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Prediction of tissue-specific alternatively spliced exons

Motivation: Alternative splicing plays a major role in protein diversity and regulating gene expression in higher Eucaryotes [1], and more than half of the human genes are known to be alternatively spliced. Splicing is regulated by the interactions of protein factors with the splicing machinery, where the factors bind to elements in the regulated exons or the flanking introns. It is widely believed that alternative splicing is of particular importance in the nervous system. Defects in the splicing machinery have been known to cause a substantial fraction of human genetic diseases [2, 3]. But very little is actually known about the precise mechanism behind alternative splicing. In most cases, it is not clear what causes exons to be differentially skipped in different tissues. In fact, a very small percentage of all alternatively spliced exons have been discovered and confirmed. We aim to use microarray data for exon-exon juctions in different tissues to predict and discover new alternatively spliced exons using machine learning and statistical techniques, and to try to find splicing patterns across tissues. Previous Work: Alternative splicing has been studied by looking at Expressed Sequence Tags (EST’s). This method has significant limitations because of bias in transcript coverage and non-uniformity of tissue libraries or sampling [2]. Reverse Transcriptase polymerase chain reaction (RT-PCR) provides a more concrete proof of the alternative splicing of the exon, but it is labor-intensive, and is preferred when analysing only a few genes in a small number of tissues. Recent experiments have illustrated that microarray or fiber-optic array probes can monitor splicing events as well. The probes are positioned at exon-exon junctions across 52 tissue samples for each gene. This paper [4] analyzed the hybridization intensity data to identify tissue-specific differences. They modeled an expected background model for the intensities that assumed the presence of all exons in the gene, and this model was subtracted from the normalized observed intensities. Those with the greatest deviation from the normal expected hybridization values were labeled as being possibly skipped and some of these predictions were validated using RT-PCR. The Problem: We are trying to discover new alternatively spliced exons within human genes with the help of microarray data. Microarray data tends to be very noisy, and this particular data has a lot of variables associated with it. The gene expression levels in the various tissues are not known to us. In addition, probes show vast differences in their binding power within the genes.