EventPointer: an effective identification of alternative splicing events using junction arrays

Abstract

BackgroundAlternative splicing (AS) is a major source of variability in the transcriptome of eukaryotes. There is an increasing interest in its role in different pathologies. Before sequencing technology appeared, AS was measured with specific arrays. However, these arrays did not perform well in the detection of AS events and provided very large false discovery rates (FDR). Recently the Human Transcriptome Array 2.0 (HTA 2.0) has been deployed. It includes junction probes. However, the interpretation software provided by its vendor (TAC 3.0) does not fully exploit its potential (does not study jointly the exons and junctions involved in a splicing event) and can only be applied to case–control studies. New statistical algorithms and software must be developed in order to exploit the HTA 2.0 array for event detection.ResultsWe have developed EventPointer, an R package (built under the aroma.affymetrix framework) to search and analyze Alternative Splicing events using HTA 2.0 arrays. This software uses a linear model that broadens its application from plain case–control studies to complex experimental designs. Given the CEL files and the design and contrast matrices, the software retrieves a list of all the detected events indicating: 1) the type of event (exon cassette, alternative 3′, etc.), 2) its fold change and its statistical significance, and 3) the potential protein domains affected by the AS events and the statistical significance of the possible enrichment.Our tests have shown that EventPointer has an extremely low FDR value (only 1 false positive within the tested top-200 events). This software is publicly available and it has been uploaded to GitHub.ConclusionsThis software empowers the HTA 2.0 arrays for AS event detection as an alternative to RNA-seq: simplifying considerably the required analysis, speeding it up and reducing the required computational power.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2816-x) contains supplementary material, which is available to authorized users.

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

“…The gene expression correlations observed are similar to previously reported comparisons between RNA-seq and exon arrays [38]. Figure 1 depicts the EventPointer pipeline for both profiling technologies (see original publication [8] for further detail), with CEL files (microarray) or BAM files (RNA-seq) as starting input. When building the splicing graph, each exon is split into two nodes that correspond to its start and end genomic positions respectively (Figure 1.b).…”

“…The software is available for download at Bioconductor. A thorough description of EventPointer for microarrays can be found in [8]. This method has been extended to RNA-seq.…”

“…The development of exon microarrays enabled the transcriptomic study of differential splicing events, but PCR validation rates for identification of splice differences via microarray analysis tend to be lower than those observed for identification of differential gene expression using similar technologies [3][4][5]. Junction arrays [6][7][8][9][10] have been proposed to overcome this problem by using oligonucleotide probe-sets that interrogate junctions between exons in the transcriptome, as well as the exons themselves.…”

“…SpliceGrapher and SGSeq detect events prior to application of separate software in order to state corresponding statistical significance, whereas rMats and SplAdder perform both detection and statistical analysis. Alongside NGS-based approaches, AS event detection methods are available for exon arrays [31], and exon-junction arrays [6,8,9,32]. The latter methods display validation rates well above 50%.…”

“…The principal aim of this work is to compare RNA-seq and exon-junction microarray technologies in their ability to detect differential AS events. To do so comprehensively, and to allow as close to a direct comparison as possible, we have adapted the EventPointer [8] algorithm for application to data from both platforms, generated from the same control experiment. The control experiment comprises three distinct triple-negative breast cancer (TNBC) cell-lines, exposed in culture to a drug known to affect the transcriptional machinery and, thereby, to induce AS events.…”