Shaked Shanas - Second Place
Advisor - Dr. Martin Mikl
Department of Human Biology

A eukaryotic gene typically gives rise to multiple mRNA isoforms and consequently multiple protein variants with potentially different functions. One way to describe this functional diversification of a gene is based on the expression of the mRNA isoforms derived from it, including aspects such as the number of isoforms and their relative abundance. Expression of a single dominant isoform can be interpreted as the gene having one main function, while expression of multiple isoforms at equal levels can imply more functional diversification. To systematically analyze gene expression from the angle of functional diversification, we need a way to quantitatively describe the isoform diversity of a gene. Here we developed a gene-level diversity metric and applied it to large transcriptomic datasets. We compare gene-level and transcriptome-level diversity across tissues and individuals and explore the relationship between isoform diversity, gene architecture and expression level. We show that isoform diversity is a gene- and tissue-specific property which can be decoupled from expression levels and often changes with age in a tissue-specific manner. Isoforom diversity can also separate tissues based on non-differentially expressed genes. This isoform diversity metric provides a complementary perspective to analyzing gene expression and can capture changes in the functional diversification of genes not detectable by classical gene expression analyses.