Normalization of transposon-mutant library sequencing datasets to improve identification of conditionally essential genes

Sequencing of transposon-mutant libraries using next-generation sequencing (TnSeq) has become a popular method for determining which genes and non-coding regions are essential for growth under various conditions in bacteria. For methods that rely on quantitative comparison of counts of reads at transposon insertion sites, proper normalization of TnSeq datasets is vitally important. Real TnSeq datasets are often noisy and exhibit a significant skew that can be dominated by high counts at a small number of sites (often for non-biological reasons). If two datasets that are not appropriately normalized are compared, it might cause the artifactual appearance of Differentially Essential (DE) genes in a statistical test, constituting type I errors (false positives). In this paper, we propose a novel method for normalization of TnSeq datasets that corrects for the skew of read-count distributions by fitting them to a Beta-Geometric distribution. We show that this read-count correction procedure reduces the number of false positives when comparing replicate datasets grown under the same conditions (for which no genuine differences in essentiality are expected). We compare these results to results obtained with other normalization procedures, and show that it results in greater reduction in the number of false positives. In addition we investigate the effects of normalization on the detection of DE genes.