Computational Genome-Wide Discovery of Aberrant Splice Variations with Exon Expression Profiles

Abstract

Alternative splicing plays a prominent role in eukaryotic gene regulations that allow a single gene to generate the multiple mRNA products. The recent advent of GeneChipreg Human Exon 1.0 ST Array enables us to measure the exon expression profiles of human cells on a genome-wide scale. With this advent, analysis of functional gene regulation could be extended to detect not only differentially expressed genes, but also specific splicing events that occur in target cells, but not in normal controls. We address some statistical issues for the identification of biomarker splice variations with exon expression data. The proposed method involves the following steps: (1) Whole transcript analysis with the nonparametric analysis of variance (ANOVA) to identify potential biomarkers that present specific splice variations. (2) Meta-analysis for discriminating non-specific splice variations that are caused by clinical heterogeneity in the collected samples. In the analysis of human cells, controlling non-specific splicing factors is essential for success in the detection of biomarker splice variations because splice patterns are possibly affected by inter-individual differences in the collected samples. We demonstrate its utility and perform a whole transcript analysis of exon expression profiles of colorectal carcinoma.