Model-based identification of conditionally-essential genes from transposon-insertion sequencing data

Vishal Sarsani, Berent Aldikacti, Shai He, Rilee Zeinert, Peter Chien, Patrick Flaherty

Abstract:

The understanding of bacterial gene function has been greatly enhanced by recent
advancements in the deep sequencing of microbial genomes. Transposon insertion
sequencing methods combines next-generation sequencing techniques with transposon
mutagenesis for the exploration of the essentiality of genes under different environmental
conditions. We propose a model-based method that uses regularized negative binomial
regression to estimate the change in transposon insertions attributable to gene-environment
changes in this genetic interaction study without transformations or uniform normalization.
An empirical Bayes model for estimating the local false discovery rate combines unique and
total count information to test for genes that show a statistically significant change in transposon
counts. When applied to RB-TnSeq (randomized barcode transposon sequencing)
and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentus
using both total and unique count data the model was able to identify a set of conditionally
beneficial or conditionally detrimental genes for each target condition that shed light on their
functions and roles during various stress conditions.