Probabilistic Inference of Alternative Splicing Events in Microarray Data

Alternative splicing (AS) is an important and frequent step in mammalian gene expression that allows a single gene to specify multiple products, and is crucial for the regulation of fundamental biological processes. The extent of AS regulation, and the mechanisms involved, are not well un- derstood. We have developed a custom DNA microarray platform for surveying AS levels on a large scale. We present here a generative model for the AS Array Platform (GenASAP) and demonstrate its utility for quantifying AS levels in different mouse tissues. Learning is performed using a variational expectation maximization algorithm, and the parame- ters are shown to correctly capture expected AS trends.