2416 Nucleic Acids Research, 2012, Vol. 40, No. 6 expressed in all conditions as well as exons that are not expressed in any condition were ﬁltered out. This elimin- ates incorrectly identiﬁed probe sets with high SI, which are resulting from the background noise (i.e. low signal-to-noise ratio). Cross-hybridizing probe sets and probe sets with an abnormally high SI were discarded, because they may not reﬂect true expression changes. Furthermore, large differences in gene expression levels can increase the noise resulting in false positives (11). All ﬁlters are optional and can be modiﬁed by the user. Since the initial introduction of EAA, we implemented several additional features. Previously, analysis was limited to pairwise comparisons, which we now extended to up to four groups per analysis by implementing limma (17) from the Bioconductor package, which has been ori- ginally applied on exon arrays by Shah and Pallas (18). Matching putative splicing events as found by GAA to known splicing events is a critical step to distinguish between false and true positives. For this purpose, we added a table with known splicing events to the result view. To implement this feature, we downloaded the ‘knownAlt’ track from the UCSC genome browser (19). This track contains 132 205 splicing events in human and mouse, which are categorized into nine different event types. In addition, we downloaded the transcript coordin- ates from the AceView database (20) of human, mouse and rat and identiﬁed 211 098 splicing events by utilizing ASTALAVISTA (21). Splicing events from both sources were imported into MySQL tables. Additionally, we added information about tissue-speciﬁc splicing events. For this purpose, we analyzed the publicly available tissue dataset from Affymetrix for human, mouse and rat. Each dataset contains 11 tissues with three replicates each. We compared each tissue against all other tissues as a group using EAA. For all 11 3 datasets, we used the Robust Multichip Average (RMA) (22) as a preprocessing algorithm and the default settings of the EAA. A probe set (exon) is considered as tissue-speciﬁcally (P < 0.01) expressed (SI > 1) or skipped (SI < 1). mESC culture and differentiation The clone CM7/1 of J1 mouse embryonic stem cell (mESC) line carrying the neomycin resistance gene driven by cardiac alpha-myosin heavy chain promoter was maintained and differentiated as previously described (23). The selection of cardiomyocytes with 400 mg/ml of G418 (GIBCO) was initiated on 9 days after the initiation of differentiation. Animals Hearts [E17.5, P1 and adult (2-month old)] were isolated from individual C57BL/6 mice. All animal experiments in this study were approved by the local animal care committee. Microarray experiment Total RNA was prepared with TRIzol Reagent (Invitrogen). The quality of extracted RNA was assessed by the Agilent Bioanalyzer (Agilent Technologies). Three hundred nanograms of total RNA from each sample was processed for hybridization onto GeneChip Mouse Gene 1.0 ST Array (Affymetrix) or GeneChip Mouse Exon 1.0 ST Array according to the manufacturer’s protocol and scanned. Raw and normalized data have been submitted to GEO and are available under series GSE33183. AltAnalyze For a comparison to the result of AltAnalyze, we down- loaded the most recent release (2.0.4) from http://www .altanalyze.org/. To reduce complexity, we excluded EB from the analysis and compared ES to CB only. We used the ENSEMBL release 55, since GAA is based on this version. To retrieve all probe sets (not just signiﬁcant ones), we set ‘Minimum alternative exon score’ and ‘Max MiDAS/normalized intensity P-value’ to 1. All other par- ameters were left as default settings. Gene Ontology analysis We used PantherDB (http://www.pantherdb.org/) to identify up- and downregulated Gene Ontology terms. All genes and exons, which were signiﬁcantly different between any condition (F-statistic P < 0.001), were used. Hierarchical clustering The normalized expression ﬁles were loaded onto MeV (24) to ﬁlter for the 25% of genes with highest standard deviations. The same software was used for hierarchical clustering of samples by using Pearson’s correlation with average linkage clustering. The dendogram was generated by TreeGraph2 (25). RESULTS Gene Array Analyzer The reliability of gene arrays compared to exon arrays has been studied at the gene (7) and exon levels (9), suggesting that gene arrays have the potential to proﬁle exon expres- sion changes (8). Inspired by these studies, we reasoned that gene arrays might be utilized to analyze alternative splicing events with existing algorithms. We therefore reconﬁgured our web server for exon array analysis (10) to match requirements of gene arrays and named this new tool GAA. To use GAA, the user only has to provide raw CEL ﬁles. GAA is capable of performing a complete analysis, including pre-processing (background correction, normalization and summarization), ﬁltering, gene expres- sion analysis, alternative splicing analysis and visualiza- tion of spliced genes (Figure 2). Unlike the former version of EAA, which was only able to perform a pairwise comparisons, up to four groups per analysis can be processed (Figure 3A). All groups can be compared to each other individually by setting up a contrast matrix. GAA allows graphical representation of known isoforms within its built-in genome browser to evaluate expressed isoforms, which is a critical step to dis- tinguish between false and true positives. Moreover, known splicing events from two different sources were integrated into GAA to assess whether a differentially