Capturing Uncertainty by Modeling Local Transposon Insertion Frequencies Improves Discrimination of Essential Genes.

IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM

PubMedID: 26357081

DeJesus MA, Ioerger TR. Capturing Uncertainty by Modeling Local Transposon Insertion Frequencies Improves Discrimination of Essential Genes. IEEE/ACM Trans Comput Biol Bioinform. 2015;12(1):92-102.
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 were obtained for each gene, and the probability of each gene being essential is estimated. We compared 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.