Probabilistic identification of bacterial essential genes via insertion density using TraDIS data with Tn5 libraries.

ABSTRACT:

Motivation

Probabilistic Identification of bacterial essential genes using transposon-directed insertion-site sequencing (TraDIS) data based on Tn5 libraries has received relatively little attention in the literature; most methods are designed for mariner transposon insertions. Analysis of Tn5 transposon-based genomic data is challenging due to the high insertion density and genomic resolution. We present a novel probabilistic Bayesian approach for classifying bacterial essential genes using transposon insertion density derived from transposon insertion sequencing data. We implement a Markov chain Monte Carlo sampling procedure to estimate the posterior probability that any given gene is essential. We implement a Bayesian decision theory approach to selecting essential genes. We as

SUBMITTER: Nlebedim VU 

PROVIDER: S-EPMC8652038 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature