Abstract
Transposon insertion sequencing (TIS) is a powerful approach that can be extensively applied to the genome-wide definition of loci that are required for bacterial growth under diverse conditions. However, experimental design choices and stochastic biological processes can heavily influence the results of TIS experiments and affect downstream statistical analysis. In this Opinion article, we discuss TIS experimental parameters and how these factors relate to the benefits and limitations of the various statistical frameworks that can be applied to the computational analysis of TIS data.