GRIT

Genome Recoding Informatics Toolbox (GRIT)

Correspondence to scasper[at]mit[dot]edu.

About

This repository accompanies the paper Multiplex base editing to convert TAG into TAA codons in the human genome

The GRIT software is a pythonic platform for working with human genome data, specifically GRCh38.p13. GRIT is meant for human genome recoding of TAA-->TAG using NG base editors, but it can be easily adapted to find a variety of statistics and sequences on chromosomes, genes, and coding DNA sequences.

Installation and Configuration

GRIT requires python 3 and has only a few main dependencies, so running it should be straightforward in an environment of your own. After cloning the repo and activating a virtual environment, install requirements.

pip install -r requirements.txt

Then data will need to be gathered. To fetch CRCh38.p13 data, the UNIX command line utilities for Entrez must be installed. Run the following commands.

cd data
curl -O https://www.ncbi.nlm.nih.gov/books/NBK179288/bin/install-edirect.sh
chmod +x install-edirect.sh
./install-edirect.sh
y
sudo apt install acedb-other
sudo apt install ncbi-entrez-direct
y
chmod +x get_data.sh
./get_data.sh

Using GRIT

GRIT is primarily meant to be a platform for writing your own functions and using them to gather bioinformatic and recoding data on the human genome. However, four functions are build in which demo GRIT features, count editing sites, find genes to recode, count editable sites, and getting genome-wide data on sites. To execute these, run the following.

mkdir results
python GRIT.py --function=demo --write_file=results/demo_out.txt
python GRIT.py --function=count_total_sites --write_file=results/count_total_sites_out.txt
python GRIT.py --function=count_editing_sites --write_file=results/count_editing_sites_out.txt
python GRIT.py --function=find_genes_to_recode --write_file=results/find_genes_to_recode_out.txt
python GRIT.py --function=get_all_site_data --write_file=results/get_all_site_data.txt

Working with GRIT

The code for the repository is both long and fairly well commented, so provided here is a high-level overview of its functionality. See the actual code as well as the outputs of the demo() function for more specific info.

GRIT consists of several major sections in two .py files. The main file, GRIT.py contains key imports, sample code, and functions to replicate results. The second file, GRIT_utill.py contains imports, initializations, helper functions, the Chromosome class, and the Gene class.

The helper functions at the top of GRIT_util.py are used for parsing data, reverse complementing a string, and ordering indices by centrality in a base editor activity window for base editor site selection.

A Chromosome object is instantiated by passing in a number or letter giving the chromosome as well as arguments for the data directory, what to consider the editing windows for A and C base editors, and what size range of primer products to find. When one is instantiated, the __init__() function calls each method inside the class to instantiate a variety of sequence, site, and gene attributes. Sites are found that can be directly edited with a C base editor or edited with a daisy chain of A and C editors. See the output of demo() to see how these are formatted. Gene objects are generally meant to be instantiated from within the Chromosome class. When one is instantiated, the __init__() function will sequences and recoding sites.

To extend GRIT to specific purposes beyond the ones in the functions provided, it is recommended that you write your own functions on top of the provided code to customize your analysis.