Statistical Problems in Estimating Alternative Splicing
                               Elizabeth Purdom
                      University of California, Berkeley


The biological process of alternative splicing allows cells to encode for different protein products in
the same stretch of DNA by including or excluding segments of DNA when translating the DNA into proteins.
It is now understood that alternative splicing and transcript events are an important method of
regulation of gene expression and can also be important in the study of disease and particularly cancer.
Many high-throughput technologies, generally used for measuring overall levels of gene expression, have
been adapted to measure alternative splicing.

We will discuss the statistical and computational challenges in detecting and quantifying alternative
splicing. In particular we will focus on analyzing data from two different types of technologies:
Affymetrix's Exon microarray and Next-generation Sequencing. We will focus on two questions 1) detecting
the regions of the gene (exons) that undergo alternative splicing and 2) quantifying expression levels
for the resulting products (isoforms) that are created by piecing these exons together. We will discuss
our methodologies for addressing these problems as well as the questions of quality control and
robustness necessary in the analysis of these platforms.