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

Juan P. Romero, María Ortiz-Estévez, Ander Muniategui, Soraya Carrancio, Fernando J. de Miguel, Fernando Carazo, Luis M Montuenga, Remco Loos, Rubén Pío, Matthew W. B. Trotter, Angel Rubio
2017 · doi:10.1101/197798
preprint OA: closed CC-BY-NC-ND-4.0
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RNA-seq and junction arrays show high coherence in detecting splicing events, with RNA-seq superior for novel events and junction arrays viable for well-described regions, especially at high read counts.

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Abstract

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. 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. 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.

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License: CC-BY-NC-ND-4.0