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.
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Acknowledgements
This work was supported by the Howard Hughes Medical Institute, the US National Institutes of Health (AI R37-042347 to M.K.W.; 5F32 GM108355-02 to M.C.C.), and the Swiss Foundation for Grants in Biology and Medicine (PASMP3_142724/1 to S.A.).
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Chao, M., Abel, S., Davis, B. et al. The design and analysis of transposon insertion sequencing experiments. Nat Rev Microbiol 14, 119–128 (2016). https://doi.org/10.1038/nrmicro.2015.7
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DOI: https://doi.org/10.1038/nrmicro.2015.7
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