two showed the expected bands. This demonstrates that splicing events that could not be detected by exon arrays could be identiﬁed and validated by gene arrays. AltAnalyze is a stand-alone software package to analyze different types of microarrays, including gene arrays, for alternative splicing. We compared the result of GAA with AltAnalyze side-by-side (Supplementary Table S6). Regarding known splicing events, both tools discovered the same proportion. However GAA found 48% more DEEs than AltAnalyze using the same thresh- olds. Five out of seven validated splicing events between ES and CB were not signiﬁcant or not found with AltAnalyze. The difference between both tools originates from different annotations and ﬁlters. Furthermore, AltAnalyze uses ‘constitutive exons’ instead of all core exons to estimate the gene expression, which inﬂuences the calculation of the SI. Since we cannot test all putative spliced exons, it is not possible for us to compare comprehensively with other tools. However, we showed by RT–PCR validations that GAA identiﬁes splicing events reliably and we believe that GAA outper- forms AltAnalyze in respect to usability. In the comparison between heart development and cardiomyocyte differentiation, we made an interesting ob- servation: although 275 genes showed statistically signiﬁ- cant differences among both related conditions (Figure 5D), only very limited number of exons (six exons) matched both processes. One might speculate that alternative splicing is a very common process by which genes respond to different conditions. This hypoth- esis is also quoted frequently to explain discrepancies between transcriptomics and proteomics datasets (36). To further investigate the discrepancy, we searched for regulated splicing factors and found a signiﬁcant enrich- ment during heart development, but not during cardio- myocyte differentiation (Supplementary Figure S1). One of the strongly regulated splicing factors in the adult heart is A2bp1 (7.36 fold upregulated), which encodes the Fox-1 protein. Fox-1 is known to be expressed in the adult mouse heart (37) and involved in the post-natally devel- opment of the heart (27). Other regulated splicing factors are the serine/arginine-rich family (Snrnp27, Sfrs2, Srsf5 and other) and the heterogeneous nuclear ribonucle- oproteins (Hnrnpa1, Hnrnpk and other) of which both can act as splicing activator and silencer by binding to short cis-motifs on the pre-mRNA [reviewed in (38)]. These regulated splicing factors in adult hearts may have caused signiﬁcantly more splicing events, which cannot be observed during cardiomyocyte differentiation. In addition, we found genes that did not show differ- ences of expression levels during differentiation of cardio- myocytes but underwent alternative splicing, in which we named this phenomenon to be ‘isoform switch’. The gene ‘Spna2’ is an example of such phenomenon. In our study, we identiﬁed an isoform switch (inclusion of Exon 51 upon the differentiation of ES cells into beating cardio- myocytes), which was validated by an RT–PCR experi- ment (Figure 5C). The spliced exon lies in the spectrin/ alpha-actinin domain (IPR018159; 47-2,315aa) at position 2217–2237aa, on the last of 20 repeats. A previous study demonstrated that homozygous mice appear healthy and Nucleic Acids Research, 2012, Vol. 40, No. 6 2423 morphologically normal when Exon 25 27 of Spna2, which lays downstream of the spectrin/alpha-actinin domain (1154–1189aa, which corresponds to repeat 10) and named CCC (calpain, caspase, calmodulin) domain, are deleted in the mouse (39). However, in rat hearts, an insertion of 20 aa exists next to repeat 10 called ‘alpha II-SH3i’, which is important for intracellular targeting of Connexin 43 to gap junctions and suspected to be a po- tential target for stress signaling pathways (40). Inclusion or exclusion of exon 51 might affect localization of Spna2 encoded ‘alpha II-spectrin’ at the Z disc and the plasma membrane of myoﬁbrils (41), and thereby biological properties of cardiomyocytes. It will be of interest to study functional consequences of differences in the distri- bution of isoforms during tissue development. Application of GAA might serve as a starting point for such approaches using existing data sets. We obtained CEL ﬁles from studies of pathological conditions of dilated cardiomyopathy (34) and right ven- tricular hypertrophy (35), which have been analyzed at the gene but not at the exon level. Since these experiments used different parts of the heart and have been conducted at different laboratories with animals of different genetic backgrounds, a direct comparison between all experiments needs careful evaluation (Figure 6A shows the similarity between all samples). Yet, comparison of datasets at the gene level revealed that some genes showed the same ex- pression pattern during the heart development and under pathological conditions. However, this correlation was somewhat lost when the comparison was made at the exon level (Figure 6B). In contrary to the popular belief that there is a re-initiation of embryonic gene expressions under pathological conditions (42,43), such trend does hold at the gene level but not so well at the exon level. This indicates that developmental alternative splicing events are not activated to full extent under pathological conditions. Hence, the activation of the fetal gene program under stress conditions is incomplete in respect to speciﬁc isoforms and represents a distinct state that can be distinguished from the regular developmental program. These ﬁndings further underline that changes in the tran- scriptome originate from gene expressions, but a care must be taken at the exon level by taking into account for al- ternative splicing events. Up until recently, high-throughput transcriptome studies mostly concentrated on changes in gene expres- sion. This trend has changed dramatically with the intro- duction of RNA-seq using deep sequencing technology, which uncovered that over 93% of genes have isoforms (1) indicating that alternative splicing events are more common than one anticipated. Although RNA-seq tech- nology is improving constantly, results cannot be analyzed without proper bioinformatical analysis, which imposes a hurdle for experimental biologists. GAA is an easy to use tool that allows experimental biologists with limited bioin- formatical skills to detect new splicing isoforms. GAA is a one-stop-shop for analysis of gene arrays to which a user can upload CEL ﬁles of gene arrays. Through a click of the mouse, a list of analytical methods can be selected. DEEs are indicated by box-and-whisker plot (Figure 3C) and by the genome viewer (Figure 5B) to identify locations