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MAGenTA: a Galaxy implemented tool for complete Tn-Seq analysis and data visualization.

Authors :
McCoy KM
Antonio ML
van Opijnen T
Source :
Bioinformatics (Oxford, England) [Bioinformatics] 2017 Sep 01; Vol. 33 (17), pp. 2781-2783.
Publication Year :
2017

Abstract

Motivation: Transposon insertion sequencing (Tn-Seq) is a microbial systems-level tool, that can determine on a genome-wide scale and in high-throughput, whether a gene, or a specific genomic region, is important for fitness under a specific experimental condition.<br />Results: Here, we present MAGenTA, a suite of analysis tools which accurately calculate the growth rate for each disrupted gene in the genome to enable the discovery of: (i) new leads for gene function, (ii) non-coding RNAs; (iii) genes, pathways and ncRNAs that are involved in tolerating drugs or induce disease; (iv) higher order genome organization; and (v) host-factors that affect bacterial host susceptibility. MAGenTA is a complete Tn-Seq analysis pipeline making sensitive genome-wide fitness (i.e. growth rate) analysis available for most transposons and Tn-Seq associated approaches (e.g. TraDis, HiTS, IN-Seq) and includes fitness (growth rate) calculations, sliding window analysis, bottleneck calculations and corrections, statistics to compare experiments and strains and genome-wide fitness visualization.<br />Availability and Implementation: MAGenTA is available at the Galaxy public ToolShed repository and all source code can be found and are freely available at https://vanopijnenlab.github.io/MAGenTA/ .<br />Contact: vanopijn@bc.edu.<br />Supplementary Information: Supplementary data are available at Bioinformatics online.<br /> (© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Details

Language :
English
ISSN :
1367-4811
Volume :
33
Issue :
17
Database :
MEDLINE
Journal :
Bioinformatics (Oxford, England)
Publication Type :
Academic Journal
Accession number :
28498899
Full Text :
https://doi.org/10.1093/bioinformatics/btx320