UMMS Affiliation

Department of Microbiology and Physiological Systems; UMass Metabolic Network

Date

2-22-2017

Document Type

Article

Disciplines

Biochemistry | Cellular and Molecular Physiology | Computational Biology | Microbiology | Molecular Biology

Abstract

Tn-Seq is an experimental method for probing the functions of genes through construction of complex random transposon insertion libraries and quantification of each mutant's abundance using next-generation sequencing. An important emerging application of Tn-Seq is for identifying genetic interactions, which involves comparing Tn mutant libraries generated in different genetic backgrounds (e.g. wild-type strain versus knockout strain). Several analytical methods have been proposed for analyzing Tn-Seq data to identify genetic interactions, including estimating relative fitness ratios and fitting a generalized linear model. However, these have limitations which necessitate an improved approach. We present a hierarchical Bayesian method for identifying genetic interactions through quantifying the statistical significance of changes in enrichment. The analysis involves a four-way comparison of insertion counts across datasets to identify transposon mutants that differentially affect bacterial fitness depending on genetic background. Our approach was applied to Tn-Seq libraries made in isogenic strains of Mycobacterium tuberculosis lacking three different genes of unknown function previously shown to be necessary for optimal fitness during infection. By analyzing the libraries subjected to selection in mice, we were able to distinguish several distinct classes of genetic interactions for each target gene that shed light on their functions and roles during infection.

Rights and Permissions

© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. Citation: Nucleic Acids Res. 2017 Feb 22. doi: 10.1093/nar/gkx128. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Keywords

genes, libraries, dna transposons, gene interaction, datasets

PubMed ID

28334803

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

 
 

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