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Estimating Isoform-Specific Gene Expression Using Paired-End RNA-Seq

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If you have a question about this talk, please contact Mustapha Amrani.

Statistical Challenges Arising from Genome Resequencing

In mammalian cells, RNAs can have highly similar sequences yet encode proteins with remarkably different functional roles. Isoforms of a gene are an example of a collection of such RNAs. Accumulating evidence suggests that a key factor charactering cell function in mammals is differential isoform expression. Quantifying differences in cellular abundance of isoforms is therefore of significant biological interest. Ultra High Throughput Sequencing (UHTS) is an emerging technology which promises to become as (or more) powerful, popular and cost-effective than current microarray technology for estimating gene expression, particularly at the level of isoforms. This talk will introduce a statistical model for estimating isoform abundance from UHTS data, and illustrate its intuitive minimal sufficient statistics and computationally feasible implementation. Time permitting, we will describe extensions of this work including how Fisher information can be used to quantify statistical gains from using a paired-end RNA -Seq protocol. This is joint work with Julia Salzman and Wing Hung Wong.

This talk is part of the Isaac Newton Institute Seminar Series series.

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