2420 Nucleic Acids Research, 2012, Vol. 40, No. 6 Figure 5. (A) Plot represents GNI of eight selected probe sets, which are up and downregulated at the exon level during cardiomyocyte differen- tiation but are expressed equally on the gene level (see text). (B) One such gene is Spectrin alpha 2 (Spna2), which is known to be spliced into many isoforms. The parts of the 30-end of two isoforms is shown above. During cardiomyocyte differentiation, probe set ‘10470885’ is differentially expressed while the expression level of the gene does not change. (C) The RT-PCR validation of the cassette Exon 51 (according to ENSMUST00000113719) in ES cells compared with CB (upper). Additional primers were used to measure the global gene expression of Spna2 (lower). (D) Venn diagrams show the number of DEG, DEE and spliced genes during heart development and differentiation of cardiomyocytes. A gene or exon was considered as regulated if any of the three conditions in the two datasets differs signiﬁcantly (F-statistic P < 0.001). above revealed an overlap of regulated genes and exons (Figure 5D). The number of DEE derived from the cardiomyocyte differentiation dataset was remarkably smaller compared with the heart development dataset. To further investigate the reasons for the high discrepancy of DEE, we searched for genes that are involved in ‘RNA splicing’ (by Gene Ontology term GO:0008380). Interestingly, we found only three genes that are regulated during cardiomyocyte differentiation, but 34 splicing factors were identiﬁed during development of the heart (P=1 and P=0.04725, respectively, by right-tailed Fisher’s exact test). Hierarchical clustering of these 37 genes shows the large difference among different samples (Supplementary Figure S1). Comparison to exon arrays Previously, we described the identiﬁcation of eight heart-speciﬁc splicing events using EAA and exon arrays (10). For the current analysis, gene array data from the same set of tissues were employed (http://www.affymetrix .com) and used for a direct comparison between exon and gene arrays. Independently validated splicing events were compared to the result of the gene array analysis and are listed in Supplementary Table S3. In the case of Idh3b, gene array analysis indicated a wrong splicing event due to insufﬁcient probe set coverage (Supplementary Figure S2). However, in half of the observed cases, both types of arrays identiﬁed identical splicing events. The origin of samples which were used in the Affymetrix tissue dataset has not been published. For a systematic comparison between gene and arrays, we performed exon arrays with the same samples as we used for cardiomyocyte differentiation. First, we measured the cor- relation at the gene and exon levels (Supplementary Figure S3). As previously reported by others (8), we detected a very high correlation at the gene level (of R = 0.91) and a slightly lower correlation at the exon level (R = 0.87). To identify DEG and DEE, we analyzed the exon arrays with EAA using its default parameters. We found 1,014 DEGs and 769 DEEs using the same cut-off as for gene arrays (F-statistic P < 0.001). The identiﬁed genes and exons can be found in Supplementary Table S4. Next, we asked how many DEGs and DEEs were detected on both arrays. We identiﬁed 762 signiﬁcantly