Skip to main content
Skip to main menu


Hui Jiang

<a href="">Stanford University</a>

In mammalian cells, isoforms of a gene can have highly similar sequences yet encode proteins with remarkably different functional roles. Quantifying cellular abundance of isoforms is therefore of significant biological interest. In this talk, we will review methods for profiling isoform-specific gene expression using high-throughput technologies such as microarrays and ultra high-throughput RNA sequencing (RNA-Seq). We will show the intrinsic non-identifiability issue involved in the isoform deconvolution problem, especially for microarray data. We will introduce a statistical approach for profiling isoform-specific gene expression for RNA-Seq data, which uses a joint Poisson model for the estimation and a Bayesian approach for quantifying the uncertainty. We will then generalize the method to accommodate paired-end RNA-Seq data, as well as illustrate its intuitive minimal sufficient statistics and computationally feasible implementation. Time permitting, we will show Fisher information can be used to quantify statistical gains from using a paired-end RNA-Seq protocol.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar givenĀ has a direct impact upon ourĀ students and faculty.