Code for analysis of the p53 prime editing sensor screening dataset.

This repository is broken down into the following sections:

1. Jupyter Notebooks for recreating all of the figures that appear in the paper, included extended data figures.

   • These notebooks are located in the main directory and are labelled according to the figure # (i.e. figure_X.ipynb). All extended data figures are generated in a single notebook (extended_figures.ipynb).
   • All source data is referenced with relative paths to allow for complete reproducibility (you should be able to download the repository and directly run all the code in these notebooks).

2. Analysis scripts used to process raw fastq files (see folder: analysis_scripts):

   • split_fastq.py: python script for deconvolution of fastq file into separate files by sample.

   • sensor_extraction.py: python script for filtering reads, counting pegRNAs and recombination events, extracting sensor reads and placing them in separate fastq files for each pegRNA.

   • crispresso_analysis.py: python script for using crispresso2 to perform quantification of editing outcomes for each pegRNA at each time-point/condition/replicate.

   • crispresso_analysis_aggregation.py: as the name suggests, a python script for aggregating the results of crispresso runs (from the 28,000+ folders per sample) and formatting them into readable csv files.

   • analysis scripts endogenous sequencing: folder that contains scripts used for analysis of the endogenous sequencing results.

     • fastq_trimmer.py: trimming 150 nt reads down to 100 nt so that they can be joined.
     • join_read_analyzer.py: analyzing fastq-joined reads to filter and count sequencing outcomes, and determine their effect at the DNA and protein level.

   • Other files in this folder are referenced by some (or all scripts).

   • The .sh scripts with corresponding names are the scripts that were used to parallelize and run these jobs on the Luria Computing Cluster at MIT.

   • Note: In order to run crispresso2 (crispresso_analysis.py), you need to create a virtual environment that satisfies all requirements. To do so use the following:

     • crispresso_environment.yml: for generating a virtual environment that allows users to run crispresso via python commands (this script will break otherwise). This can be done with the following syntax:

     conda env create -n crispresso_env -f ./crispresso_environment.yml
     
     #And then to activate it:
     conda activate crispresso_env
     

3. Source data for generating figures

   • These are contained in the other folders, and are referenced (as relative paths) by the figure generation python notebooks.
   • Includes:
     • MAGeCK Analysis: Complete MAGeCK analysis runs for read normalization, and calculation of LFC and FDR value for pegRNAs.
     • crispresso & crispresso_replicates_merged: Aggregated crispresso analysis with quantification of sensor editing activity for each pegRNA.
     • To use and explore these datasets, see the figure generation python notebooks for examples that can be easily modified.

The jupyter notebooks for recreating the figures that appear in the paper should run in most python environments (no particularly exotic packages are used). For troubleshooting purposes, here is a list of packages and version numbers used:

• python==3.9.12
• matplotlib==3.6.2
• numpy==1.23.5
• pandas==1.5.2
• regex==2022.10.31
• scikit-learn==0.24.2
• scipy==1.10.0
• seaborn==0.12.2
• statannot==0.2.3