A statistical framework for genome-wide discovery of biomarker splice variations with GeneChip Human Exon 1.0 ST Arrays.

Alternative splicing is an important regulatory mechanism that generates multiple mRNA transcripts which are transcribed into functionally diverse proteins. According to the current studies, aberrant transcripts due to splicing mutations are known to cause for 15% of genetic diseases. Therefore understanding regulatory mechanism of alternative splicing is essential for identifying potential biomarkers for several types of human diseases. Most recently, advent of GeneChip Human Exon 1.0 ST Array enables us to measure genome-wide expression profiles of over one million exons. With this new microarray platform, analysis of functional gene expressions could be extended to detect not only differentially expressed genes, but also a set of specific-splicing events that are differentially observed between one or more experimental conditions, e.g. tumor or normal control cells. In this study, we address the statistical problems to identify differentially observed splicing variations from exon expression profiles. The proposed method is organized according to the following process: (1) Data preprocessing for removing systematic biases from the probe intensities. (2) Whole transcript analysis with the analysis of variance (ANOVA) to identify a set of loci that cause the alternative splicing-related to a certain disease. We test the proposed statistical approach on exon expression profiles of colorectal carcinoma. The applicability is verified and discussed in relation to the existing biological knowledge. This paper intends to highlight the potential role of statistical analysis of all exon microarray data. Our work is an important first step toward development of more advanced statistical technology. Supplementary information and materials are available from https://bonsai.ims.u-tokyo.ac.jp/~yoshidar/IBSB2006_ExonArray.htm.