22 April 2016
Jesse Bloom
Basic Sciences Division
Fred Hutchinson Cancer Research Center
Computational algorithms to infer phylogenetic relationships or detect sites of positive selection are widely used in diverse branches of biology. However, anyone with a passing knowledge of modern biochemistry can recognize that the quantitative models used by these algorithms are woefully oversimplified. I will discuss prospects for making these models more realistic while keeping them computationally tractable. In particular, I will discuss how new sources of high-throughput experimental data might be leveraged to improve algorithms for the analysis of gene sequences.