2016
DOI: 10.1038/nrmicro.2015.7
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The design and analysis of transposon insertion sequencing experiments

Abstract: Preface Transposon-insertion sequencing (TIS) is a powerful approach that can be widely applied to genome-wide definition of loci that are required for growth in diverse conditions. However, experimental design choices and stochastic biological processes can heavily influence the results of TIS experiments and affect downstream statistical analysis. Here, we discuss TIS experimental parameters and how these factors relate to the benefits and limitations of the various statistical frameworks that can be applied… Show more

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Cited by 189 publications
(213 citation statements)
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“…The combined read counts per gene were further normalized per million mapped reads to account for the differences in sizes of mapped reads among sequenced libraries. Then a genomic location bias correction was performed in each library as described by (16). During bacterial growth and replication, the amount of DNA close to the origin of replication (Ori) increases, which results in more DNA around the Ori becoming available for sequencing.…”
Section: Methodsmentioning
confidence: 99%
“…The combined read counts per gene were further normalized per million mapped reads to account for the differences in sizes of mapped reads among sequenced libraries. Then a genomic location bias correction was performed in each library as described by (16). During bacterial growth and replication, the amount of DNA close to the origin of replication (Ori) increases, which results in more DNA around the Ori becoming available for sequencing.…”
Section: Methodsmentioning
confidence: 99%
“…However, some mutants may behave differently in a pool where there can be both competition as well trans complementation than when grown individually. Secondly, technically, transposon libraries are often constructed with an initial isolation on a rich medium and then subjected to a selection for growth on the condition of interest (often a more minimal media or media that models the host [13]. Because the isolation step is in fact a selection on rich media, genes that are essential in rich media cannot be evaluated in this way.…”
Section: Discussionmentioning
confidence: 99%
“…The different tools can vary dramatically both in the assumptions built into the analysis and how conservatively each model calls essentiality i.e., whether one is more willing to tolerate false positives or false negatives. For example, a Hidden Markov Model (HMM) and sliding window approach rely on a stretch of TA sites that have zero to very low level insertions to denote an essential gene [13,31]. An advantage of these methods is that intergenic regions and essential domains within a larger gene can be queried.…”
Section: Discussionmentioning
confidence: 99%
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