Nucleic Acids Research, 2012, Vol. 40, No. 6 2415 Figure 1. (A) Probe sets on exon arrays are virtually grouped into three non-overlapping categories, namely ‘core’, ‘extended’ and ‘full’. The ‘core’ set covers well-annotated genes from RefSeq and full-length mRNAs, ‘extended’ cDNA alignments and ‘full’ gene predictions. The number of covered genes on gene and exon array ‘core’ sets are very similar. However, exon arrays have a large number of additional probe sets for hypo- thetical exons and genes (e.g. from GeneScan) in the ‘extended’ and ‘full’ sets. The numbers shown above were calculated using Galaxy’s (6) function ‘Proﬁle Annotations’ on the genomic positions of mouse gene and exon array probe sets. (B) Distribution of probes on 30, gene and exon arrays. The red boxes represent exons of a hypothetical gene, spliced into three isoforms. 30 arrays have probes in the 30 region of a gene to measure gene expressions. For gene and exon arrays, probes distributed across the entire length of the gene are summarized to measure gene expressions. Eij GNIij ¼ Gj GNIiA SI ¼ log2 GNIiB where Eij is the exon signal for exon i in sample j. Gj is the gene level signal in j. Using this value, the SI can be calculated as follows: In a previous study, we introduced the Exon Array Analyzer (EAA) (http://EAA.mpi-bn.mpg.de), an easy-to-use web tool for analyzing exon arrays for differ- ential expressed exons (10). Here, we describe the Gene Array Analyzer (GAA), a versatile tool to analyze GeneChip Array data at both gene and exon levels. To demonstrate the applicability of GAA, we proﬁled isoform switch events that occur during heart develop- ment and cardiomyocyte differentiation of murine embry- onic stem (ES) cells. We also used GAA to re-evaluate already existing publicly available datasets, which resulted in the detection of disease-related splicing events to recapitulate the developmental splicing processes. MATERIALS AND METHODS Implementation of GAA The analysis of gene arrays followed a similar approach as our previous work with exon arrays (10). In brief, the Affymetrix Power Tools (http://www.affymetrix.com) were used for normalization, background correction and summarization of raw CEL ﬁles uploaded to the GAA server by the user. The ps- and the mps-ﬁles for gene 1 ð Þ 2 ð Þ arrays were utilized. The ps-ﬁle contains a list of probe sets for exon level, and the mps-ﬁle contains probe sets grouped into transcript cluster, which represent a gene. All probe set signals of a transcript cluster are used by the Affymetrix Power Tools to estimate the gene expression signal. For the identiﬁcation of differentially expressed exons, we applied the Splice Index (SI) (11). Gene level normalized intensity (GNI) was calculated for each exon: where A and B are the two samples to compare. This approach has been commonly applied in exon array ex- periments on the ‘core’ annotation set (12–14). To avoid inﬂated false positive results produced by the SI and other algorithms (15), we used a set of ﬁlters as described by a guideline of Affymetrix (16). Thus, genes that are not