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Improving discrimination of essential genes by modeling local insertion frequencies in transposon mutagenesis data

Transposon mutagenesis experiments enable the identification of essential genes in bacteria. Deep-sequencing of mutant libraries provides a large amount of high-resolution data on essentiality. Statistical methods developed to analyze this data have traditionally assumed that the probability of observing a transposon insertion is the same across the genome. This assumption, however, is inconsistent with the observed insertion frequencies from transposon mutant libraries of M. tuberculosis. We propose a modified binomial model of essentiality that can characterize the insertion probability of individual genes in which we allow local variation in the background insertion frequency in different non-essential regions of the genome. Using the Metropolis-Hastings algorithm, samples of the posterior insertion probabilities are obtained for each gene, and the probability of each gene being essential is estimated. We compare our predictions to those of previous methods and show that, by taking into consideration local insertion frequencies, our method is capable of making more conservative predictions that better match what is experimentally known about essential and non-essential genes.