MH-ESS is a python implementation of the Bayesian analysis method. It utilizes read information obtained from sequencing libraries of transposon mutants, to determine the essentiality of genes. Using a Bayesian framework, essentiality is modeled through the Extreme Value (Gumbel) distribution, which characterizes the maximum run of non-insertions (i.e. number of consecutive TA sites lacking insertion in a row). Genes with significantly larger runs of non-insertion thant statistically expected have a higher likelihood of essentiality. A Metropolis-Hastings sampling procedure is utilized to sample from conditional densities of essentiality for all genes, and posterior estimates of the probability of being essential are estimated for all genes.
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Michael DeJesus; Yanjia J. Zhang; Christopher M. Sassetti; Eric J. Rubin; James C. Sacchettini; Thomas R. Ioerger
Bayesian Analysis of Gene Essentiality based on Sequencing of Transposon Insertion Libraries
Bioinformatics (2013) 29 (6): 695-703.