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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

ABSTRACT: Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

PROVIDER: PRJNA414289 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR6173170_1.fastq.gz	Fastqsanger.gz
	SRR6173170_2.fastq.gz	Fastqsanger.gz
	SRR6173180_1.fastq.gz	Fastqsanger.gz
	SRR6173180_2.fastq.gz	Fastqsanger.gz
	SRR6173190_1.fastq.gz	Fastqsanger.gz

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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

Project description:Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [RNA-Seq]

Project description:Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [RNA-Seq]

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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [Junction Arrays]

Project description:Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [Junction Arrays]

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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [RNA-Seq]

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.

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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [Junction Arrays]

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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

Project description:This SuperSeries is composed of the SubSeries listed below.

2018-09-05 | GSE104974 | GEO

Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm.

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

Development of a novel splice array platform and its application in the identification of alternative splice variants

Project description:Changes in alternative splicing are associated with several pathological conditions, including cancer. Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array platform was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events. The array consists of exon probes and thermodynamically balanced junction probes. Suboptimal probes are tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was first validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms for 8000 genes in lung cancer samples compared to matched normal lung tissue. The expression of lung cancer-associated splice isoforms was validated by RT-PCR. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer. In conclusion, this highly accurate methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies. 20 normal/tumor paired specimens. Tumor samples are from non-small cell lung cancer (NSCLC) whereas normals are from adjacent normal lung tissue.

2010-06-21 | E-GEOD-18346 | biostudies-arrayexpress

Development of a novel splice array platform and its application in the identification of alternative splice variants

2009-10-06 | GSE18346 | GEO

Cross-platform study

Project description:Gene expression microarrays have made a profound impact in biomedical research. The diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and in house platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by QRT-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent pre-processing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms. This SuperSeries is composed of the SubSeries listed below.

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