TRANSIT - A Software Tool for Himar1 TnSeq Analysis

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

Table of HMM Results for H37Rv grown in glycerol.

<p>Distribution of state calls for the glycerol datasets obtained by the HMM method. Essential states represent those regions which are mostly devoid of insertions. Non-Essential regions contain read-counts that are close to the mean read-count in the dataset. Growth-Defect regions and Growth-Advantage regions represent those regions which have significantly suppressed or increased read-counts.</p><p>Table of HMM Results for H37Rv grown in glycerol.</p

Table of results for comparative analysis between glycerol and cholesterol.

<p>Breakdown of the number of differentially essential genes identified by the resampling method, in each condition (glycerol and cholesterol). Differentially essential genes are those with an adjusted p-value <i>q</i> < 0.05.</p><p>Table of results for comparative analysis between glycerol and cholesterol.</p

Table of results obtained from resampling, comparing replicates grown in glycerol versus cholesterol.

<p>Table of results obtained from resampling, comparing replicates grown in glycerol versus cholesterol.</p

Picture of the main TRANSIT interface.

<p>Picture of the main TRANSIT interface.</p

Hidden Markov Model Diagram.

<p>The HMM is fully connected, allowing transitions between each of the states. Transition probabilities and parameters are estimated in such a way that the HMM will remain in the state which best represents the read-counts observed. (a) Essential regions (“ES”) are mostly devoid of insertions, (c) while non-essential regions (“NE”) contain read-counts around the global mean. (b) Growth-defect regions (“GD”), and (d) growth-advantage regions (“GA”) represent those areas with significantly suppressed or inflated read-counts.</p

Track View of read counts for datasets grown in glycerol and cholesterol.

<p>This region spans approximately 12 kb, and includes 5 genes. TA dinucleotides, which are candidate insertion sites, are indicated in the middle track. Vertical height of each bar reflects # of reads or Tn insertions at each TA site. Some sites with no insertions are probably missing from the library, while others may reflect essential regions. Note that GlpK lacks insertions in the glycerol condition, indicating that it is essential when grown on glycerol.</p

Resampling histogram for gene Rv0017c.

<p>Rv0017c has 23 TA sites, and the sum of the observed counts at the TA sites in this genes <i>in vitro</i> was 1,318 and <i>in vivo</i> was 399, therefore the observed difference in counts is -918. To determine the significance of this difference, 10,000 permutations of the counts at the TA sites among the datasets was generated and the observed differences plotted as a histogram showing that a difference as extreme as -918 almost never occurs by chance. The p-value is determined by the tail of this distribution to be 0.003 (30 out of 10,000).</p

TPP flowchart.

<p>Reads in .fasta, .fastq or fastq.gz format are taken in as input, and mapped to the genome to get read-counts at individual TA sites. A .wig formatted file is returned as output, containing the coordinates and the read-counts at all TA sites in the genome.</p

Table of Bayesian/Gumbel Results for H37Rv grown in glycerol.

<p>Breakdown of essentiality calls for the glycerol datasets obtained by the Bayesian/Gumbel method. Essential and Non-Essential genes are those genes whose posterior probability of essentiality exceeds the dynamic thresholds of essentiality. Uncertain genes are those who do not exceed these thresholds, and “Too Small” represents those genes who are too small for reliable analysis.</p><p>Table of Bayesian/Gumbel Results for H37Rv grown in glycerol.</p