regulated genes by both GAA and EAA, which corres- ponds to 76% of all DEG on exon arrays. For a compari- son at the exon level, we mapped the signiﬁcantly regulated gene array probe sets to ENSEMBL exons. Of these 89 unique ENSEMBL exons, 14 were identiﬁed as DEE by exon array probe sets as well (Supplementary Table S5 for a comparison). We performed RT–PCR ex- periments for nine splicing events and were able to validate all events. To demonstrate that gene arrays are capable of detecting splicing events that were not found with exon arrays, we randomly chose 14 probe sets that show no signiﬁcant regulation on exon arrays. Of these, two splicing events were validated as true positive results. Results of RT–PCR experiments can be found in Supplementary Figure S4 and the used primer pairs in Supplementary Table S6. Comparison to other software products Since the idea of using gene arrays for the analysis of al- ternative splicing events is new, there are only a very limited number of other products that can be directly compared to GAA. FIRMAGene, a modiﬁcation of FIRMA to gene arrays, represents an alternative approach, although it is an algorithm not a software product (9). Since there is no ‘gold standard’ dataset, a direct comparison between FIRMAGene and the use of Splice Indices employed by GAA is not possible. While we were building GAA, a software work ﬂow became available that can be used to analyze gene arrays as well as exon arrays at the gene and exon levels (31). The work ﬂow consists of two products: ‘AltAnalyze’ for pre-processing and statistic analysis of gene arrays; and DomainGraph for visualization of downstream analysis (e.g. miRNA predicted binding sites, pathway analysis and protein domain information). Compared to this work ﬂow, which requires a manual installation to the work station of the users, GAA users do not need to worry about the computational power of analysis because all calculations are carried out through server machines, which should minimize the requirement for high-end, powerful computers at the user side. Furthermore, since GAA is completely browser based, no set up on the user side is required, which avoids time-consuming operations, such as downloading annota- tion ﬁles and other relevant and required ﬁles for process- ing of gene array data. Another point is that AltAnalyze is a stand-alone application that provides the conﬁdentiality of experimental data. To cope with conﬁdentiality concerns, the uploaded CEL ﬁles are password protected and are kept at the server for 1 week, which can be extended through request by users, and will be eliminated thereafter. Moreover, users can use pseudo-sample names to further increase the conﬁdentiality. Unlike AltAnalyze and DomainGraph, GAA provides mRNA sequences of exons upstream and downstream of the identiﬁed differ- entially expressed exon in a new window (Figure 3E). This feature allows a user to simply copy and paste sequences to a primer designing program, such as ‘Primer3’ (32), to obtain a set of primers for the validation by RT–PCR experiments. Nucleic Acids Research, 2012, Vol. 40, No. 6 2421 To compare the result of AltAnalyze with GAA, we analyzed our gene arrays as described in the ‘Materials and Methods’ section. Since AltAnalyze uses SI > 1 and P < 0.05 as default thresholds, we also applied these values for the GAA result. Under these thresholds, we found 1707 regulated probe sets using GAA and 830 using AltAnalyze. Of which, 312 were found with both tools. To evaluate the true positive rate of both tools, we checked which probe sets match to known splicing events. In all, 17% of the probe sets detected as differen- tially expressed with both tools and 15% of the probe sets detected with either tool match to known splicing events. We also compared the result to our seven validated RT–PCR experimental results, which show differences between ES and CB. Two of these DEEs were also identiﬁed by AltAnalyze, further three show regulation but no signiﬁcant P-value. The other three DEEs appear not in the result of AltAnalyze. A direct comparison of the results can be found in Supplementary Table S7. Aberrant alternative splicing events under pathological conditions The major advantage of GAA is that it only requires Affymetrix CEL ﬁles. In other words, it is possible to re-evaluate previously published gene array data, which were analyzed at the gene level but not at the exon level. Hence, the information about changes in alternative splicing events can be obtained without additional experi- ments. As a proof-of-principle for such an approach, we re-analyzed public datasets dealing with various patho- logical conditions of the heart using GAA and compared the results to the dataset of heart development (Figure 6A). Phosphoinositide 3-kinase (PI3K) is essential for the heart development and plays an important role for the induction of physiological cardiac hypertrophy (33). Pretorius et al. (34) studied whether loss of PI3K makes the heart susceptible to atrial ﬁbrillation (AF) in a mouse model of dilated cardiomyopathy (DCM). To evaluate molecular changes, the authors analyzed transcriptomes of the left and right atria by gene arrays. Essentially, the authors found that a reduction of PI3K is correlated with a reduction of heart weight, although the heart functions normally. However, combination of PI3K loss and DCM leads to a more severe phenotype than DCM alone. We obtained the raw CEL ﬁles from the Gene Expression Omnibus (GSE12420) and analyzed them with GAA using the default settings. We found 565 DEEs and 2285 DEGs between Mst1 (mammalian sterile 20-like kinase 1 overexpression as a model for DCM) and PI3K knock-out and Mst1-PI3K (Mst1 overexpression and PI3K knock-out) compared to the NTg (non-transgenic) mice (Supplementary Table S8). Next, we used our previously published gene array data (GSE30428) of right ventricular hypertrophy induced by using pulmonary artery clipping (PAC) (35). In this data set, we found 105 DEGs and 419 DEEs using the same thresholds as above (P-value of the F-test < 0.001) (Supplementary Table S8).