Probabilistic Inference of Alternative Splicing Events in Microarray Data
Ofer Shai, University of Toronto

Alternative splicing (AS) is an important and frequent step in
mammalian gene expression that allows a single gene to specify multiple
products. In addition to providing a mechanism for expanding the genetic
repertoire, AS is critical for the regulation of fundamental biological
processes, including cellular differentiation, development and evolution.
The extent of AS regulation, and the mechanisms involved, are not well
understood. We have developed a custom DNA microarray platform for surveying
AS levels on a large scale. In this report, we present the development of
GenASAP (a Generative model for the AS Array Platform) and demonstrate its
utility for quantifying AS levels in different mouse tissues. Machine
learning is performed using a variational expectation maximization
algorithm, and the parameters are shown to correctly capture expected AS
trends. A comparison of the results obtained with a well-established but low
through-put experimental method demonstrate that AS levels obtained from
GenASAP are highly predictive of AS levels in mammalian tissues.