Similar Datasets
Project description:BACKGROUND:RNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem. We compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments. RESULTS:As might be expected, RNA-seq appears superior in cases where the technologies disagree and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads. CONCLUSIONS:Our results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions.
| S-EPMC6156849 | biostudies-literature
Project description:In this study, we compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of splicing events. Three different cell lines were treated with CX-4945, a drug that severely affects splicing. To make a fair comparison, we developed EventPointer, an algorithm that detects and labels alternative splicing events in both junction arrays and RNA-seq. Common results and discrepancies between both technologies were validated or resolved by more than 200 PCRs.
2018-09-05 | GSE104973 | GEO
Project description:In this study, we compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of splicing events. Three different cell lines were treated with CX-4945, a drug that severely affects splicing. To make a fair comparison, we developed EventPointer, an algorithm that detects and labels alternative splicing events in both junction arrays and RNA-seq. Common results and discrepancies between both technologies were validated or resolved by more than 200 PCRs.
2018-09-05 | GSE104610 | GEO
Project description:Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm
| PRJNA414289 | ENA
Project description:Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [RNA-Seq]
| PRJNA414291 | ENA
Project description:Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [Junction Arrays]
| PRJNA413297 | ENA
Project description:Titration experiments measuring the gene expression from two different tissues, along with total RNA mixtures of the pure samples, are frequently used for quality evaluation of microarray technologies. Such a design implies that the true mRNA expression of each gene, is either constant or follows a monotonic trend between the mixtures, applying itself to the use of order restricted inference procedures. Exploiting only the postulated monotonicity of titration designs, we propose three statistical analysis methods for the validation of high-throughput genetic data and corresponding preprocessing techniques.Our methods allow for inference of accuracy, repeatability and cross-platform agreement, with minimal required assumptions regarding the underlying data generating process. Therefore, they are readily applicable to all sorts of genetic high-throughput data independent of the degree of preprocessing. An application to the EMERALD dataset was used to demonstrate how our methods provide a rich spectrum of easily interpretable quality metrics and allow the comparison of different microarray technologies and normalization methods. The results are on par with previous work, but provide additional new insights that cast doubt on the utility of popular preprocessing techniques, specifically concerning the EMERALD projects dataset.All datasets are available on EBI's ArrayExpress web site http://www.ebi.ac.uk/microarray-as/ae/) under accession numbers E-TABM-536, E-TABM-554 and E-TABM-555. Source code implemented in C and R is available at: http://statistics.msi.meduniwien.ac.at/float/cross_platform/. Methods for testing and variance decomposition have been made available in the R-package orQA, which can be downloaded and installed from CRAN http://cran.r-project.org.
| S-EPMC4766743 | biostudies-literature
Project description:Carbohydrates participate in almost every aspect of biology from protein sorting to modulating cell differentiation and cell-cell interactions. To date, the majority of data gathered on glycan expression has been obtained via analysis with either anti-glycan antibodies or lectins. A detailed understanding of the specificities of these reagents is critical to the analysis of carbohydrates in biological systems. Glycan microarrays are increasingly used to determine the binding specificity of glycan-binding proteins (GBPs). In this study, six different glycan microarray platforms with different modes of glycan presentation were compared using five well-known lectins; concanavalin A, Helix pomatia agglutinin, Maackia amurensis lectin I, Sambucus nigra agglutinin and wheat germ agglutinin. A new method (universal threshold) was developed to facilitate systematic comparisons across distinct array platforms. The strongest binders of each lectin were identified using the universal threshold across all platforms while identification of weaker binders was influenced by platform-specific factors including presentation of determinants, array composition and self-reported thresholding methods. This work compiles a rich dataset for comparative analysis of glycan array platforms and has important implications for the implementation of microarrays in the characterization of GBPs.
| S-EPMC4001710 | biostudies-literature
Project description:High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets.
| S-EPMC3019174 | biostudies-literature