids, Gene Ontology terms and biochemical pathways annotations are integrated with exon and gene level expression plots. The user can customize the results choosing custom thresholds for the statistical parameters and exploiting the available clinical data of the samples for a multivariate AS analysis.ConclusionsDespite exon array chips being widely used for transcriptomics studies, there is a lack of analysis tools offering advanced statistical features and requiring no programming knowledge. BEAT provides a user-friendly platform for a comprehensive study of AS events in human diseases, displaying the analysis results with easily interpretable and interactive tables and graphics.
| S-EPMC3314565 | biostudies-literature