Tutorial: Essentiality Analysis of the Entire Genome

To illustrate how TRANSIT works, we are going to go through a tutorial where we analyze datasets of H37Rv M. tuberculosis grown on glycerol and cholesterol.


Run TRANSIT

Navigate to the directory containing the TRANSIT files, and run TRANSIT:

python PATH/src/transit.py

Adding the annotation file

Before we can analyze datasets, we need to add an annotation file for the organism corresponding to the desired datasets. Click on the file dialog button, on the top of the TRANSIT window (see image below), and browse and select the appropriate annotation file. Note: Annotation files must be in ”.prot_table” format, described above.

_images/transit_tutorial_annotation.png

Adding the control datasets

We want to analyze datasets grown in glycerol to those grown in cholesterol. We are choosing the datasets grown in glycerol as the “Control” datasets. To add these, we click on the control sample file dialog (see image below), and select the desired datasets (one by one). In this example, we have two replicates:

_images/transit_tutorial_control_datasets.png

As we add the datasets they will appear in the table in the Control samples section. This table will provide the following statistics about the datasets that have been loaded so far: Total Number of Reads, Density, Mean Read Count and Maximum Count. These statistics can be used as general diagnostics of the datasets.

Visualizing read counts

TRANSIT allows us to visualize the read-counts of the datasets we have already loaded. To do this, we must select the desired datasets (“Control+Click”) and then click on “View -> Track View” in the menu bar at the top of the TRANSIT window. Only those selected datasets will be displayed:

_images/transit_dataset_track_view.png

This will open a window that allows that shows a visual representation of the read counts at the TA sites throughout the genome. The scale of the read counts can be set by changing the value of the “Max Read” textbox on the right. We can browse around the genome by clicking on the left and right arrowm, or search for a specific gene with the search text box.

This window also allows us to save a .png image of the canvas for future reference if desired (i.e. Save Img button).

Scatter plot

We can also view a scatter plot of read counts of two selected datasets. To achieve this we select two datasets (using “Control + Clicck”) and then clicking on “View -> Scatter Plot” in the menu bar at the top of the TRANSIT window.

_images/transit_dataset_scatter_graph.png

A new window will pop-up, show a scatter plot of both of the selected datasets. This window contains controls to zoom in and out (magnifying glass), allowing us to focus in on a specific area. This is particularly useful when large outliers may throw off the scale of the scatter plot.

Essentiality analysis with the HMM method

An alternative method for determining essentiality is the HMM method. This method differs from the Gumbel method in that is capable of assessing the essentiality of the entire genome, and is not limited to a gene-level analysis (See above for discussions of the pros and cons of each method). To run the HMM method we select it from the list of (Himar1) methods on the Analysis at the top. This automatically displays the available options for the HMM methid. Because the HMM method estimates parameters by examining the datasets, there is no need to set parameters for the model. One important option provided is how to deal with replicate datasets. Because the glycerol replicates had a mean read-count between 53-85, we decide to sum read-counts together by selecting “Sum” from the drop-down option.

_images/transit_hmm_options.png

Finally we click on the “Run HMM” button, and wait for the method to finish. Once the analysis finishes, two new files will be created and automatically added to the list of files in the Results Files section. One file contains the output of states for each TA site in the genome. The other file contains the analysis for each gene. We can display each of the files be selecting them (individually) and clicking on the “Display Table” button (one at a time). Like for the Gumbel method, a break down of the states is provided at the top of the table. In the case of glycerol, the HMM analysis classifies 16.3% of the genome as belonging to the “Essential” state, 5.4% belonging to the Growth-Defect state, 77.1% to the Non-Essential state, and 1.2% to the Growth Advantage state. This break down can be used as a diagnostic, to see if the results match our expectations. For example, in datasets with very low read-counts, or very low density, the percentage of Growth-Defect states may be higher (e.g. > 10% ), which could indicate a problem.

The HMM sites file contains the state assignments for all the TA sites in the genome. This file is particularly useful to browse for browsing the different types of regions in the genome. We can use this file to see how regions have different impacts on the growth-advantage (or disadvantage) of the organism. For example, the PDIM locus, which is required for virulance in vivo, results in a Growth-Advantage for the organism when disrupted. We can see this in the HMM Sites file by scrolling down to this region (Rv2930-Rv2939) and noticing the large read-counts at these sites, and the how they are labeled “GA”.