Workflow of the system for CRISPR Outcome and Risk Evaluation (SCORE)

It is unclear how CRISPR editing outcomes vary across the genome and whether undesirable events such as structural variants (SVs) are predictable or preventable. Here, we describe a computational workflow to process whole-genome sequencing (WGS) data generated for multiplexed CRISPR/Cas genome editing experiments. The workflow characterizes and classifies diverse editing outcomes resulting from CRISPR and trains a predictive model based on a machine-learning-based framework termed SCORE (System for CRISPR Outcome and Risk Evaluation).
, , # MAGESTIC-SCORE

## A genome-wide annotation of difficult-to-edit regions based on MAGESTIC and SCORE

### Install conda environment

a. Install conda environment using CONDA (replace {myenv_name} with the name of environment to create):

```
git clone https://github.com/shli-embl/MAGESTIC-SCORE.git
cd MAGESTIC-SCORE/
conda env create -f envs/environment.yaml -p envs/{myenv_name}
```

Or using MAMBA (recommended):

```
git clone https://github.com/shli-embl/MAGESTIC-SCORE.git
cd MAGESTIC-SCORE/
mamba env create -f envs/environment.yaml -p envs/{myenv_name}
```

If you are using our \"MAGESTIC-SCORE-main.zip\" downloaded from DRYAD, unzip it first and then do:

```
cd MAGESTIC-SCORE/
mamba env create -f envs/environment.yaml -p envs/{myenv_name}
```

b. Then, activate the environment before running the snakemake pipelines:

```
conda activate envs/{myenv_name}
```

### Snakemake pipelines

### Pipeline 1: Single clonal whole-genome sequencing data analysis and editing outcome (pre-)callin...,