title: A zero-inflated Poisson model for insertion tolerance analysis of genes based on Tn-seq data.
abstract: Transposon insertion sequencing (Tn-seq) is an emerging technology that combines transposon mutagenesis with next-generation sequencing technologies for the identification of genes related to bacterial survival. The resulting data from Tn-seq experiments consist of sequence reads mapped to millions of potential transposon insertion sites and a large portion of insertion sites have zero mapped reads. Novel statistical method for Tn-seq data analysis is needed to infer functions of genes on bacterial growth.
  
  
  
  In this article, we propose a zero-inflated Poisson model for analyzing the Tn-seq data that are high-dimensional and with an excess of zeros. Maximum likelihood estimates of model parameters are obtained using an expectation-maximization (EM) algorithm, and pseudogenes are utilized to construct appropriate statistical tests for the transposon insertion tolerance of normal genes of interest. We propose a multiple testing procedure that categorizes genes into each of the three states, hypo-tolerant, tolerant and hyper-tolerant, while controlling the false discovery rate. We evaluate the proposed method with simulation studies and apply the proposed method to a real Tn-seq data from an experiment that studied the bacterial pathogen, Campylobacter jejuniAvailability and implementation: We provide R code for implementing our proposed method at http://github.com/ffliu/TnSeq A user's guide with example data analysis is also available there.
  
  
  
  pliu@iastate.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, Iowa State University.
authors.forename: Fangfang
authors.initials: F
authors.lastname: Liu
authors.affiliation: Department of Statistics, Iowa State University, Department of Veterinary Diagnostic and Production Animal Medicine and.
authors.forename: Chong
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50010, USA.
authors.forename: Zuowei
authors.initials: Z
authors.lastname: Wu
authors.affiliation: Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50010, USA.
authors.forename: Qijing
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Department of Statistics, Iowa State University.
authors.forename: Peng
authors.initials: P
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D004251:DNA Transposable Elements; D059014:High-Throughput Nucleotide Sequencing; D016013:Likelihood Functions
chemical_list: D004251:DNA Transposable Elements