Development of a Python‐Based Algorithm for the Quantification and Analysis of Multivariate Next Generation Sequencing Data Following Genome‐Wide Mutagenesis and Cell Survival Screening

Hsp90 is a highly conserved eukaryotic chaperone protein responsible for mediating a myriad of intracellular signaling pathways, including those associated with the glucocorticoid receptor (GR) and the intracellular immunoinflammatory response. Both hsp90 inhibitors such as geldanamycin and glucocorticoids such as prednisone have been shown to possess potent anticancer activity, which can be overcome at the cellular level by hereto unknown genetic modifications. The purpose of this study was to develop a computational approach for interpreting and analyzing next generation sequencing data following genome‐wide mutagenesis and anticancer cell survival screening assays. In order to assess genome mutation sites, an open‐source computer program was developed. The program allows for the reading of raw sequencing and annotated data files and permits the user to select filtering parameters to quantify the incidence and prevalence of identified mutations and mutation insertion sites. Further analysis parameters include annotation and extrapolation of the surrounding gene landscape and anticipated mutation influence. Additional parameters, including filtration based on mutation type and significance, sequencing confidence and reads, mutation location and neighboring gene profiles are also included, thus allowing for prediction of potential causal candidate mutations and genetic perturbations affecting cell viability subsequent to cell chaperone inhibition and glucocorticoid‐mediated transactivation.