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).