Statistical analysis of genetic interactions in Tn-Seq data.
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.
Department of Computer Science, Texas A&M University, College Station, TX 77843, USA.
Michael A
MA
DeJesus
Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, 55 Lake Avenue N., Worcester, MA 01655, USA.
Subhalaxmi
S
Nambi
Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, 55 Lake Avenue N., Worcester, MA 01655, USA.
Clare M
CM
Smith
Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, 55 Lake Avenue N., Worcester, MA 01655, USA.
Richard E
RE
Baker
Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, 55 Lake Avenue N., Worcester, MA 01655, USA.
Christopher M
CM
Sassetti
Department of Computer Science, Texas A&M University, College Station, TX 77843, USA.
Thomas R
TR
Ioerger
D000465:Algorithms; D001426:Bacterial Proteins / Q000235:genetics; D001499:Bayes Theorem; D004251:DNA Transposable Elements; D003627:Data Interpretation, Statistical; D004843:Epistasis, Genetic; D005787:Gene Frequency; D055786:Gene Knockout Techniques; D015723:Gene Library; D005798:Genes, Bacterial; D008957:Models, Genetic; D009010:Monte Carlo Method; D016254:Mutagenesis, Insertional; D009169:Mycobacterium tuberculosis / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*
D001426:Bacterial Proteins; D004251:DNA Transposable Elements