nusG paper

Repository containing the code and methods for Eckartt & Delbeau et al. 2024

Code

Below is a short legend describing the main files, followed by examples running the code:

vulnerability_modeling_tools.R

R code with functions useful for running the vulnerability analysis.

vulnerability_plotting_tools.R

R code with functions useful for plotting vulnerability results

gene_vulnerability_analysis.R

R code for running vulnerability analysis.

model_negbinom_2line_logistic_w_guide_lambda_betamin_zero.stan

Stan code for the vulnerability model.

gene_vulnerability_plots.R

R code for creating plots of vulnerability results in parallel.

process_reads.py

Python code to process fastq files with reads

subread.py

Tools useful to interact with the subread aligner. Must install via your OS package manager (e.g. sudo apt install subread)

counting_tools.py

Tools useful for counting the aligned reads

Data

As data files are too large to host on github, they are provided in the Dropbox link below:

https://www.dropbox.com/scl/fo/rbgm0vuhuq6j0t2x7zcvo/AJhbuM0qnkruHmw9ws5pg6Y?rlkey=h2giel9y3anlmoyvxmbz90tmb&st=l2w0qz5h&dl=0

Included in the repository is two, small example files that can be used to test running the code.

Example

Examples to show how to to run the code.

Analysis

To run the actual analysis with example parameters simply run:

Rscript gene_vulnerability_analysis.R --data <COUNTPATH> --strain <STRAIN> --label <LABEL> --output <RESULTSPATH> --cores <WORKERS>

where:

• COUNTPATH is a path to a dataframe containing counts for each of the sgRNAs at different passages.
• STRAIN is the strain being analyzed, which use used when distinguishing results.
• LABEL is general label to distinguish results
• RESULTSPATH is a path were you want results to be stored
• WORKERS is the number of workers to be used when running in parallel.

For example:

Rscript gene_vulnerability_analysis.R --data data/example_counts_H37Rv_biotinRR.txt --strain H37Rv --label biotinRR --output ./results/ --cores 20

This will create a local folder "results", which will have two sub-folders containing the data passed to the model ("./results/model_data/"), and the samples obtained from the model (./results/model_data/).

Plotting

Similarly, users can create plots of the results by running the following command:

Rscript gene_vulnerability_plots.R --data <DATAPATH> --strain <STRAIN> --exp <EXP> --date <DATE> --label <LABEL> --plot_dir <PLOTDIR> --dir <RESULTSPATH> --cores <WORKERS>

where:

• COUNTPATH is a path to a dataframe containing counts for each of the sgRNAs at different passages.
• STRAIN is the strain being analyzed, which use used when distinguishing results.
• EXP is a label for the experiment being analyzed, which use used when distinguishing results.
• DATE is a date for the experiment being analyzed, which use used when distinguishing results.
• LABEL is general label to distinguish results
• RESULTSPATH is a path where the results from the vulnerability analysis were stored.
• PLOTDIR is a path where you want plots to be outputed to.
• WORKERS is the number of workers to be used when running in parallel.

Rscript gene_vulnerability_plots.R --data data/example_counts_H37Rv_biotinRR.txt --strain H37Rv --label biotinRR --dir ./results/ --cores 20 --exp biotinRR --date 08_10_2023 --plot_dir ./results/plots/