"Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities."

This repository contains the analysis source code used running MLDE for SpCas9 and SaCas9 in the paper "Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities" by Dawn Thean et al.

Prerequisite

Please go to the MLDE github page https://github.com/fhalab/MLDE to install and run MLDE according to user's instruction. Also R 4.0.2 is used for running the R code with packages: tidyverse, ggplot2, seqnir, readxl, and stringdist.

Overview

Here is the step by step break down of how to use the codes.

Step 1: use prepare_MLDE_fasta_file_for_encoding.R to generate the customized fasta file for MLDE encoding.

Step 2: use prepare_MLDE_fitness_input.R to generate customized fitness file for MLDE fitness prediction.

Step 3: Use Run_MLDE.sh with the scripts and parameter files in ./RUN MLDE to run MLDE.

Step 4: Use Organise_MLDE_output.sh to prepare the MLDE results for analyses in R.

Step 5: Use Evaluate_MLDE_Results.R to perform analyses and plot figures.