Dataset Information

TRANSIT--A Software Tool for Himar1 TnSeq Analysis.

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.

SUBMITTER: DeJesus MA

PROVIDER: S-EPMC4598096 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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TRANSIT--A Software Tool for Himar1 TnSeq Analysis.

DeJesus Michael A MA Ambadipudi Chaitra C Baker Richard R Sassetti Christopher C Ioerger Thomas R TR

PLoS computational biology 20151008 10

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 da ...[more]

PMID: 26447887

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Project description:TnSeq is a widely used methodology for determining gene essentiality, conditional fitness, and genetic interactions in bacteria. The Himar1 transposon is restricted to insertions at TA dinucleotides, but otherwise, few site-specific biases have been identified. As a result, most analytical approaches assume that insertions are expected to be randomly distributed among TA sites in nonessential regions. However, through analysis of Himar1 transposon libraries in Mycobacterium tuberculosis, we demonstrate that there are site-specific biases that affect the frequency of insertion of the Himar1 transposon at different TA sites. We use machine learning and statistical models to characterize patterns in the nucleotides surrounding TA sites that correlate with high or low insertion counts. We then develop a quantitative model based on these patterns that can be used to predict the expected counts at each TA site based on nucleotide context, which can explain up to half of the variance in insertion counts. We show that these insertion preferences exist in Himar1 TnSeq data sets from other mycobacterial and nonmycobacterial species. We present an improved method for identification of essential genes, called TTN-Fitness, that can better distinguish true biological fitness effects by comparing observed counts to expected counts based on our site-specific model of insertion preferences. Compared to previous essentiality methods, TTN-Fitness can make finer distinctions among genes whose disruption causes a fitness defect (or advantage), separating them out from the large pool of nonessentials, and is able to classify many smaller genes (with few TA sites) that were previously characterized as uncertain. IMPORTANCE When using the Himar1 transposon to create transposon insertion mutant libraries, it is known that the transposon is restricted to insertions at TA dinucleotide sites throughout the genome, and the absence of insertions is used to infer which genes are essential (or conditionally essential) in a bacterial organism. It is widely assumed that insertions in nonessential regions are otherwise random, and this assumption is used as the basis of several methods for statistical analysis of TnSeq data. In this paper, we show that the nucleotide sequence surrounding TA sites influences the magnitude of insertions, and these Himar1 insertion preferences (sequence biases) can partially explain why some sites have higher counts than others. We use this predictive model to make improved estimates of the fitness effects of genes, which help make finer distinctions of the phenotype and biological consequences of disruption of nonessential genes.

Project description:BackgroundThis paper presents PyElph, a software tool which automatically extracts data from gel images, computes the molecular weights of the analyzed molecules or fragments, compares DNA patterns which result from experiments with molecular genetic markers and, also, generates phylogenetic trees computed by five clustering methods, using the information extracted from the analyzed gel image. The software can be successfully used for population genetics, phylogenetics, taxonomic studies and other applications which require gel image analysis. Researchers and students working in molecular biology and genetics would benefit greatly from the proposed software because it is free, open source, easy to use, has a friendly Graphical User Interface and does not depend on specific image acquisition devices like other commercial programs with similar functionalities do.ResultsPyElph software tool is entirely implemented in Python which is a very popular programming language among the bioinformatics community. It provides a very friendly Graphical User Interface which was designed in six steps that gradually lead to the results. The user is guided through the following steps: image loading and preparation, lane detection, band detection, molecular weights computation based on a molecular weight marker, band matching and finally, the computation and visualization of phylogenetic trees. A strong point of the software is the visualization component for the processed data. The Graphical User Interface provides operations for image manipulation and highlights lanes, bands and band matching in the analyzed gel image. All the data and images generated in each step can be saved. The software has been tested on several DNA patterns obtained from experiments with different genetic markers. Examples of genetic markers which can be analyzed using PyElph are RFLP (Restriction Fragment Length Polymorphism), AFLP (Amplified Fragment Length Polymorphism), RAPD (Random Amplification of Polymorphic DNA) and STR (Short Tandem Repeat). The similarity between the DNA sequences is computed and used to generate phylogenetic trees which are very useful for population genetics studies and taxonomic classification.ConclusionsPyElph decreases the effort and time spent processing data from gel images by providing an automatic step-by-step gel image analysis system with a friendly Graphical User Interface. The proposed free software tool is suitable for researchers and students which do not have access to expensive commercial software and image acquisition devices.

Dataset Information

TRANSIT--A Software Tool for Himar1 TnSeq Analysis.

Publications

TRANSIT--A Software Tool for Himar1 TnSeq Analysis.

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