NetCIS: a network-based common insertion site pipeline for case-control Sleeping Beauty screens.

Abstract

Forward genetic screens are powerful tools for novel biological discoveries because they do not require a priori knowledge of genes controlling a phenotype of interest. The Sleeping Beauty (SB) transposon system is one of many commonly employed genetic screen tools used to induce both gain and loss of gene function mutations via insertional mutagenesis. In the past, SB screens have been successfully utilized to discover several cancer driver genes that could serve as potential therapeutic targets. More recently, SB technologies have been extended to other applications, including the discovery of mechanisms contributing to drug resistance and novel targets for immunotherapy. Importantly, existing tools for SB screen data analysis do not support data-driven case-control comparisons capable of analyzing screens with this design. To this end, we developed a network-based common insertion site (NetCIS) analysis tool to robustly identify common insertion sites (CISes) in a case-control phenotype selection screen. NetCIS uses an efficient graph-based algorithm for discovering statistically significant CISes that differ between cases and controls. We benchmark NetCIS against other insertional mutagenesis analysis tools using a previously published SB dataset and show that NetCIS is able to prioritize previous biologically validated genes similarly or better than competing methods, is the only method that effectively utilizes a case-control experimental setup, and can identify CISes involving unannotated regions of the genome that are overlooked by existing methods. Code for NetCIS and an accompanying tutorial can be found at https://github.com/RogersLabGroup/NetCIS.