UMMS Affiliation
Department of Microbiology and Physiological Systems
Publication Date
2015-10-08
Document Type
Article
Disciplines
Bioinformatics | Computational Biology
Abstract
TnSeq has become a popular technique for determining the essentiality of genomic regions in bacterial organisms. Several methods have been developed to analyze the wealth of data that has been obtained through TnSeq experiments. We developed a tool for analyzing Himar1 TnSeq data called TRANSIT. TRANSIT provides a graphical interface to three different statistical methods for analyzing TnSeq data. These methods cover a variety of approaches capable of identifying essential genes in individual datasets as well as comparative analysis between conditions. We demonstrate the utility of this software by analyzing TnSeq datasets of M. tuberculosis grown on glycerol and cholesterol. We show that TRANSIT can be used to discover genes which have been previously implicated for growth on these carbon sources. TRANSIT is written in Python, and thus can be run on Windows, OSX and Linux platforms. The source code is distributed under the GNU GPL v3 license and can be obtained from the following GitHub repository: https://github.com/mad-lab/transit.
Rights and Permissions
Copyright: © 2015 DeJesus et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI of Published Version
10.1371/journal.pcbi.1004401
Source
PLoS Comput Biol. 2015 Oct 8;11(10):e1004401. doi: 10.1371/journal.pcbi.1004401. eCollection 2015. Link to article on publisher's site
Journal/Book/Conference Title
PLoS computational biology
Related Resources
PubMed ID
26447887
Repository Citation
DeJesus, Michael A.; Ambadipudi, Chaitra; Baker, Richard E.; Sassetti, Christopher M.; and Ioerger, Thomas R., "TRANSIT - A Software Tool for Himar1 TnSeq Analysis" (2015). Open Access Articles. 2600.
https://escholarship.umassmed.edu/oapubs/2600
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.