Statistical Analysis of Genetic Interactions in TnSeq data

Motivation: ``Transposon Sequencing" (TnSeq) is a method used to determine the composition of large libraries of bacterial transposon (Tn) mutants. Analytical methods are available for comparing two differentially selected pools of mutants to identify those that display conditional changes in fitness. However, identifying genetic interactions, which involves making Tn mutant libraries in different genetic backgrounds and subjecting them to different growth conditions, requires more sophisticated analyses. Pairwise comparative methods cannot distinguish between compositional differences that occurred during library generation from those that occurred as a result of selection. Furthermore, several distinct classes of genetic interaction are possible, which cannot all be distinguished with simple pairwise tests (e.g. alleviating vs. suppresive interactions).

Results: We present a hierarchical Bayesian method for evaluating the statistical significance of changes in enrichment of Tn mutants that is specifically designed for detecting genetic interactions. This approach allows a four-way comparison of insertion counts to specifically identify transposon mutants that differentially affect bacterial fitness depending on genetic background. Our approach was applied to TnSeq 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 distinct classes of genetic interactions, including entire biochemical pathways and known protein complexes.

Contact Information

If you have any questions, contact us at: ioerger@cs.tamu.edu.

Introduction

The software available here is a python implementation of the Bayesian analysis method referenced above. It utilizes read information obtained from sequencing libraries of transposon mutants, to determine genetic interactions.

Source Code

Source code is written in Python, and comes with a README document containing instructions.

Version History

Version 1.12 (04/07/2018): Fixed bug with incorrect sorting of adjusted probabilities.
Version 1.11 (03/18/2018): Fixed bug with size of datasets.
Version 1.10 (04/02/2017): Using empirical priors; Added type classification; More options.
Version 1.00 (12/08/2016): Initial release of code.

Requirements:

Python 2.7+ www.python.org
Scipy 0.6.0+ www.scipy.org/Download
Numpy 1.2.1+ www.scipy.org/Download

Source code can be extracted by using the following command:

tar -xvzf tnseq_GI_1.00.tar.gz

Data

Example files are provided below to test the execution of the script and help verify that input files are in the appropriate format:

Copyright Information

The method and implementation provided in this website was created by Michael A. DeJesus and Thomas R. Ioerger and is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License.

If you wish to use this source code, please provide attribution by using the following citation:

DeJesus, M.A., Nambi, S., Smith, C.M., Baker, R.E., Sassetti, C.M., and Ioerger, T.R. (2017). Statistical Analysis of Genetic Interactions in TnSeq Data. Nucleic Acids Research. 10.1093/nar/gkx128

Creative Commons License