2422 Nucleic Acids Research, 2012, Vol. 40, No. 6 Figure 6. Gene and exon expression changes under pathological conditions. (A) Hierarchical clustering of gene arrays of different studies. The data set from cell culture experiments are clearly separated from those derived from whole hearts. In addition, hearts at early developmental stages are distant from pathological conditions and adult hearts. (B) Venn diagrams of regulated genes under three different studies. Only genes that are signiﬁcantly regulated (P < 0.001 in both comparisons) and show the same trend of differentially expression (fold change < 0.5 or >0.5 in the compared conditions) are counted. The comparison of the results of the above two studies with our study of heart development identiﬁed 268 overlapping genes among the three groups, but only 29 probes showing similar expression patterns at the exon level (Figure 6B). DISCUSSION In our study, we describe a simple and efﬁcient way to analyze splicing events using the widely applied gene arrays. Gene arrays were originally designed to measure genome-wide expression changes. However, their probe design also allows for the analysis of changes at the exon level to identify alternative splicing events. Gene arrays have limitations due to the lower number of probes per probe set, less probe sets per gene and no probes for putative genes in comparison to exon arrays, but they are much cheaper to use. Furthermore, numerous datasets from gene arrays are available, which have not yet been exploited to study differential splicing events. The applicability of GAA was demonstrated by analyzing datasets from heart development and cardio- myocyte differentiation. We were able to identify DEGs and DEEs in both datasets and illustrated how the graph- ical output of GAA helps to recognize different isoforms. Gene arrays are not limited to a priori known splicing events, which may lead to the detection of de novo isoforms. To support such novel discoveries, GAA provides the built-in genome browser and the inclusion of known splicing events to easily separate known and novel splicing isoforms. Our own data (Supplementary Figure S3) and others’ (7–9) demonstrated that gene arrays provide a comparable signal for exon expression as exon arrays. To demonstrate this potential, we analyzed similar data sets by using both types of arrays and compared the outcome on eight validated examples (Supplementary Table S3). As expected, gene arrays were able to identify most splicing events, although one splicing event was falsely recognized because of the lower probe set coverage. To further evaluate the performance of gene arrays compared to exon arrays, we analyzed the same data set of cardiomyocyte differentiation with exon arrays. We compared the number of DEGs and DEEs identiﬁed by both array types. At the gene level, we found less DEG with exon arrays compared to gene arrays. The reasons for this difference have been discussed elsewhere (7). At the exon level, we found 14 DEEs with both arrays and validated nine splicing events by RT–PCR experiments (Supplementary Figure S4). In addition, we performed RT–PCR experiments for further 14 DEEs. Of which,